Author: Isabella Rossi Affiliation: Swiss International University (SIU) ORCID ID: 0009-0009-1291-9122 Submitted 18 April 2026; Revised 14 June 2026; Accepted 21 July 2026; Available online 08 August 2026; Version of Record 08 August 2026. Doi: https://doi.org/10.65326/u7y.SpecSDG10010 Volume 3, December 2026, (SpecSDG10010) Abstract With five years remaining before the 2030 deadline, the United Nations reports that 35% of assessable Sustainable Development Goal targets are on track or advancing moderately and that 18% have regressed below their 2015 baseline. Explanations for the shortfall divide into three diagnoses that are rarely tested against one another: an investment gap now estimated near four trillion dollars a year, a governance architecture that sets goals without binding anyone to them, and a measurement system that cannot see roughly half of what it is meant to track. This policy analysis appraises the progress record against the monitoring infrastructure that produces it, then examines the four constraints that the literature treats as parallel obstacles: commitment design, measurement and visibility, selective national implementation, and financing. The analysis finds the constraints are causally coupled rather than additive. Indicator selection removed the distributional and environmental content of several goals; the goals that lost measurability were subsequently deprioritised in national reviews; weak accountability then permitted the shortfall to be attributed to insufficient finance; and financing responses concentrated on the goals that remained visible. Shocks act on this cycle as an amplifier rather than an independent cause. The paper sets out three feedback loops that sustain the cycle, identifies the points at which each can be cut, and argues that a post-2030 architecture that fixes financing without fixing bindingness and measurement will reproduce the same shortfall. Keywords: 2030 agenda, sustainable development goals, policy coherence, sdg indicators, development finance, global governance. 1. Introduction The 2030 Agenda is closer to its deadline than to its adoption, and the arithmetic has stopped being encouraging. The United Nations (2025) reports that 35% of assessable targets are on track or making moderate progress, nearly half are advancing too slowly, and 18% now sit below where they stood in 2015. Xing et al. (2025), assessing 117 indicators across 167 countries, project a global SDG score near 63% by 2030 and calculate that meeting the targets would require roughly 4% annual improvement, several times the observed rate. Neither of those figures describes a programme that is behind schedule. They describe one that will not arrive. Explanations for the shortfall fall into three groups that seldom confront one another. The financing diagnosis is the most institutionally established: the United Nations Conference on Trade and Development (UNCTAD, 2023) puts the annual investment gap in developing countries at roughly four trillion dollars, up from two and a half trillion when the goals were adopted, and the Organisation for Economic Co-operation and Development (OECD, 2025) reviews the Addis Ababa Action Agenda across seven action areas as the principal financing response. A governance diagnosis locates the problem elsewhere. Biermann et al. (2017) described the goals from the outset as governance through goal-setting, inclusive in formation but non-binding and institutionally thin, and Biermann et al. (2022), meta-analysing more than 3,000 studies published between 2016 and April 2021, concluded that the political impact of the goals has been largely discursive. A third diagnosis concerns measurement: Dang and Serajuddin (2020) find data available for just over half of the indicators, and for only 19% of what tracking progress comprehensively across countries and over time would require. Each diagnosis is supported. None has displaced the others, and the reason is that they are usually studied apart. A financing analysis takes the goals and their monitoring as given and asks what resources would close the gap. A governance analysis takes the shortfall as given and asks what institutional design produced it. A measurement analysis asks what the numbers can support and stops there. The result is a catalogue of parallel obstacles and a sequence of single-constraint remedies, with the shortfall re-attributed each time to whichever constraint the analyst happens to study. I argue that these are not parallel obstacles. They are coupled, and the coupling runs in a direction that makes each one harder to fix than it looks in isolation. Indicator selection stripped distributional and environmental content out of several goals during the negotiation that produced them (Fukuda-Parr, 2019; Elder & Olsen, 2019). Goals that lost measurability lost visibility in national reporting, and national reviews then concentrated on the goals that were easiest to show progress on (Forestier & Kim, 2020). Weak bindingness meant that concentration carried no cost, which left the shortfall available for attribution to insufficient finance. Financing responses in turn flow toward what can be counted and reported. The cycle closes, and each rotation makes the next one more likely. Monsod et al. (2023) supply the sharpest reason to take this seriously. Estimating endogenous debt limits for climate-vulnerable developing countries, they find fiscal space in fact fairly ample for many of them, and argue that conflating standard debt-sustainability thresholds with genuine solvency limits traps otherwise solvent governments. If that assessment holds even partially, the financing gap is more than a resource constraint. It is in part a governance and analytical construct, produced by the same institutional weaknesses it is invoked to explain. The paper does three things. It appraises the reported progress record against the measurement infrastructure that generates it, so that what is known is separated from what is merely unreported. It analyses the four constraints in turn and specifies how each transmits to the next. And it sets out the resulting cycle as a diagnostic, identifying where the loops can be cut and what that implies for the years remaining and for whatever architecture follows 2030. 2. Progress at the Two-Thirds Mark and What the Record Can Support Any evaluation of the 2030 Agenda inherits the limits of the system that measures it, so the record and its basis have to be read together. Table 1 sets out the principal assessments alongside their coverage, metric and caveats. The headline assessments agree on direction while differing on magnitude. The United Nations (2025) gives the official stocktake: 35% on track or moderate, nearly half too slow, 18% in reverse. Xing et al. (2025) add a finding that the aggregate obscures, namely that progress depends on where a country starts. Among low-scoring indicators, 25% of countries advanced against 16% that regressed; among high-scoring indicators the figures were 16% and 10%. Improvement is happening, and it is happening unevenly enough that the global average describes almost no one. Projection work reached compatible conclusions well before the reversals of the 2020s. Moyer and Hedden (2020) modelled nine indicators across six human development goals under a middle-of-the-road scenario and found that 43% of 1,674 country-indicator pairs had already met their target values in 2015, with only 53% projected to do so by 2030. Sanitation, upper secondary completion and child underweight were furthest off track, and 28 countries were identified as most vulnerable. A five-year gain of ten percentage points on goals that were already partly achieved at adoption is not the trajectory the Agenda was designed around, and that projection assumed no pandemic. The measurement basis is weaker than the confidence of the headline numbers suggests. MacFeely (2020) examined the framework of 232 indicators adopted by the United Nations Statistical Commission in March 2017 and set out both the compilation burden it imposes and the unanticipated consequences of the measurement mechanism itself. Dang and Serajuddin (2020) quantified the shortfall: data exist for just over half the indicators, and for 19% of what comprehensive tracking would need, while ambiguous indicator wording lets different evaluation methods reach different verdicts on the same country. Beegle et al. (2025) show that absent data is only part of the problem. Countries reported on average 38% of the 50 SDG gender indicators in at least one year between 2016 and 2020, and sex-disaggregating the 32 indicators for which population estimates already exist would raise coverage to 47% without collecting anything new. Poorer countries reported no worse than high-income ones, which locates the failure in reporting practice rather than in statistical poverty alone. Composite indices partly compensate and partly conceal. Kynčlová et al. (2020) built an SDG-9 index for 128 economies over 2000 to 2016 from the official framework and used it to expose the specific dimensions on which countries lag, which is what a well-constructed index should do. The same construction also produces a single number that travels further than its components, and a single number is what a ministry reports. Two conclusions follow for the rest of the analysis. Assessments of the Agenda's progress are more reliable for the goals that were easiest to measure at the outset, which are broadly the socioeconomic ones, and least reliable for the distributional, environmental and institutional goals. And the “18% regressed” figure is a floor rather than an estimate, because regression on an unreported indicator does not register at all. The Independent Group of Scientists appointed by the Secretary-General (2023) concluded that incremental and fragmented change will not deliver the goals in the time remaining. The measurement record suggests the situation is somewhat worse than the incrementalism visible in the data. Table 1 Principal assessments of 2030 Agenda progress and the measurement basis on which they rest Assessment Coverage Metric Headline finding Measurement caveat United Nations (2025) All assessable targets, global Share of targets on track, slow, or regressing 35% on track or moderate; nearly half too slow; 18% below the 2015 baseline Assessable targets only; regression on unreported indicators does not register Xing et al. (2025) 117 indicators, 167 countries Country-level advance or regression by baseline score Low-scoring indicators: 25% advanced, 16% regressed; high-scoring: 16% and 10%; global score projected near 63% by 2030 Projection assumes continuation of observed rates Moyer and Hedden (2020) 9 indicators, 6 human development goals, 1,674 country-indicator pairs Share of pairs meeting target values under an SSP2 baseline 43% met targets in 2015, 53% projected by 2030; 28 countries most vulnerable Pre-pandemic scenario; human development goals only Kynčlová et al. (2020) 128 economies, 2000–2016 Composite index of inclusive and sustainable industrial development Industrialised economies lead; index isolates the dimensions on which countries lag Single goal (SDG 9); composite scores travel further than their components Dang and Serajuddin (2020) Full official indicator set and UN SDG database Data availability Data exist for just over half of indicators, and for 19% of what comprehensive tracking would require Ambiguous indicator wording lets methods differ on the same country Beegle et al. (2025) 50 SDG gender indicators, 2016–2020 National reporting coverage Countries reported 38% on average; disaggregating 32 existing indicators would raise coverage to 47% Reporting failure, not data absence, drives much of the gap MacFeely (2020) 232-indicator framework adopted March 2017 Compilation burden and measurement design Documents the statistical challenge and the unanticipated consequences of the measurement mechanism Analysis of the framework rather than of country performance Independent Group of Scientists (2023) Agenda-wide science assessment Qualitative appraisal of transformation Incremental and fragmented change will not deliver the goals by 2030 Assessment rather than measurement; no indicator-level estimates Note. Compiled by the author from the cited assessments. Percentages are reproduced as reported by each source and are not comparable across rows, since coverage, metric and reference period differ. The caveat column records the limitation each source states or that follows directly from its design. 3. Analytical Framework: Four Coupled Constraints The analysis that follows appraises four constraints against four criteria, and the criteria are stated here so that the judgements can be checked. Constraints were selected on the basis that each is independently documented in the peer-reviewed or intergovernmental literature as an obstacle to SDG achievement, and that each operates on the Agenda as a whole rather than on a single goal. That yields four: commitment design, meaning the bindingness and institutional backing of the goals; measurement and visibility, meaning what the indicator framework can see; selective implementation, meaning which goals national governments actually pursue; and financing and fiscal space, meaning the resources and the fiscal rules that govern their availability. Shocks are treated not as a fifth constraint but as an amplifier acting on all four, following the polycrisis account of Lawrence et al. (2024). Each constraint is appraised on four criteria: the strength of the evidence that it binds, drawn only from sources that state their data and method; the mechanism by which it blocks progress, stated explicitly rather than asserted; its transmission to the other three constraints, which is the criterion that distinguishes this analysis from a catalogue; and the tractability of relief, meaning whether relief requires new resources, new rules, or only new practice. Sources were identified through scholarly database searching combining ("Sustainable Development Goals" OR "2030 Agenda") with terms for progress, implementation, governance, indicators, financing, Voluntary National Reviews and policy coherence, supplemented by direct retrieval of intergovernmental assessments that indexed databases cover poorly. The window ran from 2015 to 2026. Inclusion required a peer-reviewed article or an official intergovernmental assessment, an explicit empirical or documentary basis, an Agenda-wide rather than single-sector claim, and publication in English. Every reference was verified against CrossRef metadata or the issuing organisation's own page before use, with authors, year, outlet and pagination confirmed; no claim rests on a source whose metadata could not be confirmed. The design is a policy analysis of documented institutions, monitoring arrangements and programmes rather than an empirical study, and it generates no new data. Its central proposition — that the four constraints form a reinforcing cycle — is a causal claim assembled from evidence collected for other purposes, and it is stated as an interpretation open to disconfirmation rather than as a measured result. Two safeguards apply. Evidence that cuts against the argument is engaged where it exists, most directly Monsod et al. (2023) against the financing consensus and Glass and Newig (2019) against the assumption that governance quality broadly explains achievement. And the strength of each link in the cycle is stated separately, so that a reader who rejects one link can see what survives. 4. Commitment Design: Goals Without Obligation The Agenda's institutional form was a deliberate choice, and its consequences are now measurable. Biermann et al. (2017) characterised it as governance through goal-setting: an inclusive formation process, non-binding goals, weak institutional arrangements, and extensive national leeway, with success dependent on states formalising commitments, strengthening global arrangements, translating goals into national contexts and integrating sectoral policy. Almost none of those conditions has been met at scale. The evidence on what the form produced is now substantial. Biermann et al. (2022) found across more than 3,000 studies that the political impact of the goals has been largely discursive, altering how actors talk about sustainable development while legislative change and reallocated resources remain rare. Hickmann et al. (2024), a scoping review by 32 governance scholars, identified five arenas of observable effect — global governance, national policy integration, subnational initiatives, private governance and education — and concluded that effects remain patchy and often symbolic. Two independent reviews of a large literature converging on the same verdict is about as strong as evidence about political impact becomes. Glass and Newig (2019) qualify this usefully. Testing which governance attributes explain SDG achievement across 41 high and upper-middle income countries, they found that of participation, policy coherence, reflexivity, adaptation and democratic institutions, only democratic institutions and participation contributed alongside economic power, education and geography. Policy coherence, the attribute that SDG governance discourse has emphasised most heavily, did not register. That finding should temper any expectation that coherence machinery alone will move outcomes, and it points attention toward the political conditions under which coherence is actually enforced. It is worth being precise about what the design achieved, because dismissing it would misstate the counterfactual. The inclusive formation process that Biermann et al. (2017) describe is what produced a universal agenda covering environment, inequality and institutions rather than a donor-defined poverty list, and a binding treaty covering that scope would not have been adopted. The trade the drafters made was breadth of ambition for enforceability. What the subsequent evidence shows is that the cost of that trade was underestimated: the goals inherited the political reach of a treaty and the compliance apparatus of a communiqué, and states have used the resulting leeway more than they have used the ambition. The transmission from this constraint to the others is straightforward. Where nothing is binding, reporting substitutes for compliance, and what is reported is what can be measured. Commitment design therefore hands the next constraint its authority: in a non-binding regime the indicator framework does not merely describe performance, it defines it. 5. Measurement and Visibility: What the Indicator Framework Removed The indicator framework is usually treated as a technical instrument that lags political ambition. The evidence suggests it also shaped that ambition, and did so during the negotiation rather than afterwards. Fukuda-Parr and McNeill (2019) document systematic slippage between the goals produced by the Open Working Group and the targets and indicators eventually selected, and argue that indicator selection, presented throughout as technical, is a political act that reorients what a goal means. Fukuda-Parr (2019) traces the mechanism for SDG 10. A goal that appears to set a strong norm on reducing inequality was rendered into targets and indicators built around shared prosperity rather than distributional measures such as the Gini coefficient or the Palma ratio, converting a commitment to reduce extreme inequality into a commitment to inclusion. Elder and Olsen (2019) find the parallel pattern on the environmental side: environmental concerns were incorporated broadly but many environment-related targets were housed inside non-environmental goals, planetary boundaries and beyond-GDP framings were rejected in favour of decoupling and resource efficiency, and environmental elements of many targets were omitted or weakened at the indicator stage. Bexell (2024) shows the same process operating after adoption rather than during it. Tracing what she terms indicator reporting trajectories through the case of SDG 17 multistakeholder partnerships, she finds that globally agreed commitments can disappear quietly across long follow-up cycles for want of data and measurability, producing policy shrinking in place of accountability. A commitment that cannot be reported is not formally abandoned. It simply stops appearing. MacFeely (2020) adds the administrative dimension that makes the pattern self-sustaining. Compiling 232 indicators is a burden that falls on national statistical offices whose capacity was built for a much smaller monitoring task, and the measurement mechanism itself produces consequences its designers did not anticipate. Offices under that load rationalise toward the indicators that are already collected for other purposes, which are the economic and social series that predate the Agenda. The environmental and institutional indicators, which would require new instruments and new administrative relationships, are the ones deferred. No decision to neglect them is ever taken; the neglect is the aggregate of reasonable triage decisions made under a workload nobody costed at adoption. Set beside the coverage failures documented earlier, this yields the constraint's mechanism. The indicator framework does not fail uniformly. It fails hardest on distribution, environment and institutions, which are the goals whose achievement would require redistributive or regulatory action, and it fails least on the socioeconomic indicators that improve with growth. The framework thereby encodes a bias toward the goals that are politically cheapest to pursue, and in a non-binding regime that bias becomes a de facto prioritisation. Whether the bias was intended is not something the sources establish, and the analysis here does not claim it. What they do establish is that it operates. 6. Selective Implementation and the Erosion of Indivisibility Indivisibility is the Agenda's foundational claim about itself, and national practice has not honoured it. Forestier and Kim (2020) content-analysed the Voluntary National Reviews of 19 countries across income levels and found SDG 1 and SDG 8 by far the most widely prioritised, describing the pattern as cherry-picking that defies the integrated character of the Agenda. Those two goals are also among the best measured and the most compatible with existing growth strategies, which is what the preceding section predicts. National studies show the selection is not arbitrary but domestically driven. Horn and Grugel (2018), interviewing national and Quito city policymakers in Ecuador, found engagement concentrated on inequality and inclusive-cities targets and refracted through the country's own Buen Vivir agenda, concluding that states do not adopt the SDGs as a template so much as filter them through decentralised structures and prior political preferences. Jönsson and Bexell (2021) find in Tanzania that government, civil society and parliamentary engagement drive localisation while unclear allocation of responsibility, weak coordination and shrinking democratic space obstruct it, and that localisation demands awareness well beyond elite circles. Koff and Häbel (2022), analysing normative coherence across world regions, conclude that regions matter as intermediaries between global and local levels but that normative coherence remains unachieved, because overriding political priorities and narrowly technical readings of coherence crowd out the normative content of the Agenda. The methods that would expose the cost of selectivity exist and are not being used. Allen et al. (2021) reviewed more than 150 publications to identify 22 science-based approaches recommended for national implementation, then examined 56 Voluntary National Reviews and found cross-fertilisation mainly in monitoring and evaluation, with methods for assessing interlinkages and national transformations largely absent. Pradhan et al. (2024) make the same point from the science side, arguing that the integrated character of the goals is being undermined by siloed implementation and identifying SDG interactions, modelling and decision-support tools as the science-policy foci that would operationalise integration. Sachs et al. (2019) offer the structural alternative, organising the Agenda into six transformations that fit existing government machinery while preserving interdependence. Lusseau and Mancini (2019) supply the reason selectivity carries a real cost rather than merely an aesthetic one: the contribution of individual goals to overall progress varies systematically with country income, and poverty and inequality reduction generate compound positive effects, so choosing the wrong subset forgoes gains elsewhere. Selectivity transmits to financing directly. A government that has narrowed its effective agenda to the goals it can show progress on will cost that agenda, not the full one, and the difference between the two appears in aggregate accounts as an unmet financing need. 7. Financing and Fiscal Space: A Gap That Is Partly Constructed The financing diagnosis is the one with institutional weight behind it, and the numbers are large. UNCTAD (2023) reports the annual SDG investment gap in developing countries widening from 2.5 trillion dollars in 2015 to roughly 4 trillion, with the deepest shortfalls in energy, water and transport infrastructure. Gaspar et al. (2019) estimate the additional annual public spending on health, education, roads, electricity and water and sanitation that countries would need by 2030 and analyse the financing strategies open to low-income and emerging economies. OECD (2025) reviews progress across the seven action areas of the Addis Ababa Action Agenda as an input to the fourth financing-for-development conference. The gap is real. What it means is contested. Monsod et al. (2023) apply an endogenous debt-limit measure to climate-vulnerable developing countries and find fiscal space fairly ample for many of them, arguing that treating standard debt-sustainability thresholds as limits to fiscal space leaves solvent governments unable to borrow for adaptation and development. On that reading, part of the gap is produced by the analytical conventions used to assess it, which is a governance failure wearing the costume of a resource shortage. The composition of existing spending points the same way. Naidoo and Fisher (2020) note roughly 4.7 trillion dollars in annual fossil-fuel subsidies and argue for redirecting that flow along with part of military spending, alongside prioritising goal combinations that deliver several objectives at once. Barbier and Burgess (2020) reach a compatible conclusion from the post-pandemic fiscal position, arguing that with less financing available the priority should be low-cost measures that advance several goals simultaneously, including redirecting fossil fuel and irrigation subsidies toward energy and water poverty and a tropical carbon tax to fund natural climate solutions. Both arguments assume something the preceding sections have shown to be weak: an institutional capacity to reallocate against entrenched interests, and an ability to identify which goal combinations pay off jointly. The first requires bindingness. The second requires the interlinkage methods that Allen et al. (2021) found missing from national reviews. The institutional response has been organised around resource mobilisation rather than around the conditions under which resources translate into outcomes. The seven action areas reviewed by OECD (2025) — domestic public resources, private business and finance, development co-operation, trade, science and technology, debt, and systemic issues — describe an architecture built to increase and channel flows. That is the correct architecture for a shortage of money. It contains no instrument that binds a recipient government to the goals it deprioritised, and no mechanism that improves the reporting on which allocation decisions depend. A financing conference is therefore capable of closing a financing gap and incapable of closing the gap between what states pledged and what they pursue, which are not the same shortfall even though the same number is used to describe both. The financing constraint is therefore the least independent of the four. Its magnitude depends on which agenda is being costed, which depends on selectivity; its distribution depends on debt rules that are analytical conventions rather than physical limits; and its deployment depends on measurement, because money moves toward what can be reported. 8. Shocks as Amplifier Treating the pandemic and its successors as the explanation for the shortfall would misread the sequence, since the projection literature was already pessimistic before 2020. What shocks do is convert slow constraints into visible reversals. Naidoo and Fisher (2020) estimated early in the pandemic that roughly two-thirds of the 169 targets were under threat. Shulla et al. (2021), working from moderated expert focus groups, identified a pandemic-driven pattern of interconnection centred on health, education, decent work, consumption and climate action, with spillovers threatening gender equality, infrastructure, inequality reduction, sustainable cities and partnerships. Lawrence et al. (2024) give the general form, defining global crisis as fast-moving triggers combining with slow-moving stresses to push a global system out of equilibrium, and identifying common stresses, domino effects and inter-systemic feedbacks as the pathways of entanglement. Read against the four constraints, the amplification is specific rather than diffuse. Shocks raise financing needs while contracting fiscal space, which widens the measured gap without any change in the underlying agenda. They push governments toward emergency prioritisation, which deepens selectivity. They interrupt data collection, which thins the evidence base exactly when reallocation decisions are being made. A system with binding obligations, complete measurement and integrated planning would absorb such a shock. The Agenda as constituted transmits it. 9. The Barrier Reinforcement Cycle The four constraints and the shock amplifier assemble into the cycle shown in Figure 1, and the value of stating it as a cycle rather than a list is that it explains why single-constraint remedies have underperformed. Table 2 summarises each constraint with its evidence, blocking mechanism and the point at which it can be relieved. Figure 1 The Barrier Reinforcement Cycle: four coupled constraints, the three loops that sustain them, and shocks as amplifier Note. The four constraints and the evidence supporting each are set out in Table 2. The three loops are stated at different evidential strengths in the text: the visibility loop rests on the firmest evidence, the attribution loop is the most interpretive. Three feedback loops carry the cycle. The visibility loop runs from indicator design to national prioritisation and back: goals whose content was weakened at the indicator stage attract less national reporting, thin reporting further weakens the case for strengthening those indicators, and the goals recede from view (Fukuda-Parr, 2019; Bexell, 2024; Forestier & Kim, 2020). The attribution loop runs from weak bindingness to financing: because no actor is accountable for a missed target, the shortfall is available for attribution to insufficient finance, which is the explanation that assigns responsibility to no one in particular and requires no institutional change (Biermann et al., 2022; Hickmann et al., 2024; UNCTAD, 2023). The allocation loop runs from financing back to visibility: resources flow toward outcomes that can be counted and reported, which improves performance and data on the already-visible goals and widens the gap against the rest (Beegle et al., 2025; Dang & Serajuddin, 2020). Each loop is a claim of different strength, and they should not be accepted together. The visibility loop rests on the firmest evidence, since indicator weakening, reporting gaps and national prioritisation patterns are each separately documented and their alignment is close. The attribution loop is the most interpretive: the sources establish that impact has been discursive and that the financing framing dominates, but no study demonstrates that the second follows from the first. The allocation loop sits between the two, supported by the concentration of both data coverage and national attention on the same subset of goals but not by direct evidence on where money went. The cycle also identifies where it can be cut, which is the practical payoff. Cutting at measurement is the cheapest available intervention and the most immediately achievable: Beegle et al. (2025) show that sex-disaggregating existing data would lift gender indicator coverage from 38% to 47% without new collection, and comparable reporting gains are likely elsewhere. Cutting at selectivity requires no new resources either, only the use of interlinkage methods that already exist and that Allen et al. (2021) found absent from national reviews. Cutting at commitment design would do the most and is the least likely, since it would require what Biermann et al. (2017) called for at the outset and states have consistently declined. Cutting at financing alone, which is where institutional effort has concentrated, is the intervention the cycle predicts will underdeliver, because money entering a system with weak bindingness, incomplete measurement and selective implementation will follow the same channels that produced the imbalance. Table 2 The four constraints: evidence, blocking mechanism, and where the cycle can be cut Constraint Principal evidence How it blocks progress Where it can be cut Commitment design Biermann et al. (2017, 2022); Hickmann et al. (2024); Glass and Newig (2019) Non-binding goals with weak institutional backing make reporting a substitute for compliance, so missed targets carry no cost Formalised commitments and enforcement; highest effect, lowest political feasibility Measurement and visibility Fukuda-Parr (2019); Fukuda-Parr and McNeill (2019); Elder and Olsen (2019); MacFeely (2020); Dang and Serajuddin (2020); Beegle et al. (2025); Bexell (2024) Distributional, environmental and institutional content was weakened at indicator selection and is under-reported afterwards, so the hardest goals become invisible Report existing data and disaggregate what is already collected; cheapest available intervention Selective implementation Forestier and Kim (2020); Horn and Grugel (2018); Jönsson and Bexell (2021); Koff and Häbel (2022); Allen et al. (2021); Pradhan et al. (2024) National reviews concentrate on the best-measured, growth-compatible goals, so indivisibility erodes without any formal decision Require reviews to state deprioritised goals; apply existing interlinkage methods Financing and fiscal space UNCTAD (2023); Gaspar et al. (2019); OECD (2025); Monsod et al. (2023); Naidoo and Fisher (2020); Barbier and Burgess (2020) Resource shortfalls are real, but the estimated gap depends on which agenda is costed and on debt conventions that function as rules rather than limits Revise solvency assessment and redirect existing subsidy flows; ineffective if pursued alone Shocks (amplifier) Naidoo and Fisher (2020); Shulla et al. (2021); Lawrence et al. (2024) Raise financing needs while contracting fiscal space, deepen emergency prioritisation, and interrupt data collection Not a target for intervention; the case for relieving the other four before the next shock Note. Compiled by the author. Shocks are listed for completeness as an amplifier acting on the four constraints rather than as a fifth constraint. The final column states the intervention implied by the analysis, not a costed policy proposal. 10. Discussion What the cycle implies for the remaining period Five years is too short to redesign the Agenda's institutional form, which makes the low-cost cuts the ones worth pressing. Reporting existing data that is currently unreported, sex-disaggregating indicators where the population estimates already exist, and requiring Voluntary National Reviews to state which goals were deprioritised and why would together change what is visible without changing what is owed. Visibility is not accountability, but in a non-binding regime it is the closest available substitute, and the evidence in this analysis suggests it is the input on which the other constraints depend. The finding also reframes the financing conversation rather than dismissing it. The investment gap is genuine and the deficits in energy, water and transport infrastructure are physical. The claim here is narrower: that the gap's magnitude is partly endogenous to which agenda is costed and to which debt conventions are applied, and that closing it without addressing the other three constraints will fund the goals that were already advancing. Monsod et al. (2023) show that at least one component of the perceived shortage is a rule rather than a resource, and rules can be revised on a shorter timescale than tax bases can be grown. Implications for a post-2030 architecture Design work on what follows 2030 is underway, and the cycle carries three implications for it. A successor framework that retains non-binding goals while improving indicators will strengthen the measurement of an unenforced commitment. One that binds without fixing measurement will bind states to the goals that were already easiest to show. One that fixes both while leaving the unit of coordination at 17 separately reported goals will reproduce selectivity, because selection pressure operates on whatever the reporting unit is. The six transformations of Sachs et al. (2019) and the interaction-based approach of Pradhan et al. (2024) both suggest organising commitment and reporting around delivery systems rather than goal numbers, which would make partial implementation visible as an incomplete transformation rather than as progress on a subset. Legitimacy belongs in this design conversation too. Sénit et al. (2017) assessed three civil society consultations in which the United Nations consulted nearly ten million people and found their democratising potential limited by sociodemographic bias and under-representation of developing-country actors, with clear objectives, adequate participant time and formal linkage to negotiations mattering more than resources or open access. Sénit (2020) shows that civil society influence on outcomes concentrated in informal and exclusive venues early in the process, where the groups with access were least representative. A commitment to leave no one behind that was drafted through venues structured this way starts with a representational deficit that no indicator can correct later. What would falsify the cycle An interpretive construct earns its place by being refusable, so the disconfirming evidence should be named. The cycle predicts that national attention tracks measurability; a finding that Voluntary National Reviews give sustained priority to goals with poor indicator coverage would break the visibility loop. It predicts that financing follows reportability; disbursement data concentrated on poorly measured goals would break the allocation loop. It predicts that attributions of failure shift toward resources as accountability weakens; a documentary record in which institutional explanations hold steady or grow would break the attribution loop. The cycle also predicts that improving reporting raises subsequent attention, so a country that closed a coverage gap without any change in national priority would count against it. None of these tests has been run, and the argument here should be held only as firmly as that admission allows. Limitations This is a policy analysis of documented institutions and published assessments, not an empirical study, and its central construct is an interpretation. The cycle is assembled from evidence gathered for other purposes, and while each constraint is separately documented, the causal links between them are inferred from alignment rather than demonstrated by design. The three loops are stated at different strengths for that reason, and the attribution loop in particular could be wrong without the rest of the analysis failing. The evidence base is uneven in a way that matters: governance and indicator scholarship is concentrated in a small number of European and North American research groups, national implementation evidence rests on a handful of country cases, and English-language publication bias excludes work that would likely complicate the selectivity findings. The progress figures reproduced here inherit the coverage limits the analysis itself identifies, which means the assessment of how far off track the Agenda is cannot be more precise than the data that would establish it. No quantification of the cycle is attempted, and none of the proposed interventions is costed. Future research Three lines would test what this analysis infers. The attribution loop is checkable: coding how national and multilateral documents explain missed targets over time, and testing whether financing explanations displace institutional ones as bindingness weakens, would either support the loop or break it. The allocation loop needs disbursement data mapped against indicator coverage, to establish whether resources do concentrate on well-measured goals. And the measurement cut proposed here has a natural experiment available, since countries differ in how much existing data they report; comparing subsequent national attention to goals whose coverage improved against those where it did not would show whether visibility moves priority in the direction the cycle assumes. Beyond testing, the more useful work is design: specifying what a binding, transformation-organised, fully reported successor framework would look like in enough operational detail that states are refusing something concrete rather than an abstraction. 11. Conclusion The 2030 Agenda will miss most of its targets, and the reasons are better understood separately than together. Money is short, the goals bind no one, and the measurement system cannot see roughly half of what it was built to track. Treating these as three problems has produced three sets of remedies, of which only the financing one has attracted sustained institutional effort, and it is the one the evidence suggests is least independent of the others. The account offered here is that the constraints hold each other in place. Indicators that were weakened during negotiation made certain goals hard to see; invisibility permitted national selectivity; selectivity went uncorrected because nothing binds; and the resulting shortfall was attributed to a financing gap whose magnitude depends on which agenda is costed and which debt conventions are applied. Shocks did not create this arrangement, they revealed how little it absorbs. What follows from the analysis is a narrow practical claim and a broader design one. Reporting the data that already exists is the cheapest available intervention and the one on which the others depend. And any successor to the 2030 Agenda that improves financing while leaving bindingness and measurement as they are will be a better-funded version of the same shortfall. Declarations Funding. This research received no external funding. Conflicts of interest. The author declares no conflict of interest. Ethics statement. This study analyses published literature, official statistics and intergovernmental policy documents. It involved no human participants, no animal subjects and no primary data collection, and therefore required no ethical approval. Data availability. No new data were generated. All sources analysed are published works listed in the reference list and identified by digital object identifier or public repository address. References Allen, C., Metternicht, G., & Wiedmann, T. (2021). Priorities for science to support national implementation of the sustainable development goals: A review of progress and gaps. Sustainable Development, 29(4), 635-652. https://doi.org/10.1002/sd.2164 Barbier, E. B., & Burgess, J. C. (2020). Sustainability and development after COVID-19. World Development, 135, Article 105082. https://doi.org/10.1016/j.worlddev.2020.105082 Beegle, K., Serajuddin, U., Stacy, B., & Wadhwa, D. (2025). Missing SDG gender indicators. World Development, 196, Article 106957. https://doi.org/10.1016/j.worlddev.2025.106957 Bexell, M. (2024). Indicator accountability or policy shrinking? Multistakeholder partnerships in reviews of the sustainable development goals. Global Policy, 15(2), 276-287. https://doi.org/10.1111/1758-5899.13348 Biermann, F., Hickmann, T., Sénit, C.-A., Beisheim, M., Bernstein, S., Chasek, P., Grob, L., Kim, R. E., Kotzé, L. J., Nilsson, M., Ordóñez Llanos, A., Okereke, C., Pradhan, P., Raven, R., Sun, Y., Vijge, M. J., van Vuuren, D., & Wicke, B. (2022). Scientific evidence on the political impact of the Sustainable Development Goals. Nature Sustainability, 5(9), 795-800. https://doi.org/10.1038/s41893-022-00909-5 Biermann, F., Kanie, N., & Kim, R. E. (2017). Global governance by goal-setting: The novel approach of the UN Sustainable Development Goals. Current Opinion in Environmental Sustainability, 26-27, 26-31. https://doi.org/10.1016/j.cosust.2017.01.010 Dang, H.-A. H., & Serajuddin, U. (2020). Tracking the sustainable development goals: Emerging measurement challenges and further reflections. World Development, 127, Article 104570. https://doi.org/10.1016/j.worlddev.2019.05.024 Elder, M., & Olsen, S. H. (2019). The design of environmental priorities in the SDGs. Global Policy, 10(S1), 70-82. https://doi.org/10.1111/1758-5899.12596 Forestier, O., & Kim, R. E. (2020). Cherry-picking the Sustainable Development Goals: Goal prioritization by national governments and implications for global governance. Sustainable Development, 28(5), 1269-1278. https://doi.org/10.1002/sd.2082 Fukuda-Parr, S. (2019). Keeping out extreme inequality from the SDG agenda - The politics of indicators. Global Policy, 10(S1), 61-69. https://doi.org/10.1111/1758-5899.12602 Fukuda-Parr, S., & McNeill, D. (2019). Knowledge and politics in setting and measuring the SDGs: Introduction to special issue. Global Policy, 10(S1), 5-15. https://doi.org/10.1111/1758-5899.12604 Gaspar, V., Garcia-Escribano, M., Prady, D., & Soto, M. (2019). Fiscal policy and development: Human, social, and physical investments for the SDGs (Staff Discussion Note No. 2019/003). International Monetary Fund. https://www.imf.org/en/publications/staff-discussion-notes/issues/2019/01/18/fiscal-policy-and-development-human-social-and-physical-investments-for-the-sdgs-46444 Glass, L.-M., & Newig, J. (2019). Governance for achieving the Sustainable Development Goals: How important are participation, policy coherence, reflexivity, adaptation and democratic institutions? Earth System Governance, 2, Article 100031. https://doi.org/10.1016/j.esg.2019.100031 Hickmann, T., Biermann, F., Sénit, C.-A., Sun, Y., Bexell, M., Bolton, M., Bornemann, B., Censoro, J., Charles, A., Coy, D., Dahlmann, F., Elder, M., Fritzsche, F., Gehre Galvão, T., Grainger-Brown, J., Inoue, C., Jönsson, K., Koloffon Rosas, M., Krellenberg, K., ... Weiland, S. (2024). Scoping article: Research frontiers on the governance of the Sustainable Development Goals. Global Sustainability, 7, Article e7. https://doi.org/10.1017/sus.2024.4 Horn, P., & Grugel, J. (2018). The SDGs in middle-income countries: Setting or serving domestic development agendas? Evidence from Ecuador. World Development, 109, 73-84. https://doi.org/10.1016/j.worlddev.2018.04.005 Independent Group of Scientists appointed by the Secretary-General. (2023). Global Sustainable Development Report 2023: Times of crisis, times of change - Science for accelerating transformations to sustainable development. United Nations. https://sdgs.un.org/gsdr/gsdr2023 Jönsson, K., & Bexell, M. (2021). Localizing the Sustainable Development Goals: The case of Tanzania. Development Policy Review, 39(2), 181-196. https://doi.org/10.1111/dpr.12497 Koff, H., & Häbel, S. (2022). Normative coherence for development: What relevance for responsive regionalism? Development Policy Review, 40(S1), Article e12586. https://doi.org/10.1111/dpr.12586 Kynčlová, P., Upadhyaya, S., & Nice, T. (2020). Composite index as a measure on achieving Sustainable Development Goal 9 (SDG-9) industry-related targets: The SDG-9 index. Applied Energy, 265, Article 114755. https://doi.org/10.1016/j.apenergy.2020.114755 Lawrence, M., Homer-Dixon, T., Janzwood, S., Rockström, J., Renn, O., & Donges, J. F. (2024). Global polycrisis: The causal mechanisms of crisis entanglement. Global Sustainability, 7, Article e6. https://doi.org/10.1017/sus.2024.1 Lusseau, D., & Mancini, F. (2019). Income-based variation in Sustainable Development Goal interaction networks. Nature Sustainability, 2(3), 242-247. https://doi.org/10.1038/s41893-019-0231-4 MacFeely, S. (2020). Measuring the Sustainable Development Goal indicators: An unprecedented statistical challenge. Journal of Official Statistics, 36(2), 361-378. https://doi.org/10.2478/jos-2020-0019 Monsod, T. M. C., Majadillas, M. A., & Gochoco-Bautista, M. S. (2023). Unlocking the flow of finance for climate adaptation: Estimates of 'fiscal space' in climate-vulnerable developing countries. Climate Policy, 23(6), 735-746. https://doi.org/10.1080/14693062.2023.2224281 Moyer, J. D., & Hedden, S. (2020). Are we on the right path to achieve the sustainable development goals? World Development, 127, Article 104749. https://doi.org/10.1016/j.worlddev.2019.104749 Naidoo, R., & Fisher, B. (2020). Reset Sustainable Development Goals for a pandemic world. Nature, 583(7815), 198-201. https://doi.org/10.1038/d41586-020-01999-x Organisation for Economic Co-operation and Development. (2025). Global outlook on financing for sustainable development 2025: Towards a more resilient and inclusive architecture. OECD Publishing. https://doi.org/10.1787/753d5368-en Pradhan, P., Weitz, N., Daioglou, V., Abrahão, G. M., Allen, C., Ambrósio, G., Arp, R., Asif, F., Bennich, T., Benton, T. G., Biermann, F., Carlsen, H., Hubacek, K., Leininger, J., Lotze-Campen, H., Pedercini, M., Prell, C., van Vuuren, D., Warchold, A., ... Zimm, C. (2024). Three foci at the science-policy interface for systemic Sustainable Development Goal acceleration. Nature Communications, 15(1), Article 8600. https://doi.org/10.1038/s41467-024-52926-x Sachs, J. D., Schmidt-Traub, G., Mazzucato, M., Messner, D., Nakicenovic, N., & Rockström, J. (2019). Six transformations to achieve the Sustainable Development Goals. Nature Sustainability, 2(9), 805-814. https://doi.org/10.1038/s41893-019-0352-9 Sénit, C.-A. (2020). Leaving no one behind? The influence of civil society participation on the Sustainable Development Goals. Environment and Planning C: Politics and Space, 38(4), 693-712. https://doi.org/10.1177/2399654419884330 Sénit, C.-A., Biermann, F., & Kalfagianni, A. (2017). The representativeness of global deliberation: A critical assessment of civil society consultations for sustainable development. Global Policy, 8(1), 62-72. https://doi.org/10.1111/1758-5899.12371 Shulla, K., Voigt, B.-F., Cibian, S., Scandone, G., Martinez, E., Nelkovski, F., & Salehi, P. (2021). Effects of COVID-19 on the Sustainable Development Goals (SDGs). Discover Sustainability, 2(1), Article 15. https://doi.org/10.1007/s43621-021-00026-x United Nations. (2025). The Sustainable Development Goals report 2025. United Nations Department of Economic and Social Affairs. https://unstats.un.org/sdgs/report/2025/ United Nations Conference on Trade and Development. (2023). World Investment Report 2023: Investing in sustainable energy for all. United Nations. https://unctad.org/publication/world-investment-report-2023 Xing, L., Lu, Y., Wang, J., Chen, W., Liu, L., Pradhan, P., Bryan, B. A., Moallemi, E. A., Gao, L., Schaubroeck, T., Carrasco, L. R., Banik, D., Xu, Z., Li, Y., Zhang, X., Ding, Z., Liu, J., Zeng, S., & Wu, J. (2025). Country-specific progress toward the Sustainable Development Goals: Past, present, and prospects. Proceedings of the National Academy of Sciences, 122(52), Article e2524299122. https://doi.org/10.1073/pnas.2524299122 Hashtags: #SDGs #2030Agenda #GlobalGoals #SDG1 #SDG8 #SDG9 #SDG10 #SDG17 #SustainableDevelopmentGoals #PolicyCoherence #SDGIndicators #DevelopmentFinance #GlobalGovernance #U7YJournal #U7Y #AcademicResearch

Author: Oliver Smith Affiliation: Swiss International University (SIU) ORCID ID: 0009-0001-8637-7727 Submitted 07 April 2026; Revised 09 June 2026; Accepted 16 July 2026; Available online 08 August 2026; Version of Record 08 August 2026. Doi: https://doi.org/10.65326/u7y.SpecSDG10011 Volume 3, December 2026, (SpecSDG10011) Abstract Quantitative assessments of how the 17 Sustainable Development Goals interact have produced two incompatible verdicts from broadly the same official indicator data. One body of work reports that positive associations between goal pairs outnumber negative ones, with the synergy share rising above nine-tenths of total influence once indirect paths are admitted. Another reports that no country meets basic human needs within sustainable resource use, that gains on social goals travel with larger environmental footprints, and that no observed national trajectory has advanced all 17 goals together. This review reconciles the two positions by treating the measured synergy-to-trade-off ratio not as a property of the goal system but as a joint product of five specification choices: the accounting boundary, the analytical object, the indicator set and estimator, the order of interaction admitted, and the scale of observation. Coding 34 sources against these axes shows the disagreement is patterned rather than random: territorial, cross-sectional, level-based, indirect-path, globally averaged designs generate synergy optimism, while consumption-based, change-based, trajectory-based and subnational designs recover structural conflict. The paper develops a Conditional Interaction Assessment framework, states five testable propositions, and proposes a reporting specification under which an interaction estimate becomes interpretable. The implication for policy is that a single global synergy statistic carries little transfer value to any particular jurisdiction, and that interaction evidence entering national planning should be specified before it is used. Keywords: sustainable development goals, goal interactions, synergies, trade-offs, policy coherence, integrated assessment 1. Introduction Two peer-reviewed assessments of the same goal system, published two years apart and drawing on official indicator data, reach conclusions that cannot both guide policy. Xiao et al. (2022) report that once indirect interlinkages are admitted into the network, synergistic effects account for as much as 98.33% of total influence across the Sustainable Development Goals. Carlsen et al. (2024) test whether any country's observed historical path is consistent with progress on all 17 goals and find none. A planner reading the first result would look for entry points that pull the whole system upward. A planner reading the second would prepare to arbitrate losses. The gap between those two postures is the difference between an agenda that coheres and one that does not. This is not a marginal disagreement about magnitude. It is a disagreement about the sign of the system. The literature has absorbed it largely by treating the two clusters as separate conversations: statistical interaction studies cite one another, and biophysical-limits studies cite one another, with limited traffic across the boundary. Where the divergence has been noticed, it has been attributed to isolated technical causes. Bennich et al. (2020) reviewed 70 studies and found no consensus on conceptual framing or assessment method. Warchold et al. (2022) built a unified database from United Nations, World Bank and Bertelsmann Stiftung indicator sets and found that interaction patterns derived from them are more different than similar, with environmental goals diverging most. Ospina-Forero et al. (2022) showed that alternative network estimators applied to the same development indicators return materially different structures. Song and Jang (2023) questioned whether the 17-goal partition is even the right analytical unit, since the most tightly connected targets cut across goal boundaries. Each of these findings is a warning about a single step in the analytical chain. None of them explains why the aggregate verdict flips, and none tells a reader what an interaction claim would have to state before it could be believed. That is the gap this paper addresses. The problem matters because interaction evidence is no longer confined to journals: it enters Voluntary National Reviews, sequencing arguments, and the design debate over what follows 2030 (Allen et al., 2021; Independent Group of Scientists appointed by the Secretary-General, 2023). A number chosen from the optimistic end of a specification-dependent range can license a national strategy that assumes conflicts away. I argue that the synergy-to-trade-off ratio reported in any given study is not an estimate of a fixed quantity. It is a joint product of five analytical choices, and the two literatures are not contradicting one another so much as answering different questions under the same name. The paper makes three contributions. It reconciles the opposing clusters by showing that their disagreement is patterned along identifiable specification axes rather than distributed randomly. It develops a Conditional Interaction Assessment framework, expressed as five propositions that can be tested by re-running existing data under paired specifications. And it proposes a reporting specification under which an interaction estimate becomes interpretable by someone who did not produce it. The review proceeds by first setting the conflicting evidence side by side, then working through the specification axes that separate the clusters: accounting boundary, analytical object, indicator and estimator choice, and interaction order together with scale. The framework and its propositions follow, before the discussion turns to what this means for how goal interactions are reported and used. 2. Review Approach Three questions organise the review. Under what analytical conditions does the empirical literature report that synergies dominate, and under what conditions does it report structural conflict? Which specification choices account for the divergence between those conditions? And what would an interaction claim need to state for a reader to interpret it? The design is an integrative review rather than a systematic review or meta-analysis, and the choice follows from the questions. A meta-analysis pools comparable effect estimates; the corpus here contains cross-sectional correlation studies, network estimation, cross-impact scoring, integrated assessment modelling, consumption-based environmental accounting and qualitative archetype analysis, which share no common effect metric. A systematic review maps a field's coverage, and Bennich et al. (2020) have already produced that map with a coding scheme and reading guide. Repeating the count would add little. The question here is why methods that all claim to measure SDG interactions disagree about the sign of the system, which is a conceptual question answered by structured comparison rather than by pooling. Sources were identified through searches of scholarly databases indexing Scopus, Semantic Scholar, PubMed and arXiv records, a semantic full-text scholarly search service covering major publisher corpora, and targeted searching of publisher and intergovernmental repositories for material that indexed databases cover poorly. Search strings combined ("Sustainable Development Goals" OR "SDG") with (synerg OR "trade-off" OR interaction OR interlinkage OR interdependen) and with (network OR correlation OR model OR "planetary boundaries" OR footprint OR governance). The window ran from 2015, the year the goals were adopted, to 2026, with earlier work retained only where later studies build directly on it. Studies entered the review when they met four criteria: peer-reviewed article, book chapter, or intergovernmental scientific assessment; an analytical claim spanning multiple goals rather than a single pair; an explicit statement of data source and analytical procedure; and publication in English. Single-sector applications with no cross-goal claim were excluded, as were commentaries offering no evidence and any work flagged as retracted. Applying these criteria yielded 34 sources: 30 journal articles, two intergovernmental assessments, and two methodological reviews that also function as evidence about the field's heterogeneity. Every reference was verified against CrossRef metadata records or, for material without a digital object identifier, against the publisher's own page, with authors, year, journal, volume and pagination confirmed before the source entered the analysis. No claim in this paper rests on a source whose metadata could not be confirmed at that level. Each retained study was then coded on five dimensions drawn inductively from an initial reading: the accounting boundary of its indicators, whether it analysed levels or changes, its indicator set and estimator, whether it admitted indirect paths, and the scale at which it observed. Its headline verdict on the balance of synergies and trade-offs was recorded alongside. Table 1 presents the coded comparison for the studies that make system-level claims. The framework in the later sections was derived abductively from that table by asking which specification differences separate the synergy-dominant cluster from the trade-off-dominant one. Two safeguards limit the risk that the coding simply confirmed a prior expectation. Disconfirming evidence was searched for deliberately: the trade-off cluster was assembled by looking for work that contradicts the correlation studies, and the correlation studies were read for internal exceptions rather than summarised by their abstracts. Studies whose conclusions cut against the argument developed here, including Hegre et al. (2020), which finds synergy dominance in changes as well as levels, are engaged where they resist the pattern rather than filed under it. The review nonetheless carries real boundaries. It applies no formal quality appraisal, makes no claim to exhaustive coverage, and cannot rule out that the specification axes it identifies are themselves correlated with unobserved features of the studies. Claims about mechanism in what follows are interpretive, derived from comparison across published designs rather than from re-estimation, and are marked as such. Table 1 Specification profiles and headline verdicts of major cross-goal interaction assessments Study Evidence base Analytical object Accounting boundary Order Headline verdict Pradhan et al. (2017) Official SDG indicators, 227 countries Levels (cross-section) Territorial Direct Positive indicator pairs outweigh negative ones; SDG 12 most often in trade-offs Scherer et al. (2018) 166 nations split into four income groups, trade-linked model Levels, footprints Consumption-based Direct Progress on social goals travels with higher carbon, land and water impacts O'Neill et al. (2018) Over 150 nations, needs indicators against downscaled boundaries Levels vs. absolute thresholds Consumption-based n/a No nation meets basic needs at a sustainable level of resource use Kroll et al. (2019) Global indicator trends and projections to 2030 Levels and change Territorial Direct Synergies around SDGs 1, 3, 7, 8, 9; persistent trade-offs for SDGs 11, 13, 14, 16, 17 Lusseau and Mancini (2019) Global time series, countries grouped by income Change, estimated network Territorial Direct Interaction structure differs by income; climate, inequality and consumption are the principal hurdles Hegre et al. (2020) Global indicators 2000–2016, principal component analysis Levels and change Territorial Direct Synergies prevail in levels and change; SDG 10 the exception Warchold et al. (2020) Global indicators by population, region, income Levels Territorial Direct Synergies outweigh trade-offs; pattern varies by income and region Pham-Truffert et al. (2020) Interaction network built from literature review Documented links Mixed Direct and indirect Systemic multipliers and virtuous cycles identified as entry points Zeng et al. (2021) ~180 countries, trade-embodied environmental impacts Levels Telecoupled, consumption-based Direct Telecoupling relocates environmental trade-offs rather than resolving them Xiao et al. (2022) SDG indicator network, plus-minus DEMATEL model Levels Territorial Direct and indirect Synergy effects reach 98.33% of total influence Zhang et al. (2022) 31 Chinese provinces, 2000–2020 Levels and change Territorial, subnational Direct Trade-offs in eastern provinces; synergies weakening over the decade Kostetckaia and Hametner (2022) EU member states, measured interlinkages and progress Rates of progress Territorial Direct Trade-offs slow national progress more than synergies accelerate it Ospina-Forero et al. (2022) 87 indicators, four countries, 20 years Levels, several estimators Territorial Direct Estimated network structure varies materially with the estimator chosen Warchold et al. (2022) UN, World Bank, Bertelsmann sets combined Levels Territorial Direct Interaction patterns are more different than similar across data sources Carlsen et al. (2024) Historical national indicator trajectories Trajectories Territorial Direct No observed trajectory is consistent with progress on all 17 goals Fairbrass et al. (2024) 231 SDG indicators, environmental policy modelling Levels and change Territorial Direct Environmental gains show no evidence of translating into social benefits Xiao et al. (2024) 768 indicator pairs, four transmission channels Levels Transboundary Direct High-income countries hold 14.18% of population and 60.60% of total interactions Note. Compiled by the author from the cited studies. “Analytical object” records whether a study analyses cross-sectional levels, rates of change, or whole trajectories; “accounting boundary” records whether environmental pressure is attributed territorially or to final consumption. Where a study combines designs, the classification reflects the design behind its headline claim. 3. Two Verdicts from One Indicator Set The synergy-dominant reading rests on a coherent sequence of studies. Pradhan et al. (2017) correlated pairs of SDG indicators across 227 countries, classified significant positive correlations as synergies and significant negative ones as trade-offs, and found positive pairs outweighing negative ones in most countries, with SDG 1 synergistic with most other goals. Warchold et al. (2020) disaggregated the same style of analysis by population, region and income and again found synergies outweighing trade-offs, with linear interactions outweighing non-linear ones. Hegre et al. (2020) summarised the goals through principal component analysis of global indicator data for 2000 to 2016 and reported that synergies prevail in levels and in change alike, with SDG 10 the exception that has not moved in step with the rest. Xiao et al. (2022) added indirect interlinkages through a plus-minus DEMATEL model and returned the 98.33% figure. Pham-Truffert et al. (2020) built a network from a literature review rather than indicator data and identified systemic multipliers and virtuous cycles usable as entry points. The opposing reading is equally well evidenced and rests on different measurement. O'Neill et al. (2018) compared the resource use associated with meeting basic human needs against downscaled planetary boundaries for over 150 nations and found that no country meets basic needs at a globally sustainable level of resource use, with universal achievement of more qualitative goals requiring two to six times the sustainable level. Scherer et al. (2018) modelled 166 nations split into four income groups within a trade-linked consumption framework and found that progress on poverty and inequality goals travels with higher carbon, land and water impacts. Fairbrass et al. (2024) modelled 231 SDG indicators, found protected areas and forest certification associated with improvements in forest and water ecosystems, and found no evidence that those environmental gains translate into social benefits while the global environmental state continues to decline. Carlsen et al. (2024) found no historical trajectory consistent with progress on all 17 goals. Kostetckaia and Hametner (2022) related measured interlinkages to observed progress in European Union member states and found trade-offs exerting a larger influence on the pace of progress than synergies. The synergy cluster already contains the seed of the conflict. Pradhan et al. (2017) identified SDG 12 as the goal most frequently associated with trade-offs. Kroll et al. (2019) projected trends to 2030 and located synergies around SDGs 1, 3, 7, 8 and 9 while finding persistent trade-offs and non-associations for SDGs 11, 13, 14, 16 and 17. Hegre et al. (2020) excepted SDG 10. Read together, these exceptions are not scattered: they concentrate in the environmental goals and the institutional ones. The synergy majority is therefore a majority computed over a set of pairs in which the socioeconomic goals, which co-vary strongly with development level, are heavily represented. Why has the field not resolved this? Part of the answer is bibliographic. The correlation studies and the biophysical-limits studies form largely separate citation communities, publishing in different outlets and addressing different audiences, so neither treats the other as a rival estimate of the same quantity. Part of it is how the discrepancy has been classified. Bennich et al. (2020) documented the methodological heterogeneity carefully, but framed it as a coverage and comparability problem to be managed through better reading practice rather than as a threat to the validity of the field's central claim. Managing heterogeneity and explaining a sign reversal are different tasks, and only the first has been attempted. That leaves the argument of this review. The disagreement between the clusters is not noise. Studies reporting synergy dominance share a design profile: territorial indicators, cross-sectional levels, national aggregates, global averaging, and in several cases the admission of indirect paths. Studies reporting structural conflict share the opposite profile. What separates them is specification, and the four sections that follow take each axis in turn. 4. Accounting Boundaries and the Displacement of Trade-Offs Whether a trade-off appears in a country's statistics depends on where the accounts are drawn. Territorial indicators attribute environmental pressure to the place where it physically occurs. Consumption-based accounts attribute it to the place whose final demand caused it. The goals themselves are monitored predominantly on the territorial basis, and this choice does analytical work that is rarely stated. Zeng et al. (2021) show how much work. Analysing roughly 180 countries, they find that around 78% of SDG indicators show statistically significant correlations with telecoupled environmental impacts embodied in trade, and that accounting for these distant impacts reduces the apparent trade-offs between environmental and development goals in developing countries, because the pressure has been relocated rather than avoided. Xiao et al. (2024) quantify the same displacement from the other direction, tracing transboundary interactions through trade, river flow, ocean currents and air flow across 768 pairs of SDG indicators. High-income countries hold 14.18% of world population and account for 60.60% of total SDG interactions. Trade-mediated synergies run 14.94% stronger with distant partners than with neighbours, while nature-mediated flows produce 39.29% stronger synergies among neighbouring states. Scherer et al. (2018) and O'Neill et al. (2018) complete the picture at the level of absolute limits. When social achievement is priced in consumption-based resource terms and compared against downscaled biophysical ceilings, the conflict between social and environmental goals is not a statistical tendency that better policy might dissolve. It is the current configuration of provisioning systems, and O'Neill et al. quantify the distance: no nation is presently inside both boundaries. The boundary is not a neutral technical default. It is built into the monitoring architecture on which most interaction studies depend, since the official indicator set records environmental pressure predominantly where it is emitted, extracted or discharged rather than where the demand for it originated. A country that imports the material-intensive stages of its consumption improves on the indicators the correlation studies read while the physical pressure continues to be generated elsewhere. Warchold et al. (2022) show that environmental goals are exactly where alternative indicator sets diverge most, which is what one would expect if the boundary convention were doing heavy and unacknowledged work. Treating this as a reason to distrust environmental indicators would be the wrong response; the point is that a boundary convention chosen for administrative reasons has become an analytical assumption without ever being argued for. The interpretive consequence follows directly. A territorial accounting boundary does not remove a trade-off from the world; it removes it from the national statistic in which the correlation is computed. Under consumption-based accounting the same country pair can move from synergy to conflict, and the effect is concentrated exactly where the exceptions in the correlation literature already cluster, in SDGs 12 through 15. Any global statement about the balance of synergies and trade-offs that does not name its accounting boundary is therefore underspecified, because a substantial share of the world's environmental pressure is being attributed to somebody. 5. Levels, Rates, and Trajectories The second axis concerns what the analysis takes as its object. A cross-sectional correlation between two indicators across countries measures the extent to which nations that score well on one also score well on the other. A correlation between rates of change measures whether moving on one accompanies moving on the other. These are different claims, and only the second speaks to what happens when a government acts. Level correlations across the full development spectrum are partly a restatement of development stage. Richer countries score better on most socioeconomic indicators at once, so pairwise positive correlations among those indicators are close to guaranteed before any policy interaction is invoked. Lusseau and Mancini (2019) make the dependence explicit, estimating interaction networks separately by income band and finding that goal contributions differ by country income, with limiting climate change, reducing inequalities and responsible consumption emerging as the principal obstacles. Warchold et al. (2020) reach a compatible conclusion from disaggregation, reporting that interaction patterns vary with income and region. When the object shifts from position to movement, the balance shifts with it. Kostetckaia and Hametner (2022) find a significant negative relationship between the presence of trade-offs and observed progress among EU member states, and conclude that trade-offs shape the pace of national progress more than synergies do. Carlsen et al. (2024) push the object further, from rates to whole trajectories, and find no country path consistent with advancing all 17 goals — a result that a level-based analysis of the same data cannot produce, because it never asks the question. Linnerud et al. (2021) show the corollary that reconciliation is possible without being automatic: clustering 117 countries within a defined sustainable development space, they find that some countries have closed far more of their competing gaps than others starting from similar positions. Hegre et al. (2020) complicate this axis rather than confirming it, since their principal component analysis reports synergy dominance in change as well as in levels. Two features of that design temper the tension. Principal components summarise each goal from its constituent indicators, so within-goal aggregation can absorb indicator-level conflicts before the goal-level correlation is computed; and the 2000 to 2016 window closes before the reversals in poverty, hunger and inequality trends that the United Nations (2025) records. Neither observation refutes the finding. Both suggest that the analytical object interacts with aggregation level, which is a hypothesis this review states rather than settles. 6. Indicator Sets, Estimators, and the Instability of Interaction Networks The third axis is the least visible in published abstracts and the most damaging to the field's headline claim. An interaction network is not read off the world; it is estimated from a chosen indicator set using a chosen estimator, and both choices move the result. Warchold et al. (2022) tested the first choice directly by assembling United Nations, World Bank and Bertelsmann Stiftung indicator sets into one database and comparing the interaction patterns each produces. The patterns are more different than similar, and the environmental goals show the largest discrepancies. Ospina-Forero et al. (2022) tested the second, noting that SDG research has largely ignored the formal network-estimation literature, then comparing estimation methods on 87 development indicators for four countries across two decades and finding that different estimators yield materially different network structures. Bennich et al. (2020) supply the field-level corollary: across 70 studies there is no agreement on conceptual framing or assessment method. Coverage compounds specification. Kluck et al. (2025) screened 1,511 records to 146 SDG modelling articles and found limited attention to SDGs 5, 10, 16 and 17, the goals covering gender equality, inequality, institutions and partnership. Song and Jang (2023) applied semantic network analysis to the target text and concluded that the most closely connected targets cross goal boundaries, so the 17-goal partition is a poor unit for policy design. Le Blanc (2015) had already shown that many documented biophysical, economic and social linkages appear nowhere in the explicit target wording. Reading these together supports an inference the individual studies do not make. The goals that modelling and indicator work cover least well are the institutional and distributional ones, and those are precisely where political conflict over resource allocation concentrates. A synergy share computed over a sub-network that thins out at SDGs 5, 10, 16 and 17 is therefore likely to be biased upward relative to the full system. This is an interpretive claim about the composition of the evidence base rather than a measured bias, and it is testable: re-estimating published networks with and without the under-covered goals would show its direction and size. 7. Interaction Order and the Scale of Observation The remaining two axes are best treated together, because both concern how far an analysis reaches from the point of observation. Order refers to whether indirect paths are admitted. Weitz et al. (2018) demonstrated with Swedish data that adding second-order effects to cross-impact scoring reorders target priorities. Xiao et al. (2022) reported the 98.33% synergy share only after indirect interlinkages entered the model. Pham-Truffert et al. (2020) built their multiplier analysis on the same principle, identifying targets whose outgoing influence exceeds their incoming influence. The methodological gain is real, since a policy's consequences do propagate. The interpretive caution is that in a network where direct positive ties outnumber direct negative ties, admitting longer paths multiplies sign-consistent chains faster than sign-inconsistent ones, so the indirect synergy share rises partly as a function of network density and the direct sign ratio. A high indirect synergy share is consequently weaker evidence of policy compatibility than it appears, and the strength of that effect can be estimated by permuting edge signs in a published network. Scale refers to the unit of observation, and the evidence here is unambiguous that global averages travel badly. Zhang et al. (2022) assessed Chinese provinces using a framework grouping goals into essential needs, objectives and governance, and found trade-offs between categories in eastern provinces, synergies in parts of central and western China, and a weakening of synergies over the last decade driven by regional divergence in SDG 7 progress. Adhikari et al. (2023) found biodiversity conservation in Nepal synergistic with economic growth, gender equality and climate action while conflicting with food security, energy access and poverty reduction. Alcamo et al. (2020) identified a disconnect between national SDG planning and local implementation that generates cross-scale trade-offs, and named critical transition zones where those conflicts bite hardest. Nilsson et al. (2018), extending the seven-point interaction scale first proposed by Nilsson et al. (2016), concluded that whether a given interaction is synergistic or conflicting depends on geographical context, resource endowment, time horizon and governance capacity. Moallemi et al. (2022) draw the practical consequence. Arguing that correlation-based and model-based interaction studies are often too technical and data-hungry for decision-makers, they offer eight recurring interaction archetypes as diagnostic and prospective tools. The value of archetypes in this setting is not simplification for its own sake. It is that an archetype carries its conditions with it, whereas a global average discards them. 8. A Conditional Framework for Assessing Goal Interactions The four preceding sections support a single reformulation. A measured interaction estimate is not an observation of the goal system alone. It is a function of the system and of five specification choices: the accounting boundary B, the analytical object O, the indicator set and estimator S, the interaction order R, and the scale and context of observation C. Figure 1 sets out the resulting Conditional Interaction Assessment framework, in which those five axes condition the estimate that a study reports and determine how strong a claim that estimate can carry. Figure 1 The Conditional Interaction Assessment framework: five specification choices, the estimate they condition, and the claim strength the estimate can support Note. Specification choices B, O, S, R and C are defined in the framework section and operationalised as reporting requirements in Table 2. The ladder on the right ranks the strength of claim an estimate can carry once its conditions are known. Five propositions state the framework in testable form. P1 (Boundary). For the same country set and period, interaction estimates computed on territorial indicators will report a higher synergy share than estimates computed on consumption-based or transboundary-adjusted indicators, with the difference concentrated in SDGs 12 to 15 (Scherer et al., 2018; Zeng et al., 2021; Xiao et al., 2024). P2 (Object). Estimates based on cross-sectional levels will report a higher synergy share than estimates based on rates of change, which will in turn report a higher share than estimates based on whole trajectories (Kostetckaia & Hametner, 2022; Carlsen et al., 2024). P3 (Specification). Interaction networks are not invariant to indicator set or estimator, and the sign of a given goal-pair relation can reverse under alternative defensible specifications, most often for environmental and institutional goals (Warchold et al., 2022; Ospina-Forero et al., 2022). P4 (Order). Admitting indirect paths raises the reported synergy share as a function of network density and the direct positive-to-negative tie ratio, so the increment attributable to indirect propagation is partly structural rather than substantive (Weitz et al., 2018; Xiao et al., 2022). P5 (Scale). Dispersion of interaction signs across subnational units and income groups exceeds the difference between global averages reported by competing studies, so global-average claims have low transfer value to a specific jurisdiction (Warchold et al., 2020; Zhang et al., 2022; Adhikari et al., 2023). The framework also implies a ladder of claim strength, and most published interaction statements sit lower on it than their phrasing suggests. The first rung is descriptive co-variation: two indicators move together in a sample. The second is conditional association: they move together under a stated boundary, object, specification, order and scale. The third is mechanism-supported interaction, where a documented causal pathway links them, as in the seven-point scoring of Nilsson et al. (2016) or the archetypes of Moallemi et al. (2022). The fourth is a policy-relevant causal claim, which requires evidence that an intervention on one goal changes the other in a named context. Correlation studies establish the first rung and, when they report their specification, the second. Their conclusions are frequently written as though they had reached the fourth. One goal pair shows the framework working. Take the relation between poverty reduction (SDG 1) and climate action (SDG 13). Under territorial indicators, cross-sectional levels, global averaging and admitted indirect paths, the pair reads as synergistic: Pradhan et al. (2017) place SDG 1 in synergy with most other goals, and Lusseau and Mancini (2019) find poverty alleviation exerting compound positive effects across the network. Shift the boundary to consumption-based accounting and the sign is no longer stable, since Scherer et al. (2018) find advances on poverty and inequality travelling with higher carbon, land and water footprints. Hold absolute limits in view and the conflict hardens further, because O'Neill et al. (2018) show that no nation currently delivers basic needs within a sustainable resource envelope. Disaggregate by income band and the relation splits, with Lusseau and Mancini (2019) identifying climate limitation as one of the principal hurdles precisely where consumption is highest. The same two goals, the same underlying data, five defensible specifications, and at least three different answers. Nothing about the goal system changed between those readings. Table 2 turns the five axes into a reporting specification: what each study must state, which proposition the statement tests, and what a reader may conclude if the statement is absent. The specification is deliberately cheap to satisfy. It asks for no additional computation, only for the disclosure of choices already made. Table 2 A reporting specification for Sustainable Development Goal interaction estimates Specification axis What the study must state Proposition tested Reading when the statement is absent Accounting boundary (B) Whether indicators are territorial, consumption-based, or adjusted for transboundary flows, and how environmental pressure embodied in trade is treated P1 Assume territorial; treat any reported synergy involving SDGs 12–15 as an upper bound Analytical object (O) Whether the estimate is computed on cross-sectional levels, rates of change, or observed trajectories, and over what period P2 Assume levels; the estimate describes where countries are, not what happens when they move Indicator set and estimator (S) Which indicator database was used, how missing values were handled, which estimator produced the network, and whether alternatives were tested P3 Treat the reported network as one draw from a set of defensible specifications, not as the network Interaction order (R) Whether indirect paths are admitted, to what path length, and what share of the reported synergy is attributable to them P4 Assume direct only; if indirect paths are admitted without decomposition, discount the synergy share Scale and context (C) The unit of observation, the income and regional composition of the sample, and whether subnational variation was examined P5 Assume global averaging; the estimate has low transfer value to any single jurisdiction Note. Compiled by the author. Propositions P1 to P5 are stated in the framework section. The specification requires no additional computation, only disclosure of choices already made in the course of the analysis. 9. Discussion What the reconciliation changes The central empirical claim of the SDG interaction field — that synergies outweigh trade-offs — is not false. It is underspecified to the point where it cannot be falsified as stated, because the quantity it names varies with choices that studies do not consistently report. Reframing that quantity as conditional resolves the apparent contradiction between the two clusters without discarding either. Pradhan et al. (2017) and Carlsen et al. (2024) are both right about what they measured, and the appearance of conflict comes from a shared vocabulary applied to different objects. This has consequences for how goal interdependence is theorised. The 2030 Agenda was designed as an integrated system, and Le Blanc (2015) showed early that its targets form a network denser than the Millennium Development Goals. Integration in the text does not imply compatibility in the world, and the evidence assembled here suggests that the compatibility question has no scale-free answer. The relevant theoretical object is not the interaction between two goals but the interaction between two goals under a stated set of conditions. The finding also bears on the effectiveness debate. Biermann et al. (2017) characterised the SDGs as governance through goals — inclusive in their formation, non-binding, institutionally weak, and heavily dependent on national translation. Biermann et al. (2022), meta-analysing over 3,000 studies published between 2016 and April 2021, concluded that the political impact of the goals has been largely discursive, altering how actors talk about sustainable development while rarely reallocating resources or changing legislation. A discursive mode of influence is exactly the mode in which a specification-dependent statistic does the most damage, because the optimistic end of the range circulates as a summary while the conditions that produced it stay in the methods section. Implications for practice Allen et al. (2021) reviewed over 150 publications alongside the Voluntary National Reviews of 56 countries and found that advanced interlinkage methods remain largely unused in actual national planning. The barrier is capability as much as awareness, and the response should not be to press governments toward the most technically demanding estimators. It should be to pair whatever method a country can run with an explicit statement of its five specification choices, and to prefer condition-carrying tools where capacity is limited. The archetypes of Moallemi et al. (2022) and the seven-point contextual scoring of Nilsson et al. (2016) are more defensible inputs to a national plan than an imported global correlation, because they force the analyst to name the context in which the interaction is claimed to hold. For agenda design after 2030, two points follow. Song and Jang (2023) show that the tightest target communities cross goal boundaries, and the six transformations proposed by Sachs et al. (2019) organise the agenda around delivery systems rather than goal numbers. Both suggest that the 17-goal partition, whatever its political value, is a weak analytical unit, and that successor architectures would gain by defining the units of coordination around the systems that actually produce the outcomes. The Independent Group of Scientists appointed by the Secretary-General (2023) argues that incremental and fragmented change is insufficient and identifies governance, finance, individual and collective action, science and capacity as levers of deliberate transformation. Those levers act on the conditions that this review identifies as decisive, which is a reason to treat conditions rather than correlations as the target of policy design. The United Nations (2025) reports real gains — universal electricity access reached in 45 countries, elimination of neglected tropical diseases in 54 — alongside a pace of change insufficient to meet the goals by 2030. Both halves of that assessment are consistent with a system whose interactions are conditional rather than fixed. A specification-dependent quantity also creates an incentive that the field should name. Where several defensible designs are available and only one number is reported, the reported number will tend to be the one that supports the argument the study is making, without anyone acting in bad faith. Journals and assessment bodies can close most of that space at low cost by asking authors to state their five choices and, where feasible, to report the synergy share under one alternative specification. A study that shows its result surviving a change of boundary or object has said something that a single matrix cannot say. Limitations This is an integrative review, and its boundaries are those of the form. It applies no formal quality appraisal and makes no claim to exhaustive coverage of a literature that Bennich et al. (2020) already showed to be large and fragmented. The coding behind Table 1 is a structured reading by a single author, not a double-coded protocol, and other readers could place particular studies differently on the object or order axes. The five propositions are derived from comparison across published designs and remain untested here; the paper offers no re-estimation of any dataset. The corpus skews toward globally scaled quantitative work published in a limited set of English-language outlets, which under-represents case-based and non-English scholarship where context-specific interactions are most likely to be documented. Claims about why indirect-path inclusion raises the synergy share, and about upward bias from thin coverage of SDGs 5, 10, 16 and 17, are interpretive inferences that the cited studies support but do not themselves state. Future research The propositions are designed to be cheap to test. P1 and P2 require only that an existing indicator panel be re-analysed under paired specifications — territorial against consumption-based, levels against rates — with the synergy share reported for each. P4 can be examined by sign-permutation on published networks, isolating how much of the indirect synergy share follows from density alone. P3 and P5 call for a multi-specification sensitivity study of the kind that Warchold et al. (2022) and Ospina-Forero et al. (2022) began within their own axes but that no study has yet run across axes simultaneously. Beyond hypothesis testing, the more consequential work is institutional: developing a short reporting convention for interaction studies, along the lines of Table 2, and embedding it in the guidance that shapes Voluntary National Reviews. Kluck et al. (2025) show that the modelling base still neglects the goals where distributional conflict lives, and closing that coverage gap would do more for the credibility of interaction evidence than another global correlation matrix. 10. Conclusion The literature on Sustainable Development Goal interactions has been reporting a stable finding that turns out to depend on how the question is asked. Synergy dominance emerges from territorial indicators, cross-sectional levels, global averages and networks that admit indirect paths. Structural conflict emerges from consumption-based accounts, rates and trajectories, subnational units, and analyses that hold absolute biophysical limits in view. Both bodies of evidence are sound within their own terms, and the contradiction between them dissolves once the measured synergy-to-trade-off ratio is treated as conditional on specification rather than as a property of the goal system. What this review closes is the interpretive gap between two literatures that had stopped reading each other. What it leaves open is the empirical size of each conditioning effect, which the five propositions are written to make testable. The practical consequence is immediate and does not wait on those tests. An interaction claim that does not state its accounting boundary, its analytical object, its indicator set and estimator, its interaction order and its scale cannot be interpreted by a reader, and should not be carried into a national plan as though the goal system had been shown to cohere. Declarations Funding. This research received no external funding. Conflicts of interest. The author declares no conflict of interest. Ethics statement. This study is a review of previously published literature and intergovernmental assessments. It involved no human participants, no animal subjects and no primary data collection, and therefore required no ethical approval. Data availability. No new data were generated. All sources analysed are published works listed in the reference list and identified by digital object identifier or public repository address. References Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M., & Prescott, G. W. (2023). A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development, 31(5), 3239-3253. https://doi.org/10.1002/sd.2582 Alcamo, J., Thompson, J., Alexander, A., Antoniades, A., Delabre, I., Dolley, J., Marshall, F., Menton, M., Middleton, J., & Scharlemann, J. P. W. (2020). Analysing interactions among the sustainable development goals: Findings and emerging issues from local and global studies. Sustainability Science, 15(6), 1561-1572. https://doi.org/10.1007/s11625-020-00875-x Allen, C., Metternicht, G., & Wiedmann, T. (2021). Priorities for science to support national implementation of the sustainable development goals: A review of progress and gaps. Sustainable Development, 29(4), 635-652. https://doi.org/10.1002/sd.2164 Bennich, T., Weitz, N., & Carlsen, H. (2020). Deciphering the scientific literature on SDG interactions: A review and reading guide. Science of the Total Environment, 728, Article 138405. https://doi.org/10.1016/j.scitotenv.2020.138405 Biermann, F., Hickmann, T., Sénit, C.-A., Beisheim, M., Bernstein, S., Chasek, P., Grob, L., Kim, R. E., Kotzé, L. J., Nilsson, M., Ordóñez Llanos, A., Okereke, C., Pradhan, P., Raven, R., Sun, Y., Vijge, M. J., van Vuuren, D., & Wicke, B. (2022). Scientific evidence on the political impact of the Sustainable Development Goals. Nature Sustainability, 5(9), 795-800. https://doi.org/10.1038/s41893-022-00909-5 Biermann, F., Kanie, N., & Kim, R. E. (2017). Global governance by goal-setting: The novel approach of the UN Sustainable Development Goals. Current Opinion in Environmental Sustainability, 26-27, 26-31. https://doi.org/10.1016/j.cosust.2017.01.010 Carlsen, H., Weitz, N., Petrova, K., & Kemp-Benedict, E. (2024). A sustainable trajectory for the 2030 Agenda: Targeting historical lock-ins to accelerate progress on all SDGs. Sustainable Development, 32(5), 4738-4747. https://doi.org/10.1002/sd.2929 Fairbrass, A. J., O'Sullivan, A., Campbell, J., & Ekins, P. (2024). The SDGs provide limited evidence that environmental policies are delivering multiple ecological and social benefits. Earth's Future, 12(5), Article e2024EF004451. https://doi.org/10.1029/2024EF004451 Hegre, H., Petrova, K., & von Uexkull, N. (2020). Synergies and trade-offs in reaching the Sustainable Development Goals. Sustainability, 12(20), Article 8729. https://doi.org/10.3390/su12208729 Independent Group of Scientists appointed by the Secretary-General. (2023). Global Sustainable Development Report 2023: Times of crisis, times of change - Science for accelerating transformations to sustainable development. United Nations. https://sdgs.un.org/gsdr/gsdr2023 Kluck, T., Li, C., & Pradhan, P. (2025). A systematic review highlights the need for holistic modeling of the Sustainable Development Goals. Sustainable Development, 33(6), 8398-8413. https://doi.org/10.1002/sd.70096 Kostetckaia, M., & Hametner, M. (2022). How Sustainable Development Goals interlinkages influence European Union countries' progress towards the 2030 Agenda. Sustainable Development, 30(5), 916-926. https://doi.org/10.1002/sd.2290 Kroll, C., Warchold, A., & Pradhan, P. (2019). Sustainable Development Goals (SDGs): Are we successful in turning trade-offs into synergies? Palgrave Communications, 5(1), Article 140. https://doi.org/10.1057/s41599-019-0335-5 Le Blanc, D. (2015). Towards integration at last? The sustainable development goals as a network of targets. Sustainable Development, 23(3), 176-187. https://doi.org/10.1002/sd.1582 Linnerud, K., Holden, E., & Simonsen, M. (2021). Closing the sustainable development gap: A global study of goal interactions. Sustainable Development, 29(4), 738-753. https://doi.org/10.1002/sd.2171 Lusseau, D., & Mancini, F. (2019). Income-based variation in Sustainable Development Goal interaction networks. Nature Sustainability, 2(3), 242-247. https://doi.org/10.1038/s41893-019-0231-4 Moallemi, E. A., Hosseini, S. H., Eker, S., Gao, L., Bertone, E., Szetey, K., & Bryan, B. A. (2022). Eight archetypes of Sustainable Development Goal (SDG) synergies and trade-offs. Earth's Future, 10(9), Article e2022EF002873. https://doi.org/10.1029/2022EF002873 Nilsson, M., Chisholm, E., Griggs, D., Howden-Chapman, P., McCollum, D., Messerli, P., Neumann, B., Stevance, A.-S., Visbeck, M., & Stafford-Smith, M. (2018). Mapping interactions between the sustainable development goals: Lessons learned and ways forward. Sustainability Science, 13(6), 1489-1503. https://doi.org/10.1007/s11625-018-0604-z Nilsson, M., Griggs, D., & Visbeck, M. (2016). Policy: Map the interactions between Sustainable Development Goals. Nature, 534(7607), 320-322. https://doi.org/10.1038/534320a O'Neill, D. W., Fanning, A. L., Lamb, W. F., & Steinberger, J. K. (2018). A good life for all within planetary boundaries. Nature Sustainability, 1(2), 88-95. https://doi.org/10.1038/s41893-018-0021-4 Ospina-Forero, L., Castañeda, G., & Guerrero, O. A. (2022). Estimating networks of sustainable development goals. Information & Management, 59(5), Article 103342. https://doi.org/10.1016/j.im.2020.103342 Pham-Truffert, M., Metz, F., Fischer, M., Rueff, H., & Messerli, P. (2020). Interactions among Sustainable Development Goals: Knowledge for identifying multipliers and virtuous cycles. Sustainable Development, 28(5), 1236-1250. https://doi.org/10.1002/sd.2073 Pradhan, P., Costa, L., Rybski, D., Lucht, W., & Kropp, J. P. (2017). A systematic study of Sustainable Development Goal (SDG) interactions. Earth's Future, 5(11), 1169-1179. https://doi.org/10.1002/2017EF000632 Sachs, J. D., Schmidt-Traub, G., Mazzucato, M., Messner, D., Nakicenovic, N., & Rockström, J. (2019). Six transformations to achieve the Sustainable Development Goals. Nature Sustainability, 2(9), 805-814. https://doi.org/10.1038/s41893-019-0352-9 Scherer, L., Behrens, P., de Koning, A., Heijungs, R., Sprecher, B., & Tukker, A. (2018). Trade-offs between social and environmental Sustainable Development Goals. Environmental Science & Policy, 90, 65-72. https://doi.org/10.1016/j.envsci.2018.10.002 Song, J., & Jang, C. (2023). Unpacking the sustainable development goals (SDGs) interlinkages: A semantic network analysis of the SDGs targets. Sustainable Development, 31(4), 2784-2796. https://doi.org/10.1002/sd.2547 United Nations. (2025). The Sustainable Development Goals report 2025. United Nations Department of Economic and Social Affairs. https://unstats.un.org/sdgs/report/2025/ Warchold, A., Pradhan, P., & Kropp, J. P. (2020). Variations in sustainable development goal interactions: Population, regional, and income disaggregation. Sustainable Development, 29(2), 285-299. https://doi.org/10.1002/sd.2145 Warchold, A., Pradhan, P., Thapa, P., Putra, M. P. I. F., & Kropp, J. P. (2022). Building a unified sustainable development goal database: Why does sustainable development goal data selection matter? Sustainable Development, 30(5), 1278-1293. https://doi.org/10.1002/sd.2316 Weitz, N., Carlsen, H., Nilsson, M., & Skånberg, K. (2018). Towards systemic and contextual priority setting for implementing the 2030 Agenda. Sustainability Science, 13(2), 531-548. https://doi.org/10.1007/s11625-017-0470-0 Xiao, H., Bao, S., Ren, J., Xu, Z., Xue, S., & Liu, J. (2024). Global transboundary synergies and trade-offs among Sustainable Development Goals from an integrated sustainability perspective. Nature Communications, 15(1), Article 500. https://doi.org/10.1038/s41467-023-44679-w Xiao, H., Liu, Y., & Ren, J. (2022). Synergies and trade-offs across sustainable development goals: A novel method incorporating indirect interactions analysis. Sustainable Development, 31(2), 1135-1148. https://doi.org/10.1002/sd.2446 Zeng, Y., Runting, R. K., Watson, J. E. M., & Carrasco, L. R. (2021). Telecoupled environmental impacts are an obstacle to meeting the sustainable development goals. Sustainable Development, 30(1), 76-82. https://doi.org/10.1002/sd.2229 Zhang, J., Wang, S., Pradhan, P., Zhao, W., & Fu, B. (2022). Untangling the interactions among the Sustainable Development Goals in China. Science Bulletin, 67(9), 977-984. https://doi.org/10.1016/j.scib.2022.01.006 Hashtags: #SDGs #GlobalGoals #2030Agenda #SustainableDevelopmentGoals #GoalInteractions #Synergies #TradeOffs #PolicyCoherence #IntegratedAssessment #U7YJournal #U7Y #AcademicResearch

Author: L. Kareem Affiliation: European Council of Leading Business Schools ECLBS (ECLBS) Received 1 March 2024; Revised 7 May 2024; Accepted 16 May 2024; Available online 1 June 2024; Version of Record 1 June 2024, Publication Update 5 February 2026. https://doi.org/10.65326/u7y566815 Volume 1, December 2024, (10003-2) Abstract Distance education has expanded continuously since the nineteenth century, adapting its media, pedagogy, and institutional forms to successive waves of technological change. Much of the field’s historical writing, however, remains descriptive: it catalogues technologies and milestones without an analytical account of what persists across eras and what genuinely changes. This article offers a historical-interpretive synthesis of the development of distance education, from correspondence study to contemporary digital and artificial-intelligence-mediated learning. Drawing on the major historical and review literature of the field, it analyses each developmental period along four recurring dimensions—dominant medium, prevailing pedagogy, mode of interaction, and access and equity—to distinguish continuities from discontinuities. The analysis indicates that technological change has repeatedly widened access while leaving the field’s central pedagogical problem, the management of the distance between teacher and learner, only partially resolved. On this basis, the article advances a set of propositions linking technological transitions to recurring tensions in interaction, equity, and institutional form, and situates them within established accounts of transactional distance and generational pedagogy. The contribution is a structured periodization that reframes the history of distance education as a sequence of responses to an enduring problem rather than a linear progression of technologies. Keywords: distance education; online learning; educational history; transactional distance; periodization; educational technology 1. Introduction Distance education is most usefully understood not as a single technology but as an organised attempt to sustain teaching and learning when teacher and learner are separated in space and, often, in time. Defined in this way, it predates the internet by more than a century and has repeatedly reconstituted itself around whatever communication medium was dominant in its era, from the postal service to broadcast media, networked computers, and, most recently, generative artificial intelligence (Bozkurt, 2019a). Its long trajectory makes it an instructive case for understanding how educational institutions absorb technological change without losing their core function. The historiography of the field is substantial but uneven. A large body of writing documents the sequence of technologies and the institutions that adopted them, and several quantitative mapping studies have charted how research interests have shifted over time (Zawacki-Richter & Naidu, 2016). What is comparatively scarce is interpretive work that treats the history analytically: that asks which features of distance education recur across every period and which are genuinely produced by a new medium. In the absence of such synthesis, technological novelty is easily mistaken for pedagogical novelty, and each new tool is presented as a break with the past when it more often reproduces older problems in a new form (Anderson & Dron, 2011). This is the gap the present article addresses. The article therefore pursues a single analytical question: across the major periods of distance education, what has changed and what has persisted, and what does that pattern imply for how the field theorises its own development? To answer it, the study reads the documented history through four dimensions that are present in every era—the dominant communication medium, the prevailing pedagogy, the available mode of interaction, and the distribution of access—and uses the comparison to surface continuities that a purely chronological account tends to obscure. The aim is not to add new empirical facts to the historical record but to organise existing knowledge into an argument and to express that argument as testable propositions. The remainder of the article sets out the conceptual framework and method, presents the period-by-period analysis, advances the resulting propositions, and discusses their implications for distance education theory and current debates over equity and automation. 2. Conceptual Framework Two long-standing constructs anchor the analysis. The first is transactional distance, the proposition that the meaningful separation in distance education is pedagogical rather than merely geographic, and that it is governed by the interplay of dialogue, structure, and learner autonomy (Moore, 1973). Transactional distance directs attention away from the technology itself and toward the quality of the interaction the technology makes possible, which is precisely the dimension that a technology-centred history tends to neglect. The second is the account of distance education pedagogy as a succession of overlapping generations—behaviourist, social-constructivist, and connectivist—in which later generations add to rather than replace earlier ones (Anderson & Dron, 2011). Read together, these constructs suggest that the history of distance education should be examined as a history of how successive media reshaped, but did not eliminate, the problem of distance. From these constructs the study derives four analytical dimensions applied uniformly to each period. The dominant medium identifies the communication technology around which provision was organised. The prevailing pedagogy identifies the dominant theory of learning enacted in that provision. The mode of interaction identifies whether exchange between teacher and learner was largely one-way, two-way but delayed, or interactive and immediate. The access and equity dimension identifies who was newly able to study, and who remained excluded, as a result of the medium. Holding these four dimensions constant across eras makes it possible to compare periods that are otherwise difficult to set side by side and to distinguish genuine transformation from the reappearance of familiar tensions in new technical dress. 3. Method 3.1 Research design The study uses a historical-interpretive review design. This design is appropriate when the goal is conceptual synthesis and theory development rather than the estimation of an effect, and it is established practice in fields that periodise their own development (Bozkurt, 2019a; Zawacki-Richter & Naidu, 2016). The design is interpretive in that it reconstructs a coherent argument from an existing documentary record; it is structured in that the reconstruction proceeds through an explicit framework, the four dimensions defined above, applied consistently to each period. 3.2 Source selection Sources were selected purposively for their authority on the history, theory, and research trends of distance education rather than to achieve exhaustive coverage. Priority was given to peer-reviewed historical analyses and to systematic and bibliometric reviews published in the established journals of the field, because such reviews already aggregate primary studies and so provide a defensible evidentiary base for claims about each period (Bozkurt, Akgün-Özbek, & Zawacki-Richter, 2017; Zawacki-Richter, Marín, Bond, & Gouverneur, 2019). For the contemporary period, recent reviews of online learning, open educational resources, micro-credentials, and artificial intelligence in higher education were used to characterise current trends while avoiding reliance on individual case studies. Foundational milestones in the earlier periods—correspondence instruction, educational broadcasting, and early computer-based learning—are reported as established historical fact and are interpreted, rather than re-evidenced, through the cited historiography. 3.3 Analytical procedure The analysis proceeded in three steps. First, the documented history was divided into four periods defined by the dominant medium: correspondence, broadcast, networked and online, and digital, open, and artificial-intelligence-mediated provision. Second, each period was characterised along the four analytical dimensions, drawing the characterisation from the cited literature. Third, the periods were compared across dimensions to identify continuities and discontinuities, and the comparison was condensed into a set of propositions intended to be evaluable in subsequent empirical work. Table 1 summarises the periodisation that resulted from the first two steps and serves as the evidentiary backbone for the propositions developed later. 3.4 Scope and limitations of the design The study is a conceptual synthesis, not a systematic review conducted under a formal protocol, and it makes no claim to statistical generalisation. Its scope is restricted to formal distance and higher education and to the Anglophone literature that dominates the field’s historiography. These boundaries are deliberate, but they constrain the claims that can be made; the implications are revisited in the discussion of limitations. Table 1. A periodization of distance education across four analytical dimensions. Period Dominant medium Prevailing pedagogy Mode of interaction Access and equity dynamic Correspondence (1840s–1920s) Print and postal service Transmission of structured content; self-study Two-way but heavily delayed Opened study to those barred by geography, work, or gender; limited by literacy and postal reach Broadcast (1920s–1980s) Radio and television Behaviourist transmission to a mass audience Predominantly one-way Extended reach and scale; weak feedback constrained genuine participation Networked and online (1990s–2000s) Internet and learning management systems Social-constructivist collaboration Asynchronous and synchronous; two-way Flexible access at scale; introduced the digital divide as a new axis of exclusion Digital, open and AI-mediated (2010s–present) Open platforms, mobile devices, AI systems Connectivist and adaptive; personalised Interactive, on-demand, increasingly automated Near-universal potential reach; equity reframed around connectivity, data, and algorithmic fairness Note. Periods are defined by the dominant communication medium and overlap at their boundaries. Pedagogical generations are cumulative rather than mutually exclusive (Anderson & Dron, 2011); later media did not displace earlier practices but layered new possibilities upon them. 4. The Evolution of Distance Education 4.1 The correspondence period Organised distance education begins with correspondence study in the mid-nineteenth century, when instruction in subjects such as shorthand was delivered and returned by post. Within decades, universities and commercial schools were offering correspondence courses on a considerable scale, and the model was consolidated institutionally around the postal exchange of lessons and assignments (Bozkurt, 2019b). Read through the four dimensions, the period is defined by a print medium, a transmission pedagogy in which carefully structured materials substituted for the teacher’s presence, and a mode of interaction that was genuinely two-way—students submitted work and received correction—but subject to long delays. Its decisive contribution was to access: correspondence study reached working adults, women, and rural populations who were excluded from campus-based provision, establishing widened participation as the field’s founding rationale. The period also established the field’s founding problem. Because dialogue was slow and structure carried most of the pedagogical load, correspondence study made the management of transactional distance an explicit design concern long before the term existed (Moore, 1973). The tension between the reach the medium afforded and the thinness of the interaction it permitted recurs in every subsequent period. 4.2 The broadcast period From the 1920s, radio and later television extended distance education to mass audiences. Educational broadcasting could reach far larger numbers than the post and could convey demonstration and speech, but it did so through an essentially one-way channel. In the terms of the framework, the broadcast period combined a high-reach medium with a behaviourist transmission pedagogy and a sharply reduced capacity for interaction; structured feedback, which correspondence study had at least provided in delayed form, was largely absent (Bozkurt, 2019a). The period therefore intensified the founding tension rather than resolving it: each gain in scale was purchased with a loss in dialogue. This trade-off is analytically important because it shows that increased reach and improved interaction do not advance together as a matter of course. Broadcasting maximised one dimension of distance education while regressing on another, a pattern that recurs whenever a new medium is adopted primarily for its capacity to scale. 4.3 The networked and online period The diffusion of personal computers and then the internet from the 1990s reconfigured the field. Early computer-based learning systems had already demonstrated individualised instruction and immediate feedback; networked communication generalised these capabilities and added something the broadcast era lacked, namely two-way interaction at a distance. Learning management systems organised the delivery, assessment, and administration of online courses, and online provision expanded rapidly across both established universities and new institutions (Zawacki-Richter & Naidu, 2016). Pedagogically, the period is best characterised by the rise of social-constructivist approaches that treated learning as a collaborative and dialogic process rather than a transmission, a shift captured in frameworks that foreground the social, cognitive, and teaching dimensions of an online community (Fiock, 2020). Against the framework, the networked period is the first in which a single medium improved reach and interaction simultaneously, narrowing the trade-off that had defined the previous two periods. Yet it introduced a new axis of exclusion. Access now depended on connectivity, devices, and digital skills, so the medium that widened participation also created the digital divide, relocating rather than removing the field’s equity problem (Guo & Wan, 2022). Evidence on student engagement in this period further indicates that technology supported participation unevenly and was often deployed with limited grounding in learning theory, qualifying any straightforward narrative of progress (Bedenlier, Bond, Buntins, Zawacki-Richter, & Kerres, 2020). 4.4 The digital, open, and AI-mediated period The most recent period is marked by openness, mobility, and automation. Massive open online courses extended access toward a global scale and prompted sustained research on participation, retention, and the limits of openness (Bozkurt, Akgün-Özbek, & Zawacki-Richter, 2017). Open educational resources advanced the same logic at the level of content, although the evidence on their effects remains uneven and concentrated in particular regions and disciplines (Otto, Schröder, Diekmann, & Sander, 2021). Mobile devices changed the locus of study, allowing learning to be distributed across times and places that earlier media could not reach, while raising distinct design and engagement challenges (Crompton & Burke, 2018). In the framework’s terms, the period pushes reach toward its theoretical maximum and makes interaction continuous and on demand. Artificial intelligence is the period’s defining and least settled development. Reviews of AI in higher education document applications in profiling and prediction, intelligent tutoring, assessment, and adaptive personalisation, but they also note a persistent weakness in pedagogical and ethical grounding and a striking absence of educators from the design of these systems (Zawacki-Richter et al., 2019). Subsequent syntheses of the field reinforce this assessment, calling for greater attention to ethics, collaboration, and methodological rigour as the technology moves from laboratories into routine practice (Bond et al., 2024). The arrival of generative AI has sharpened these concerns, particularly around assessment, where reviews report both genuine opportunities to support self-regulated learning and feedback and serious risks to academic integrity (Xia, Weng, Ouyang, Lin, & Chiu, 2024). The pandemic-era shift to emergency remote teaching, although a disruption rather than a developmental stage, accelerated the period’s tendencies and exposed its fault lines; the literature is careful to distinguish hurried emergency provision from designed online learning (Adedoyin & Soykan, 2023), and documents both the scale of institutional adaptation (Anthony Jnr & Noel, 2021) and the uneven burden it placed on households and learners with the least support (Misirli & Ergulec, 2021; Frei-Landau & Avidov-Ungar, 2022). 5. Propositions The cross-period comparison summarised in Table 1 supports five propositions. They are advanced as interpretive claims grounded in the documentary record rather than as empirically tested results, and each is framed so that it could be examined in subsequent research. Proposition 1. Across periods, the adoption of a new medium in distance education has been driven primarily by gains in reach, and only secondarily by gains in the quality of interaction. The correspondence and broadcast periods illustrate the pattern most clearly, but it recurs wherever a medium is adopted chiefly for its capacity to scale. Proposition 2. Increases in reach and improvements in interaction are not jointly guaranteed by technological change; before the networked period they were frequently traded against one another, and the broadcast period represents the extreme case in which scale was maximised at the expense of dialogue. Proposition 3. Each medium that widened access also generated a new form of exclusion specific to its infrastructure—literacy and postal reach, signal coverage, and most recently connectivity, devices, and data—so that the field’s equity problem is relocated by technological change rather than solved by it. Proposition 4. Pedagogical change in distance education is cumulative rather than substitutive: transmission, collaboration, and networked or adaptive learning coexist within contemporary provision, consistent with the generational account of pedagogy. Proposition 5. The management of transactional distance is the field’s enduring problem; new media change the means available for managing it but do not dissolve it, and technologies that automate interaction reconfigure, rather than remove, the question of how dialogue and structure are balanced. 6. Discussion The contribution of this analysis is to reframe the history of distance education from a sequence of technologies into a sequence of responses to a stable problem. This reframing engages directly with the field’s central theory. Transactional distance theory holds that the consequential separation between teacher and learner is pedagogical and is regulated by dialogue, structure, and autonomy (Moore, 1973). The periodisation developed here supplies historical content for that claim: it shows that every medium, from print to artificial intelligence, has been an instrument for managing transactional distance, and that media differ not in whether they confront the problem but in the balance of dialogue and structure they make feasible. The history thus operationalises an otherwise abstract construct, and Proposition 5 states the relationship in a form open to further examination. The analysis also extends the generational account of distance education pedagogy (Anderson & Dron, 2011). That account is usually read as a sequence of pedagogical ideas; the present synthesis grounds it in the material history of media and, through Proposition 4, treats the coexistence of behaviourist, constructivist, and connectivist practice as an empirical feature of contemporary provision rather than a theoretical residue. Read together, the two theories describe complementary aspects of the same history: one specifies the problem that persists, the other the repertoire of pedagogical responses that accumulates. The synthesis speaks to two live debates. The first concerns equity. Optimistic accounts treat each new medium as a step toward universal access, while critics emphasise the inequalities that online and open provision reproduce. Proposition 3 reconciles these positions by treating exclusion as medium-specific: access genuinely widens at each transition, but a new infrastructural barrier appears in step with it, which is why the digital divide succeeds rather than ends the older barriers of distance and cost (Guo & Wan, 2022; Frei-Landau & Avidov-Ungar, 2022). The second debate concerns automation. Enthusiasm for adaptive and generative systems is tempered by evidence that such systems are frequently designed without pedagogical grounding or educator involvement, and that they raise unresolved questions of ethics and integrity (Zawacki-Richter et al., 2019; Bond et al., 2024; Xia et al., 2024). The historical pattern cautions against reading automation as the end of the field’s problem; on the present argument it is the latest, and in some respects the most consequential, reconfiguration of how transactional distance is managed. The recurring observation that technology is adopted ahead of a clear pedagogical rationale (Bedenlier et al., 2020) is, in this light, not a contemporary failing but a structural feature of a field whose history has repeatedly been led by the medium. 7. Limitations and Future Research Three limitations qualify the analysis. First, it is a conceptual synthesis rather than a systematic review conducted under a registered protocol; its propositions are interpretive and require empirical evaluation. Second, the source base is weighted toward the Anglophone literature and toward formal higher education, which may understate developments in other languages, regions, and sectors and limits the generalisability of the periodisation. Third, periodisation imposes boundaries on a continuous process and risks overstating the coherence of each era, even though the periods overlap in practice. These limitations indicate productive directions for further work. The propositions could be tested through comparative case studies that hold the four dimensions constant across institutions or national systems, or through bibliometric analysis designed to detect the reach-versus-interaction trade-off in the historical record. The equity proposition invites focused study of how successive infrastructural barriers are distributed across populations, particularly in lower-income contexts and outside higher education. The questions raised by automation—how generative and adaptive systems alter dialogue, structure, and learner autonomy—are especially urgent, and would benefit from longitudinal designs that observe provision as the technology matures, alongside research into emerging credentialing forms such as micro-credentials, where the evidence base is still consolidating (Varadarajan, Koh, & Daniel, 2023; Thi Ngoc Ha, Spittle, Watt, & Van Dyke, 2023; Tamoliune et al., 2023). 8. Conclusion The history of distance education is often told as a succession of technologies, each presented as a break with the past. This article has argued for a different reading. By analysing each period along the same four dimensions, it shows that distance education has been a continuous effort to manage the pedagogical distance between teacher and learner, and that successive media have changed the means of managing that distance without dissolving the problem itself. The contribution is a structured periodisation and an associated set of propositions that connect technological transitions to recurring tensions in interaction, equity, and institutional form, and that link the field’s history to its central theories of transactional distance and generational pedagogy. Seen this way, the latest turn toward open, mobile, and automated provision is best understood not as the resolution of the field’s founding problem but as its newest expression. References Adedoyin, O. B., & Soykan, E. (2023). COVID-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31(2), 863–875. https://doi.org/10.1080/10494820.2020.1813180 Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. The International Review of Research in Open and Distributed Learning, 12(3), 80–97. https://doi.org/10.19173/irrodl.v12i3.890 Anthony Jnr, B., & Noel, S. (2021). Examining the adoption of emergency remote teaching and virtual learning during and after COVID-19 pandemic. International Journal of Educational Management, 35(6), 1136–1150. https://doi.org/10.1108/IJEM-08-2020-0370 Bedenlier, S., Bond, M., Buntins, K., Zawacki-Richter, O., & Kerres, M. (2020). Facilitating student engagement through educational technology in higher education: A systematic review in the field of arts and humanities. Australasian Journal of Educational Technology, 36(4), 126–150. https://doi.org/10.14742/ajet.5477 Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), Article 4. https://doi.org/10.1186/s41239-023-00436-z Bozkurt, A. (2019a). From distance education to open and distance learning: A holistic evaluation of history, definitions, and theories. In S. Sisman-Ugur & G. Kurubacak (Eds.), Handbook of research on learning in the age of transhumanism (pp. 252–273). IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch016 Bozkurt, A. (2019b). Intellectual roots of distance education: A progressive knowledge domain analysis. Distance Education, 40(4), 497–514. https://doi.org/10.1080/01587919.2019.1681894 Bozkurt, A., Akgün-Özbek, E., & Zawacki-Richter, O. (2017). Trends and patterns in massive open online courses: Review and content analysis of research on MOOCs (2008–2015). The International Review of Research in Open and Distributed Learning, 18(5), 118–147. https://doi.org/10.19173/irrodl.v18i5.3080 Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers & Education, 123, 53–64. https://doi.org/10.1016/j.compedu.2018.04.007 Fiock, H. (2020). Designing a community of inquiry in online courses. The International Review of Research in Open and Distributed Learning, 21(1), 135–153. https://doi.org/10.19173/irrodl.v20i5.3985 Frei-Landau, R., & Avidov-Ungar, O. (2022). Educational equity amidst COVID-19: Exploring the online learning challenges of Bedouin and Jewish female preservice teachers in Israel. Teaching and Teacher Education, 111, Article 103623. https://doi.org/10.1016/j.tate.2021.103623 Guo, C., & Wan, B. (2022). The digital divide in online learning in China during the COVID-19 pandemic. Technology in Society, 71, Article 102122. https://doi.org/10.1016/j.techsoc.2022.102122 Misirli, O., & Ergulec, F. (2021). Emergency remote teaching during the COVID-19 pandemic: Parents’ experiences and perspectives. Education and Information Technologies, 26(6), 6699–6718. https://doi.org/10.1007/s10639-021-10520-4 Moore, M. G. (1973). Toward a theory of independent learning and teaching. The Journal of Higher Education, 44(9), 661–679. https://doi.org/10.2307/1980599 Otto, D., Schröder, N., Diekmann, D., & Sander, P. (2021). Made open: The state of international evidence-based research on Open Educational Resources (OER). Zeitschrift für Erziehungswissenschaft, 24(5), 1061–1085. https://doi.org/10.1007/s11618-021-01043-2 Tamoliune, G., Greenspon, R., Tereseviciene, M., Volungeviciene, A., Trepule, E., & Dauksiene, E. (2023). Exploring the potential of micro-credentials: A systematic literature review. Frontiers in Education, 7, Article 1006811. https://doi.org/10.3389/feduc.2022.1006811 Thi Ngoc Ha, N., Spittle, M., Watt, A., & Van Dyke, N. (2023). A systematic literature review of micro-credentials in higher education: A non-zero-sum game. Higher Education Research & Development, 42(6), 1527–1548. https://doi.org/10.1080/07294360.2022.2146061 Varadarajan, S., Koh, J. H. L., & Daniel, B. K. (2023). A systematic review of the opportunities and challenges of micro-credentials for multiple stakeholders: Learners, employers, higher education institutions and government. International Journal of Educational Technology in Higher Education, 20, Article 13. https://doi.org/10.1186/s41239-023-00381-x Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1), Article 40. https://doi.org/10.1186/s41239-024-00468-z Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0 Zawacki-Richter, O., & Naidu, S. (2016). Mapping research trends from 35 years of publications in Distance Education. Distance Education, 37(3), 245–269. https://doi.org/10.1080/01587919.2016.1185079 #DistanceEducation #OnlineLearning #EdTech #HigherEducation #EducationalTechnology #TransactionalDistance #MOOCs #AIinEducation #DigitalLearning #RemoteLearning #OpenEducation #EducationResearch #ELearning #FutureOfEducation #LifelongLearning

Authors: Mikito Takayasu Affiliation: The University of Tokyo ORCID ID: 0009-0008-9398-5545 Submitted 10 February 2026; Revised 05 April 2026; Accepted 30 May 2026; Available online 19 June 2026; Version of Record 19 June 2026. https://doi.org/10.65326/u7y566831 Volume 3, December 2026, (10027) Abstract Tokenization promises to convert lumpy, illiquid real-world assets into divisible, transferable claims, yet secondary markets for these instruments remain thin and trading is infrequent. Standard asset pricing models, including the capital asset pricing model and its liquidity-adjusted extensions, were not designed for assets whose holders derive consumption, access, or governance value directly from ownership. This paper develops a conceptual asset pricing framework for utility-backed non-fungible tokens (NFTs) and tokenized real-world assets by augmenting the liquidity-adjusted capital asset pricing model with a utility (convenience) yield. The framework decomposes the required pecuniary return into a risk-free rate, a systematic liquidity-risk premium, an amortized illiquidity level premium that scales with transaction costs and turnover, and a utility-yield offset that lowers the return investors require in cash. Two analytical implications follow. First, utility backing compresses observed pecuniary returns without eliminating the underlying illiquidity premium. Second, where utility flows covary positively with illiquidity, estimates that regress pecuniary returns on liquidity proxies understate the gross illiquidity premium. An illustrative calibration, with parameter ranges drawn from the empirical tokenization literature, quantifies the mechanism rather than estimating it. The framework yields testable predictions and implications for valuation and disclosure. Keywords: illiquidity premium; tokenization; non-fungible tokens; real-world assets; liquidity-adjusted CAPM; convenience yield 1. Introduction Blockchain-based tokenization allows ownership claims on physical and financial assets to be recorded, divided, and transferred on a distributed ledger (Yermack, 2017; Cong & He, 2019). Proponents argue that converting indivisible, costly-to-trade assets such as real estate, fine art, private credit, and infrastructure into divisible tokens lowers entry barriers, widens the investor base, and reduces the discount investors apply for being unable to sell quickly (Baum, 2021; Schär, 2021). The implicit claim is that tokenization shrinks the illiquidity premium that has long been documented for the underlying asset classes. The empirical record to date is more cautious. Studies of the first large tokenized asset class, residential real estate issued through security token offerings, find that ownership is widely dispersed but secondary trading is sparse: tokens change hands roughly once per year, transaction costs remain material, and investors hold poorly diversified positions (Kreppmeier et al., 2023; Swinkels, 2023). Markets for non-fungible tokens (NFTs) display similar features. Trading is concentrated, intermittent, and dominated by a small set of active participants, and prices are strongly tied to the broader cryptocurrency cycle (Nadini et al., 2021; Dowling, 2022a, 2022b; Kräussl & Tugnetti, 2024). Tokenization changes the rails on which ownership is recorded and transferred, but it does not by itself create deep, continuous secondary markets. A second feature distinguishes many tokenized assets from the securities that asset pricing theory was built to describe. A growing share of tokens are utility-backed: holding the token confers a stream of non-pecuniary or in-kind benefits beyond any claim on future cash flows. These benefits include access to a service or community, governance and voting rights, in-game or metaverse functionality, staking and royalty entitlements, and the consumption value of provenance and display (Cong et al., 2021; Kräussl & Tugnetti, 2024). For such assets the return that matters to the marginal holder is not exhausted by the pecuniary payoff, because part of the value is consumed rather than realized through sale. These two features interact in a way that existing models do not capture. The capital asset pricing model and its multifactor descendants price the covariance of cash returns with systematic risk (Sharpe, 1964; Lintner, 1965; Fama & French, 1993). The liquidity literature extends this logic to the cost and risk of trading, treating illiquidity both as a level cost borne by holders and as a priced risk factor (Amihud & Mendelson, 1986; Amihud, 2002; Pástor & Stambaugh, 2003; Acharya & Pedersen, 2005). None of these frameworks accommodates an asset whose holder simultaneously faces a large and uncertain cost of trading and derives consumption value from continued ownership. When both forces are present, the pecuniary return observed in the market is a biased measure of the compensation investors require for bearing illiquidity. This paper addresses that gap. It develops a conceptual asset pricing framework for utility-backed tokenized assets by augmenting the liquidity-adjusted capital asset pricing model of Acharya and Pedersen (2005) with a utility (convenience) yield analogous to that used for commodities and money-like claims. The required pecuniary return is decomposed into four components: the risk-free rate, a systematic liquidity-risk premium, an amortized illiquidity level premium that scales with transaction costs and turnover in the manner of Amihud and Mendelson (1986), and a utility-yield offset that reduces the cash return investors demand. The contribution is theoretical and interpretive. The framework is not estimated; instead it is used to derive qualitative implications and is illustrated with a calibration whose parameter ranges are taken from the empirical tokenization literature. Two implications follow. First, utility backing compresses the pecuniary return an asset must offer in equilibrium, so two tokens with identical illiquidity can command different cash returns purely because of their utility content. Second, when more useful tokens are also held longer and traded less, utility yield and illiquidity covary, and a regression of pecuniary returns on liquidity proxies understates the gross illiquidity premium. The remainder of the paper proceeds as follows. Section 2 reviews the relevant literature. Section 3 sets out the framework and the illustrative calibration. Section 4 presents the analytical results and the calibration. Section 5 discusses implications and limitations, and Section 6 concludes. 2. Literature Review 2.1. Illiquidity in asset pricing The standard equilibrium model prices an asset by the covariance of its return with a systematic factor (Sharpe, 1964; Lintner, 1965), later generalized to multiple priced factors (Fama & French, 1993). This baseline abstracts from the cost of trading. Market microstructure research showed that trading is costly because of adverse selection and inventory frictions, and that informed trading moves prices in proportion to order flow (Kyle, 1985). Amihud and Mendelson (1986) brought these costs into asset pricing: in equilibrium, gross returns are increasing and concave in the relative bid-ask spread because investors with longer horizons hold higher-cost assets, so the per-trade cost is amortized over the holding period. This clientele result implies that the return premium attributable to a fixed trading cost falls as the expected holding horizon lengthens. Subsequent work treated illiquidity not only as a level cost but as a source of systematic risk. Amihud (2002) constructed a widely used price-impact measure and documented that expected returns rise with illiquidity, while Datar et al. (1998) showed that turnover, an inverse proxy for liquidity, is negatively related to the cross-section of returns. Pástor and Stambaugh (2003) found that sensitivity to aggregate liquidity shocks is priced, and Acharya and Pedersen (2005) unified these strands in a liquidity-adjusted capital asset pricing model in which the required return depends on the expected level of illiquidity and on three liquidity betas in addition to the standard market beta. Brunnermeier and Pedersen (2009) linked market liquidity to funding constraints, showing how liquidity can evaporate during stress. These contributions establish that both the level and the risk of illiquidity carry compensation, which provides the baseline this paper extends. A related literature studies portfolio choice and pricing when assets cannot be traded continuously. Longstaff (2009) shows that the inability to rebalance distorts portfolio composition and asset prices, and Ang et al. (2014) demonstrate that uncertainty about the length of the non-trading interval, rather than its mere existence, is a primary determinant of the cost of illiquidity. These results are directly relevant to tokenized assets, whose secondary markets open and close unpredictably with the broader crypto cycle. 2.2. Pricing of digital assets and tokens Cryptocurrencies and tokens have been examined as an asset class. Corbet et al. (2019) review their relationship to conventional assets, and Liu and Tsyvinski (2021) show that cryptocurrency returns are not well explained by standard equity or macro factors but exhibit their own risk structure, with Liu et al. (2022) identifying market, size, and momentum factors specific to the asset class. Makarov and Schoar (2020) document substantial trading frictions and arbitrage gaps across crypto venues, underlining that these markets are far from frictionless. Cong et al. (2021) model token valuation explicitly through transactional demand rather than discounted cash flows, capturing the idea that a token can be valued for the use it enables on a platform; this is the closest antecedent to the utility yield used here. Research on NFTs is more recent. Nadini et al. (2021) map the NFT market and show that trading is concentrated, that traders specialize, and that sale history predicts price. Dowling (2022a, 2022b) finds that NFT prices co-move with cryptocurrencies and exhibit low but positive predictability, and Kräussl and Tugnetti (2024) survey the pricing-determinants literature and propose a framework for NFT price formation that distinguishes intrinsic from speculative components. Borri et al. (2022) examine the risk and return characteristics of NFTs and document high volatility and exposure to crypto-market risk. This body of work establishes the empirical regularities, especially thin and intermittent trading, that motivate treating illiquidity as central to token pricing. 2.3. Tokenization of real-world assets The application of tokens to real-world assets has grown around the promise of liquefying traditionally illiquid holdings. Baum (2021) sets out the case for real estate tokenization and the conditions under which secondary liquidity might develop, while Schär (2021) describes the decentralized finance infrastructure, including decentralized exchanges and on-chain asset management, on which tokenized claims trade. Aspris et al. (2021) show that decentralized exchanges list large numbers of illiquid tokens and that migration to a centralized venue is accompanied by a sharp increase in trading volume, evidence of market segmentation and thin on-chain liquidity. Direct evidence on tokenized real-world assets is concentrated in real estate. Kreppmeier et al. (2023) hand-collect data on a large set of United States real estate tokens and the underlying blockchain transactions, finding that tokenization broadens access but that investors remain under-diversified and that crypto-market conditions, including transaction costs, shape secondary activity. Swinkels (2023) documents fragmented ownership and only modest secondary turnover among tokenized properties, with somewhat higher activity for tokens listed on decentralized exchanges. Steininger (2023) analyzes the return-risk profile of real estate tokens and argues they constitute a distinct asset class. Together these studies indicate that tokenization changes the mechanics of ownership transfer without, so far, delivering the deep liquidity often assumed. The framework below takes this empirical picture as its starting point and asks how the joint presence of illiquidity and utility value should shape required returns. 3. Framework and Methodology The approach is theoretical. The framework augments an established equilibrium model with a single additional term and derives qualitative implications, which are then illustrated through calibration. No parameters are estimated; the calibration serves to make the mechanism concrete using ranges reported in the empirical literature reviewed above. Table 1 summarizes the notation. Table 1: Notation used in the framework. Symbol Definition E(rᵢ) Required (expected) gross pecuniary return on token i r_f Risk-free rate cᵢ Per-period relative illiquidity cost of token i (fraction of price) c_M Market-wide relative illiquidity cost sᵢ Effective round-trip transaction cost (spread, fees, slippage, gas) τᵢ Expected turnover: round-trips per period (inverse of holding horizon) φᵢ Utility (convenience) yield from holding token i, expressed as a return λ Market price of risk, E(r_M − c_M − r_f) β¹–β⁴ The four covariance (beta) terms of the liquidity-adjusted CAPM (see text) 3.1. The liquidity-adjusted baseline The starting point is the liquidity-adjusted capital asset pricing model of Acharya and Pedersen (2005). Let cᵢ denote the per-period relative illiquidity cost of token i and rᵢ its gross return, so the net return is rᵢ − cᵢ. In equilibrium the required gross return satisfies E(rᵢ) − r_f = E(cᵢ) + λ (β¹ᵢ + β²ᵢ − β³ᵢ − β⁴ᵢ), (1) where λ = E(r_M − c_M − r_f) is the market price of risk and the four betas are normalized covariances with the net market portfolio: β¹ᵢ captures the covariance of the token return with the market return, β²ᵢ the commonality of the token’s illiquidity with market illiquidity, β³ᵢ the sensitivity of the token return to market illiquidity, and β⁴ᵢ the sensitivity of the token’s illiquidity to the market return. The term E(cᵢ) is the expected illiquidity level borne by the holder, and the bracketed term is the systematic liquidity-risk premium. For an asset that trades on thin on-chain venues during a crypto downturn, β²ᵢ is high and β³ᵢ and β⁴ᵢ are strongly negative, so each channel raises the required return (Brunnermeier & Pedersen, 2009; Aspris et al., 2021). 3.2. An amortized illiquidity level premium Following Amihud and Mendelson (1986), the per-period illiquidity cost can be expressed through the round-trip transaction cost and the rate at which the position is turned over. Let sᵢ be the effective round-trip cost of trading the token, comprising the bid-ask spread, marketplace fees, automated-market-maker slippage, and on-chain gas, and let τᵢ be the expected number of round-trips per period, the inverse of the holding horizon. The expected per-period illiquidity cost is then approximated by E(cᵢ) ≈ sᵢ τᵢ. (2) This expression captures the amortization result directly: a fixed trading cost contributes more to the required return the more frequently the asset is traded, and less for long-horizon holders. Turnover is the same liquidity dimension used empirically by Datar et al. (1998), which makes sᵢτᵢ a tractable empirical counterpart. For tokenized real-world assets, sᵢ is large relative to listed securities and τᵢ is low, consistent with the once-a-year turnover and material transaction costs documented by Kreppmeier et al. (2023) and Swinkels (2023). 3.3. Adding a utility yield Utility-backed tokens deliver a flow of benefits to the holder that is consumed rather than realized through sale. Examples include access and membership rights, governance and voting power, in-platform functionality, royalty and staking entitlements, and the consumption value of verified provenance (Cong et al., 2021; Kräussl & Tugnetti, 2024). Let φᵢ ≥ 0 denote this utility yield, expressed as a per-period return-equivalent. Because the holder receives φᵢ directly, the pecuniary return required to hold the token in equilibrium is reduced by exactly that amount, in the same way that a convenience yield lowers the required financial return on a commodity. Augmenting equation (1) gives E(rᵢ) = r_f + sᵢ τᵢ − φᵢ + λ (β¹ᵢ + β²ᵢ − β³ᵢ − β⁴ᵢ). (3) Equation (3) decomposes the required pecuniary return into the risk-free rate, the amortized illiquidity level premium sᵢτᵢ, the utility-yield offset −φᵢ, and the systematic liquidity-risk premium. The gross illiquidity premium, defined as the total compensation for the level and risk of illiquidity, is sᵢτᵢ + λ(β²ᵢ − β³ᵢ − β⁴ᵢ). The utility yield does not appear in the gross illiquidity premium; it enters only as a wedge between that premium and the pecuniary return investors observe. 3.4. Calibration design The calibration is illustrative and is not an estimate. Parameter ranges are chosen to span values suggested by the cited literature. The risk-free rate is set at 4%. The systematic liquidity-risk premium is set at 5% for a liquid listed security and 6% for a tokenized asset, reflecting the higher liquidity betas implied by on-chain market segmentation (Aspris et al., 2021; Makarov & Schoar, 2020). The effective round-trip cost sᵢ is set near zero for the liquid security and at 8% for tokenized assets, within the range implied by spreads, fees, slippage, and gas on thin venues; turnover τᵢ is set to one round-trip per year, consistent with the holding behavior reported for real estate tokens (Kreppmeier et al., 2023; Swinkels, 2023). The utility yield φᵢ is varied from 0% to 6% to trace its effect. Figure 1 plots equation (3) as a function of sᵢ for three values of φᵢ, and Table 2 and Figure 2 report the full decomposition for three asset profiles. 4. Results The results are analytical implications of equation (3) together with an illustrative calibration. They are stated as qualitative propositions; the numbers attached to them are demonstrations of the mechanism, not empirical magnitudes. 4.1. Utility backing compresses pecuniary returns Equation (3) is strictly decreasing in the utility yield. Holding illiquidity and systematic risk fixed, a higher φᵢ lowers the pecuniary return the token must offer in equilibrium, one for one. Two tokens with identical transaction costs, turnover, and liquidity betas can therefore trade at different cash returns solely because one carries more utility value. Figure 1 shows this as a downward parallel shift of the required-return schedule: at any level of transaction cost, raising the utility yield from 0% to 6% lowers the required pecuniary return by six percentage points. This is the sense in which utility backing can make a token appear to price illiquidity less aggressively than it does. Figure 1. Required pecuniary return as a function of the effective round-trip transaction cost, for three levels of the utility yield (turnover fixed at one round-trip per year; systematic liquidity-risk premium fixed at 3%). Values are illustrative. 4.2. The wedge between gross and observed illiquidity premia Rearranging equation (3) isolates the gross illiquidity premium: sᵢ τᵢ + λ (β²ᵢ − β³ᵢ − β⁴ᵢ) = E(rᵢ) − r_f − λ β¹ᵢ + φᵢ. (4) The observed pecuniary illiquidity premium, the left-hand side computed from prices alone, falls short of the gross premium by φᵢ. An empirical exercise that proxies the illiquidity premium with realized pecuniary returns therefore understates the true compensation for illiquidity whenever utility yield is positive and unobserved. The bias is not constant across assets. If more useful tokens are also held for use rather than trade, so that φᵢ covaries positively with sᵢ and inversely with τᵢ, then the omitted utility term is correlated with the liquidity proxies, and a cross-sectional regression of pecuniary returns on those proxies is biased toward zero or, in the limit, toward a counterintuitive negative coefficient. This provides a theoretical account of why some tokenized assets with severe trading frictions nonetheless exhibit modest pecuniary returns. 4.3. Illustrative decomposition Table 2 and Figure 2 apply equation (3) to three stylized profiles: a liquid listed equity, an illiquid tokenized real-world asset with no utility flow, and a utility-backed NFT with the same illiquidity but a utility yield of five percentage points. The decomposition makes three points concrete. The illiquidity level premium dominates the difference between the liquid security and the tokenized assets, contributing the larger part of the gap. The two tokenized assets carry the same gross illiquidity premium of eight percentage points, yet the utility-backed NFT requires a pecuniary return five percentage points lower because part of its value is consumed rather than realized. An observer comparing only cash returns would conclude, incorrectly, that the utility-backed token is less exposed to illiquidity. Table 2: Illustrative decomposition of the required pecuniary return for three asset profiles. Values are illustrative and are not empirical estimates. Component Liquid equity Illiquid RWA token Utility-backed NFT Symbol Risk-free rate 4.0% 4.0% 4.0% r_f Systematic risk premium 5.0% 6.0% 6.0% λ(·) Illiquidity level premium 0.3% 8.0% 8.0% sᵢτᵢ Utility-yield offset 0.0% 0.0% −5.0% −φᵢ Net required pecuniary return 9.3% 18.0% 13.0% E(rᵢ) Gross illiquidity premium 0.3% 8.0% 8.0% sᵢτᵢ Figure 2. Decomposition of the required pecuniary return for three asset profiles. Components stack upward; the utility yield enters as a downward offset, and the diamond marks the net required pecuniary return. Values are illustrative. 5. Discussion The framework reframes a claim that recurs in the tokenization literature. The argument that tokenization reduces the illiquidity premium (Baum, 2021) conflates two distinct effects. Tokenization can lower the round-trip cost sᵢ by automating settlement and removing intermediaries, and it can in principle raise turnover τᵢ by enabling fractional secondary trading. Both would reduce the amortized illiquidity level premium. The evidence so far suggests that these gains are limited: on-chain venues remain thin and segmented, costs include slippage and gas, and turnover is low (Aspris et al., 2021; Kreppmeier et al., 2023; Swinkels, 2023). At the same time, the utility yield φᵢ lowers the observed pecuniary return through a different channel that has nothing to do with liquidity. Conflating the two leads to an overstatement of how far tokenization has liquefied the underlying asset. The analysis also speaks to measurement. Because the utility yield is unobserved and plausibly correlated with illiquidity, naive estimates of the illiquidity premium from token prices are biased. Identifying the gross premium requires either an independent measure of utility value or a research design that holds utility content fixed while varying liquidity, for example by comparing the same token across venues with different depth, or by exploiting events that change tradability without changing the underlying benefit. The intermittent, regime-dependent nature of on-chain liquidity, in which markets are deep in normal times and shallow under stress, mirrors the uncertain non-trading intervals analyzed by Ang et al. (2014) and the funding-liquidity spirals of Brunnermeier and Pedersen (2009), and suggests that the liquidity betas in equation (1) are themselves state-dependent. For valuation and disclosure, the decomposition implies that the price of a utility-backed token embeds a consumption component that conventional discounted-cash-flow analysis omits. Treating such a token purely as a financial claim understates its value to a holder who uses it and overstates the comparability of its cash return to that of a passive security. The framework connects to models in which token value derives from use rather than from cash flows (Cong et al., 2021) and to the observation that NFT and crypto prices contain both fundamental and speculative components (Dowling, 2022a, 2022b; Liu & Tsyvinski, 2021; Liu et al., 2022; Kräussl & Tugnetti, 2024). Several limitations bound these conclusions. The framework is conceptual. It adds a single reduced-form term to an existing equilibrium model and does not derive the utility yield from primitives such as preferences over access or network participation; a fuller treatment would endogenize φᵢ, as in platform-adoption models of token value (Cong et al., 2021; Cong & He, 2019). The calibration is illustrative: the parameter values demonstrate the mechanism and are anchored to ranges in the literature, but they are not estimated, and the figures should not be read as predictions of return levels for any asset. The treatment of illiquidity through the product sᵢτᵢ is a linear approximation to the concave relationship derived by Amihud and Mendelson (1986) and is most accurate for moderate costs. Finally, on-chain markets raise frictions, such as wash trading, custody and smart-contract risk, and regulatory uncertainty, that the framework does not model and that may interact with both the illiquidity and utility terms (Nadini et al., 2021; Schär, 2021; Yermack, 2017; Corbet et al., 2019). The empirical evidence remains concentrated in real estate tokens and a small number of NFT markets (Kreppmeier et al., 2023; Steininger, 2023; Borri et al., 2022), so the external validity of the calibration is correspondingly narrow. These limitations also define a research agenda. The central prediction, that pecuniary returns understate the gross illiquidity premium by the magnitude of the utility yield, is testable wherever utility content can be measured or held fixed. Cross-venue comparisons, listing and delisting events, and tokens that strip or bundle utility rights all offer potential identification. Estimating the state dependence of the liquidity betas across crypto-market regimes would sharpen the systematic-risk component. 6. Conclusion Tokenization has been promoted as a technology for liquefying illiquid assets, but its first markets are thin and many of its instruments are valued for use as well as for cash flows. This paper has argued that pricing such assets requires a model that holds both features at once. Augmenting the liquidity-adjusted capital asset pricing model with a utility yield produces a decomposition of the required pecuniary return into a risk-free rate, an amortized illiquidity level premium that scales with transaction costs and turnover, a systematic liquidity-risk premium, and a utility-yield offset. The framework clarifies that utility backing compresses observed cash returns without reducing the underlying illiquidity premium, and that estimates based on pecuniary returns are biased downward for the gross premium when utility and illiquidity covary. These are conceptual results illustrated by calibration rather than estimated magnitudes, but they yield concrete, testable predictions and caution against reading low cash returns on utility-backed tokens as evidence that tokenization has eliminated the illiquidity premium. As secondary markets and data mature, the framework offers a structure within which the distinct contributions of liquidity and utility to token prices can be separately identified. References Acharya, V. V., & Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of Financial Economics, 77(2), 375–410. https://doi.org/10.1016/j.jfineco.2004.06.007 Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1), 31–56. https://doi.org/10.1016/S1386-4181(01)00024-6 Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), 223–249. https://doi.org/10.1016/0304-405X(86)90065-6 Ang, A., Papanikolaou, D., & Westerfield, M. M. (2014). Portfolio choice with illiquid assets. Management Science, 60(11), 2737–2761. https://doi.org/10.1287/mnsc.2014.1986 Aspris, A., Foley, S., Svec, J., & Wang, L. (2021). Decentralized exchanges: The “wild west” of cryptocurrency trading. International Review of Financial Analysis, 77, 101845. https://doi.org/10.1016/j.irfa.2021.101845 Baum, A. (2021). Tokenization—The future of real estate investment? The Journal of Portfolio Management, 47(10), 41–61. https://doi.org/10.3905/jpm.2021.1.260 Borri, N., Liu, Y., & Tsyvinski, A. (2022). The economics of non-fungible tokens (SSRN Working Paper No. 4052045). Social Science Research Network. https://doi.org/10.2139/ssrn.4052045 Brunnermeier, M. K., & Pedersen, L. H. (2009). Market liquidity and funding liquidity. The Review of Financial Studies, 22(6), 2201–2238. https://doi.org/10.1093/rfs/hhn098 Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. The Review of Financial Studies, 32(5), 1754–1797. https://doi.org/10.1093/rfs/hhz007 Cong, L. W., Li, Y., & Wang, N. (2021). Tokenomics: Dynamic adoption and valuation. The Review of Financial Studies, 34(3), 1105–1155. https://doi.org/10.1093/rfs/hhaa089 Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003 Datar, V. T., Naik, N. Y., & Radcliffe, R. (1998). Liquidity and stock returns: An alternative test. Journal of Financial Markets, 1(2), 203–219. https://doi.org/10.1016/S1386-4181(97)00004-9 Dowling, M. (2022a). Fertile LAND: Pricing non-fungible tokens. Finance Research Letters, 44, 102096. https://doi.org/10.1016/j.frl.2021.102096 Dowling, M. (2022b). Is non-fungible token pricing driven by cryptocurrencies? Finance Research Letters, 44, 102097. https://doi.org/10.1016/j.frl.2021.102097 Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56. https://doi.org/10.1016/0304-405X(93)90023-5 Kräussl, R., & Tugnetti, A. (2024). Non-fungible tokens (NFTs): A review of pricing determinants, applications and opportunities. Journal of Economic Surveys, 38(2), 555–574. https://doi.org/10.1111/joes.12597 Kreppmeier, J., Laschinger, R., Steininger, B. I., & Dorfleitner, G. (2023). Real estate security token offerings and the secondary market: Driven by crypto hype or fundamentals? Journal of Banking & Finance, 154, 106940. https://doi.org/10.1016/j.jbankfin.2023.106940 Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315–1335. https://doi.org/10.2307/1913210 Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13–37. https://doi.org/10.2307/1924119 Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727. https://doi.org/10.1093/rfs/hhaa113 Liu, Y., Tsyvinski, A., & Wu, X. (2022). Common risk factors in cryptocurrency. The Journal of Finance, 77(2), 1133–1177. https://doi.org/10.1111/jofi.13119 Longstaff, F. A. (2009). Portfolio claustrophobia: Asset pricing in markets with illiquid assets. American Economic Review, 99(4), 1119–1144. https://doi.org/10.1257/aer.99.4.1119 Makarov, I., & Schoar, A. (2020). Trading and arbitrage in cryptocurrency markets. Journal of Financial Economics, 135(2), 293–319. https://doi.org/10.1016/j.jfineco.2019.07.001 Nadini, M., Alessandretti, L., Di Giacinto, F., Martino, M., Aiello, L. M., & Baronchelli, A. (2021). Mapping the NFT revolution: Market trends, trade networks, and visual features. Scientific Reports, 11, 20902. https://doi.org/10.1038/s41598-021-00053-8 Pástor, Ľ., & Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political Economy, 111(3), 642–685. https://doi.org/10.1086/374184 Schär, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 103(2), 153–174. https://doi.org/10.20955/r.103.153-74 Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x Steininger, B. I. (2023). Return–risk analysis of real estate tokens: An asset class of its own. The Journal of Portfolio Management, 49(10), 83–102. https://doi.org/10.3905/jpm.2023.1.540 Swinkels, L. (2023). Empirical evidence on the ownership and liquidity of real estate tokens. Financial Innovation, 9, 45. https://doi.org/10.1186/s40854-022-00427-5 Yermack, D. (2017). Corporate governance and blockchains. Review of Finance, 21(1), 7–31. https://doi.org/10.1093/rof/rfw074 #TokenizedAssets #RWA #NFTs #IlliquidityPremium #AssetPricing #CAPM #DeFi #Blockchain #Tokenization #ConvenienceYield #DigitalAssets #FinTech #MarketLiquidity #RealWorldAssets #UtilityTokens #FinanceResearch #QuantFinance #CryptoFinance

Authors: Sarah Meier Affiliation: International Academy in Switzerland ORCID ID: 0009-0008-2615-0767 Submitted 25 February 2026; Revised 02 April 2026; Accepted 28 May 2026; Available online 18 June 2026; Version of Record 18 June 2026. https://doi.org/10.65326/u7y566830 Volume 3, December 2026, (10026) Abstract The Strait of Hormuz carries roughly one fifth of global petroleum liquids consumption and lacks a maritime substitute, which makes it the most concentrated single point of failure in the global energy trade. The escalation of armed conflict around the Strait in 2025 and 2026 has renewed the question of how an interruption of transit would affect global supply chains. Existing scholarship treats the relevant mechanisms separately: the maritime chokepoint literature quantifies exposed trade and rerouting, the oil price shock literature distinguishes supply-driven from demand-driven shocks, and the supply chain resilience literature formalises how disruption propagates through networks. These mechanisms are activated simultaneously by a closure of the Strait, yet they have rarely been integrated for a single event, and the role of route substitutability as a determinant of disruption severity has not been made explicit. This study develops an integrated transmission framework and a set of six testable propositions that position substitutability as a moderator of chokepoint severity, and it applies the framework through a transparent, assumption-explicit scenario synthesis anchored to published parameters and to documented analogues such as the 2021 Suez Canal blockage and the 2023–2025 Red Sea crisis. The analysis indicates that the defining feature of the Strait is the near-absence of a short-term substitute, which disables the rerouting strategies that contained earlier disruptions, and that the duration of an interruption, rather than its onset, governs whether the disruption is absorbed or becomes transformative. The contribution is conceptual, and the propositions are offered for testing. Keywords: Strait of Hormuz; maritime chokepoints; supply chain resilience; route substitutability; oil price shocks; ripple effect 1. Introduction The Strait of Hormuz connects the Persian Gulf to the Gulf of Oman and the wider Indian Ocean and is the conduit through which most Gulf hydrocarbon exports reach world markets. During 2024 and the first quarter of 2025 an average of roughly twenty million barrels per day of crude oil, condensate, and petroleum products transited the Strait, a volume equivalent to about one fifth of global petroleum liquids consumption and more than one quarter of seaborne oil trade (Congressional Research Service [CRS], 2026; U.S. Energy Information Administration [EIA], 2025). The International Energy Agency estimates that close to a quarter of seaborne oil crosses the Strait, with the majority destined for Asia and with China and India together receiving a substantial share (International Energy Agency [IEA], 2025). A comparable concentration applies to liquefied natural gas, because Qatari exports that leave through the Strait account for a large fraction of the seaborne gas market (CRS, 2026). No other waterway carries so high a share of a commodity on which industrial production, transport, and electricity generation depend so directly. This concentration became an operational concern rather than a theoretical one during the conflict between Iran on one side and Israel and the United States on the other. In June 2025, following strikes on Iranian military and nuclear infrastructure, the Iranian parliament endorsed a proposal to close the Strait, although transit was not in fact halted at that time (CRS, 2026). The situation escalated in early 2026, when official sources report that, beginning in March 2026, Iranian forces declared the Strait closed and conducted attacks on vessels attempting to transit it (CRS, 2026; Gross & Beane, 2026). These developments transformed the closure of the Strait from a scenario used in risk assessment into a partially realised disruption and sharpened the question this study addresses. The economic consequences of a closure are widely discussed in policy commentary, yet the mechanisms by which such an event reaches the firm and the factory are not well integrated in the scholarly literature, which has developed in three largely separate streams. Research on maritime chokepoints has quantified the trade exposed to disruption and the rerouting that follows (Pratson, 2023; Verschuur et al., 2025; Verschuur, Koks, & Hall, 2022). Research on oil price shocks has shown that the macroeconomic effects of a price increase depend on whether it is driven by supply or by demand (Kilian, 2008, 2009, 2014). Research on supply chain resilience has formalised how a localised disruption propagates through interconnected networks and what capabilities allow firms to absorb it (Christopher & Peck, 2004; Dolgui, Ivanov, & Sokolov, 2018; Hosseini, Ivanov, & Dolgui, 2019). A closure of the Strait of Hormuz is distinctive precisely because it activates all three mechanisms at once: it is simultaneously an energy supply shock, a maritime logistics shock, and a trigger for cascading disruption across production networks. Beyond their separation, these literatures share an unexamined assumption that is decisive for the Strait. Studies of recent disruptions, including the 2021 Suez Canal blockage and the 2023 to 2025 Red Sea crisis, describe events in which rerouting preserved access at higher cost (Notteboom, Haralambides, & Cullinane, 2024; Tran, Haralambides, Notteboom, & Cullinane, 2025). The severity of a chokepoint disruption, however, depends not only on the volume of trade it carries but on whether that trade can be re-routed, and this property of route substitutability has not been theorised explicitly as a determinant of severity. The Strait of Hormuz is the limiting case in which substitutability is effectively absent, and reasoning from substitutable analogues is therefore liable to understate its consequences. This study addresses both problems. It asks three questions. First, through what channels would a war that interrupts transit through the Strait of Hormuz transmit to global supply chains? Second, what distinguishes such an interruption from the substitutable chokepoint disruptions already studied? Third, what determines whether the disruption is absorbed or becomes transformative? The study makes three contributions. It develops an integrated transmission framework that unifies the maritime, energy, and supply chain literatures for a single event. It advances a set of six testable propositions that position route substitutability as a moderator of chokepoint severity and specify the conditions under which a closure exceeds the buffering capacity of exposed networks. And it applies the framework through a transparent, assumption-explicit scenario synthesis that integrates published parameters and documented analogues, from which managerial and policy implications are derived. The analysis is conceptual and theory-elaborating rather than empirical. A sustained, war-driven closure of the Strait has not produced a complete record of supply chain outcomes, so the study reasons from established theory, from institutional data on flows, and from analogous events, and it states the limits of that approach throughout. The remainder of the article proceeds as follows. Section 2 reviews the theoretical background. Section 3 presents the framework and propositions. Section 4 describes the research design, including the structured review protocol and the scenario method. Section 5 develops the results. Section 6 discusses theoretical, managerial, and policy implications and limitations, and Section 7 concludes. 2. Theoretical Background 2.1 Maritime chokepoints and the vulnerability of trade Maritime transport carries the majority of world trade by volume, and a small number of narrow passages concentrate a disproportionate share of that traffic. The United Nations Conference on Trade and Development has documented growing strain on critical passages, including the Panama Canal, the Suez Canal and the Red Sea, and the Black Sea, as a combined result of geopolitical tension, conflict, and climate stress, and has argued that this strain threatens the functioning of maritime supply chains and, through them, energy and food security (United Nations Conference on Trade and Development [UNCTAD], 2024). The structural reason is that chokepoints couple high traffic density with geographic non-substitutability: when one is impeded, ships are delayed or rerouted, ports become congested as delayed and scheduled cargoes arrive together, and the disturbance reverberates through downstream supply chains for an extended period. Three contributions are central here. Verschuur, Koks, and Hall (2022) quantified the criticality of the world's most important ports by linking maritime flows to a global supply-chain database, finding that half of global trade in value terms is maritime and that some landlocked and island economies depend on specific ports outside their jurisdiction, an early demonstration that maritime criticality is unevenly distributed. Pratson (2023) combined geographic data on shipping lanes with bilateral trade data to estimate how the closure of any one of eleven major chokepoints would redistribute flows across the others, showing that closures generate alternate-route linkages among chokepoints and knock-on effects at seaports that persist after a blockage is physically cleared, and underscoring the particular importance of the Strait of Hormuz to the economies that depend on it. Verschuur, Lumma, and Hall (2025) modelled the systemic risk created by chokepoint disruptions, estimating the value of trade exposed to such disruptions at roughly one hundred and ninety-two billion United States dollars annually with expected economic losses near ten and seven tenths billion dollars, and, importantly, distinguishing disruptions that require only modest detours, which can be buffered, from disruptions that effectively cut countries off from trade, a category in which they placed the Strait of Hormuz. 2.2 Supply chain resilience, severity, and capabilities The resilience literature supplies the vocabulary for tracing how a chokepoint disruption becomes a production problem. Christopher and Peck (2004) defined resilience from a systems perspective as the ability of a network to return to its original state, or to move to a more desirable one, after a disturbance, and argued that resilience must be engineered in advance through redundancy, flexibility, collaboration, and a culture of risk management. Sheffi and Rice (2005) framed the central trade-off as one between redundancy, which holds slack such as inventory and backup capacity, and flexibility, which builds the capacity to reconfigure, observing that flexibility is generally the more durable source of resilience. Pettit, Fiksel, and Croxton (2010) formalised resilience as the balance between a network's vulnerabilities and its capabilities, which explains why two firms exposed to the same external shock can experience very different outcomes. Two further contributions matter for the present argument. Craighead, Blackhurst, Rungtusanatham, and Handfield (2007) asked why one supply chain disruption is more severe than another and concluded, through a set of propositions, that severity rises with the density and criticality of the affected nodes and falls with the recovery and warning capabilities available to mitigate it. Their emphasis on node criticality is directly relevant to a chokepoint, which is a maximally critical node in the maritime network. Wieland and Durach (2021) drew a distinction, imported from ecology, between engineering resilience, understood as the ability to bounce back to a prior state, and social-ecological resilience, understood as the capacity to persist, adapt, or transform, and argued that supply chain scholarship has relied too heavily on the engineering view. This distinction becomes important when a disruption is sufficiently severe and prolonged that returning to the prior state is not possible. Hosseini, Ivanov, and Dolgui (2019) synthesised the quantitative methods available for analysing these dynamics around the concept of resilience capacity, distinguishing absorptive, adaptive, and restorative capacities. 2.3 Disruption propagation, the ripple effect, and viability How a disruption travels through a network is the subject of the ripple-effect literature. Dolgui, Ivanov, and Sokolov (2018) characterised the ripple effect as the propagation of a disruption from its origin through connected parts of a supply chain, in contrast to the operational variability captured by the bullwhip effect. Li, Chen, Collignon, and Ivanov (2021) modelled this propagation explicitly, showing that a local disruption can travel forward along material flows and backward toward suppliers and that a firm's structural position shapes its vulnerability. At the level of whole economies, Inoue and Todo (2019) demonstrated empirically that shocks to individual firms propagate through supply-chain links to produce aggregate effects far larger than the initial disturbance, which implies that the supply chain cost of a disruption is not captured by the value of the directly interrupted flow. The COVID-19 pandemic prompted an extension of resilience thinking toward viability. Ivanov (2020) used simulation to show that disruptions characterised by long duration, propagation, and high uncertainty behave differently from short, localised shocks and require different management. Ivanov and Dolgui (2020) introduced the concept of intertwined supply networks, in which interconnected supply chains jointly provide essential services to society, and argued that resistance to extraordinary disruptions must be assessed at the scale of survivability rather than at the level of a single chain. The same authors set out the operational research methods suited to coping with the ripple effect under such conditions, emphasising the management of after-shock dynamics during recovery (Ivanov & Dolgui, 2021). These ideas are directly relevant to a Strait of Hormuz closure, which, like a pandemic, is potentially long in duration and broad in propagation. 2.4 Oil price shocks and geopolitical risk Because a closure of the Strait is first of all an interruption of oil and gas flows, the economics of energy price shocks is essential. Kilian (2008) reviewed the channels through which energy price shocks affect the macroeconomy and cautioned that the relationship between oil prices and output is neither simple nor stable. Kilian (2009) provided the most influential refinement of this view, showing that the source of a price increase matters: a rise driven by a disruption to supply has different and generally more adverse dynamic effects than one driven by strong global demand. Kilian (2014) consolidated these findings, emphasising that the real price of oil is endogenous to economic fundamentals and that the consequences of a shock cannot be read from the price change alone. A war-induced closure of the Strait is a paradigmatic supply-driven shock, which implies that its consequences should be assessed using the supply-shock rather than the demand-shock template. The geopolitical-risk literature situates a chokepoint conflict within a category of events whose effects operate partly through uncertainty. Caldara and Iacoviello (2022) constructed a news-based index of geopolitical risk and showed that elevated risk foreshadows lower investment and employment and is associated with larger downside risks, with adverse effects driven by both the threat and the realisation of adverse events. This implies that the anticipation of a closure, and the persistence of risk after a partial reopening, can themselves impose costs on supply chains, independently of the physical interruption of flows. 2.5 Research gap These literatures supply complementary pieces but have rarely been combined for a single event that activates all of them, and none isolates route substitutability as a determinant of severity. The chokepoint literature measures exposure and rerouting but largely studies cases in which rerouting was feasible; the resilience literature specifies capabilities but is dominated by an engineering view oriented to bouncing back; and the oil shock literature characterises the macroeconomic response without tracing it into production networks. The next section integrates these strands into a single framework and derives propositions that make substitutability, the supply-driven character of the shock, the insurance channel, duration, and asymmetric exposure explicit. 3. Conceptual Framework and Propositions Figure 1 presents the transmission framework. A closure of the Strait produces two primary shocks, an energy supply shock and a maritime disruption, which propagate through four cost channels, namely energy and gas price escalation, war-risk insurance premiums, freight and rerouting costs, and lengthened lead times and inventory costs, and through the ripple effect across intertwined networks, into a systemic outcome of sectoral output losses, inflationary pressure, and unevenly distributed regional exposure. A feedback loop captures the persistence of geopolitical risk, which can degrade the investment in resilience that would otherwise buffer future recurrence. Figure 1. Conceptual framework for the transmission of a Strait of Hormuz closure to global supply chains. The framework yields six propositions. The first concerns the compound nature of the shock. Unlike a canal grounding, which is principally a logistics event, or a producer embargo, which is principally an energy event, a closure of the Strait simultaneously curtails energy supply and halts maritime transit, so that the price, logistics, and propagation channels are activated together rather than in sequence (Dolgui et al., 2018; Kilian, 2009; Pratson, 2023). P1. A closure of the Strait of Hormuz transmits to global supply chains as a compound shock that activates energy-price, maritime-cost, and propagation channels simultaneously, rather than as a single-channel disruption. The second proposition is the study's central theoretical claim. The severity of a chokepoint disruption depends not only on the volume of trade it carries but on whether that trade can be re-routed. Where a substitute route exists, as around the Cape of Good Hope for the Suez and Bab el-Mandeb passages, disruption raises cost and time but preserves access and can be buffered with inventory and contingency routing; where no substitute exists, disruption severs access and exceeds the buffering capacity of exposed networks (Christopher & Peck, 2004; Craighead et al., 2007; Pratson, 2023; Verschuur et al., 2025). Route substitutability therefore moderates the relationship between a chokepoint's trade volume and the severity of its disruption. P2. The severity of a chokepoint disruption for global supply chains increases as route substitutability decreases; because the Strait of Hormuz has no near-term maritime substitute, its closure produces more severe and less bufferable disruption than closures of substitutable chokepoints carrying comparable volumes. The third proposition follows from the energy economics. Because the shock curtails supply rather than reflecting strong demand, it follows the more adverse macroeconomic template and transmits to output and inflation through reinforcing channels (Kilian, 2008, 2009, 2014). P3. Because a closure is a supply-driven energy shock, its macroeconomic and supply chain transmission is more adverse than that of a demand-driven price increase of comparable magnitude. The fourth proposition concerns amplification. The repricing and withdrawal of war-risk insurance can suppress flows before, and well beyond, any direct interdiction of vessels, and the anticipation of risk acts on investment and activity independently of physical flows (Caldara & Iacoviello, 2022; CRS, 2026; Gross & Beane, 2026; Notteboom et al., 2024). P4. The war-risk insurance and risk-pricing channel amplifies a closure and can precede physical interdiction, so that observed reductions in throughput exceed the volume directly interrupted. The fifth proposition specifies the role of duration, illustrated in Figure 2. A short interruption resembles the Suez analogue and is largely absorbed through inventory and other engineering-resilience buffers; a sustained interruption exhausts inventories and forces substitution of supply sources or production, shifting the relevant concept from recovery to viability and from engineering to social-ecological resilience (Ivanov, 2020; Ivanov & Dolgui, 2020; Sheffi & Rice, 2005; Wieland & Durach, 2021). P5. Disruption severity is governed by duration: short closures are absorbed through engineering-resilience buffers, whereas sustained closures exceed those buffers and require transformation through substitution, raising survivability rather than recovery as the relevant concept. The sixth proposition addresses distribution. The physical supply risk is concentrated among importers dependent on the Strait, predominantly in Asia, while the price effect is transmitted globally because oil is fungible, so that exposure is asymmetric and a coordinated response is complicated by the divergent problems different regions face (Gross & Beane, 2026; IEA, 2025; Verschuur et al., 2025; Verschuur, Koks, & Hall, 2022). P6. Exposure to a closure is asymmetric: physical supply risk is regionally concentrated among dependent importers while price effects are globally shared, which complicates coordinated mitigation. Figure 2. Stylised relationship between the duration of a closure and relative supply chain severity, distinguishing the inventory-buffering region from the substitution-and-transformation region, and contrasting full closure with partial restoration of flows. The curve is a conceptual representation synthesising the cited evidence rather than estimated data; the single annotated point is anchored to the published scenario of Kilian et al. (2026). 4. Methodology The study adopts a theory-elaboration design appropriate to a phenomenon that cannot yet be studied as a completed empirical event. Theory elaboration combines existing conceptual material with case evidence to develop propositions, and is well suited to a setting in which established theories apply but have not been integrated for the case at hand. Three methods are combined: a structured integrative review of the relevant literatures, a comparative analogue analysis of documented chokepoint disruptions, and a transparent scenario synthesis that applies the framework using published parameters. 4.1 Structured review protocol The review followed a structured protocol to support transparency and reproducibility. Sources were sought across two tiers. Tier one comprised peer-reviewed articles retrieved from major bibliographic and publisher databases using combinations of the terms maritime chokepoint, Strait of Hormuz, supply chain resilience, ripple effect, disruption propagation, oil price shock, and geopolitical risk. Tier two comprised authoritative institutional sources used for current data and event documentation, including the energy and maritime agencies and established research institutions. Inclusion required direct relevance to one of the framework's elements, namely chokepoint exposure, resilience and propagation, energy-price transmission, or geopolitical risk, and, for empirical claims, traceability to a primary source. Preference was given to recent contributions in established journals and to the original source of each datum. Sources promoting a partisan or non-scholarly agenda were excluded, as were predatory or non-peer-reviewed outlets for theoretical claims. The resulting corpus integrates a focused set of peer-reviewed studies with institutional data; it is not presented as an exhaustive systematic review, and the synthesis is interpretive. 4.2 Comparative analogue analysis Because a sustained, war-driven closure of the Strait has not produced a complete record of supply chain outcomes, documented disruptions at other chokepoints were used as analogues to reason about plausible magnitude and persistence. Two were selected for their contrast in substitutability. The 2021 grounding of the Ever Given in the Suez Canal is an analogue for a short, total blockage with an available substitute route; Tran et al. (2025) used vessel voyage data to estimate that the six-day blockage imposed losses of approximately eighty-nine million United States dollars on a single carrier whose fleet accounted for about one third of the affected ships, with sixty-nine of its vessels rerouted around the Cape of Good Hope or delayed. The 2023 to 2025 Red Sea crisis is an analogue for a prolonged disruption managed through rerouting; Notteboom et al. (2024) analysed how attacks in the Bab el-Mandeb Strait drove a large-scale diversion around the Cape of Good Hope, lengthening transit, raising fuel and surcharge costs, and pushing war-risk insurance premiums toward roughly three quarters of one percent to one percent of vessel value. The COVID-19 disruption supplies a third, more distant analogue for a long-duration, widely propagating shock (Ivanov, 2020). The decisive difference between these analogues and the Strait of Hormuz is that each permitted a routing response, so the analogues are read as lower bounds on the consequences of a comparable interruption at the Strait. 4.3 Scenario synthesis and its limits The framework is applied through a qualitative scenario synthesis that organises the channels of Figure 1 and assigns to each the best available evidence on magnitude. The synthesis is order-of-magnitude and assumption-explicit. It does not estimate new quantitative effects; numerical figures are drawn from cited institutional data and from published model-based scenarios, not from original computation, and where a figure represents a modelled scenario rather than an observed outcome this is stated. The duration dimension is represented in Figure 2 as a stylised relationship, with full-closure and partial-restoration cases distinguished and a single point anchored to a published scenario; the figure is conceptual and its vertical scale is illustrative rather than estimated. Two design limitations follow. First, the analogues are imperfect because they permitted rerouting that the Strait does not, which biases the synthesis toward conservatism. Second, behavioural responses such as strategic stockpiling, hoarding, and substitution toward alternative energy carriers are not modelled, although they would shape any real trajectory. These limitations mark the boundary between what can be inferred from existing knowledge and what requires the empirical record that a fully realised closure would generate. 5. Results 5.1 Strategic centrality and the substitutability gap The first and most consequential result is structural and supports Proposition 2. The Strait of Hormuz concentrates a larger share of a critical commodity than any other maritime passage, and, unlike the chokepoints in recent disruptions, it lacks a near-term substitute route capable of absorbing more than a fraction of its flows. Institutional assessments converge: the volume transiting the Strait represents roughly one fifth of global petroleum liquids consumption and more than a quarter of seaborne oil trade, and the pipeline capacity available to bypass the Strait can carry only a limited portion of normal throughput (CRS, 2026; EIA, 2025; IEA, 2025). This differs qualitatively from the Red Sea, where vessels were diverted around the Cape of Good Hope at the cost of longer voyages but without an absolute loss of access (Notteboom et al., 2024). The systemic-risk modelling of Verschuur et al. (2025) makes the implication explicit by separating chokepoints whose disruption requires only detours, which can be buffered, from those whose disruption severs trade, among which they place the Strait; Pratson (2023) reaches a compatible conclusion from an independent method. Table 1 summarises the contrast across the principal analogues, and Table 2 reports selected indicators of dependence on the Strait. Table 1. Comparative features of major maritime chokepoint disruptions. Event / chokepoint Year Nature of disruption Substitute route Documented supply chain effect Suez Canal (Ever Given) 2021 Six-day total blockage, then cleared Cape of Good Hope available Carrier losses of about USD 89 million for one fleet; rerouting and delay of 69 vessels (Tran et al., 2025) Bab el-Mandeb / Red Sea 2023–2025 Prolonged security threat; partial avoidance Cape of Good Hope used at scale Longer transit, higher fuel and surcharges, war-risk premiums near 0.75–1% of vessel value (Notteboom et al., 2024) Strait of Hormuz 2025–2026 Conflict-driven closure and attacks on shipping No comparable near-term maritime substitute Trade effectively severed for dependent economies; scenario estimates of large price and output effects (Verschuur et al., 2025; Kilian et al., 2026) Note. Entries are drawn from the cited sources; figures for the Strait of Hormuz combine reported events with model-based scenario estimates. 5.2 The energy price channel The most immediate channel is the price of oil and gas, supporting Proposition 3. A war-induced closure is a supply-driven energy shock, the category Kilian (2009, 2014) associates with the most adverse dynamics, and Kilian (2008) cautions that such shocks transmit through several reinforcing mechanisms. The potential scale is indicated by model-based scenario work: economists at the Federal Reserve Bank of Dallas estimated that a disruption persisting through the second quarter of 2026 would raise the average West Texas Intermediate price toward ninety-eight United States dollars per barrel and lower global real gross domestic product growth by roughly two and nine tenths percentage points on an annualised basis, while emphasising that reducing the shortfall of oil even partially would substantially dampen the impact (Kilian et al., 2026). These are scenario outputs of a structural model rather than observed outcomes. Two qualifications apply. Because oil is globally fungible, the price effect is not confined to importers of Gulf crude; even the United States, the largest producer, remains exposed because the disruption raises the global price faced by all buyers (Gross & Beane, 2026). And the gas dimension is distinct, because the concentration of Qatari liquefied natural gas passing through the Strait removes a large share of seaborne gas from the market for users that cannot readily switch fuels (CRS, 2026). 5.3 Maritime cost channels and insurance amplification Beyond the price of the cargo, a closure raises the cost and lengthens the time of moving goods, supporting Proposition 4. The analogues are informative. In the Suez case a six-day total blockage generated quantifiable carrier losses through extended voyages, waiting time, and the inventory cost of delayed cargo, even though the canal was cleared within days (Tran et al., 2025). In the Red Sea case a prolonged threat was managed by diversion around the Cape of Good Hope, which lengthened Asia to Europe voyages, raised fuel consumption and surcharges, and drove war-risk premiums sharply higher (Notteboom et al., 2024). Both illustrate the cost channels of Figure 1. For the Strait, the same channels operate but the routing response is constrained, because there is no comparable maritime detour for the landlocked Gulf exporters. Reporting on the 2026 episode indicates that the initial contraction in transit was driven substantially by the repricing and withdrawal of tanker insurance rather than by the physical interdiction of every vessel, a dynamic that can suppress flows well before, and well beyond, any direct attack (CRS, 2026; Gross & Beane, 2026). The insurance channel therefore functions as an amplifier, converting a localised security threat into a broad reduction in throughput. Table 2. Selected indicators of dependence on the Strait of Hormuz. Indicator Approximate magnitude Source Oil and petroleum products transiting daily (2024–2025) About 20 million barrels per day EIA (2025); CRS (2026) Share of global petroleum liquids consumption About one fifth EIA (2025) Share of seaborne / maritime oil trade Roughly one quarter or more CRS (2026); IEA (2025) Principal destination of flows Predominantly Asia (notably China and India) IEA (2025) Annual value of trade exposed to chokepoint disruption (global) About USD 192 billion Verschuur et al. (2025) Note. Values are approximate and are reported as stated in the cited sources. 5.4 Propagation, duration, and the threshold to transformation The cost and supply shocks propagate through production networks, and their severity depends on duration, supporting Propositions 1 and 5. A disruption at the Strait raises input costs and lengthens lead times for firms far from the Gulf, and these effects travel forward to customers and backward to suppliers according to network position (Dolgui et al., 2018; Li et al., 2021). Inoue and Todo (2019) showed that such propagation can amplify an initial shock into aggregate losses much larger than the direct disturbance. Duration determines whether the disruption is absorbed or transformative, as represented in Figure 2. Ivanov (2020) demonstrated that long-duration, widely propagating disruptions destabilise production and inventory dynamics in ways short shocks do not and that recovery carries after-shock risks. Applied to the Strait, a short closure resembles the Suez analogue and is largely absorbed through the redundancy and slack emphasised by Sheffi and Rice (2005), whereas a sustained closure exhausts inventories and forces substitution of suppliers or production. At that threshold the relevant concept shifts from recovery to viability and from engineering to social-ecological resilience, because returning to the prior configuration is no longer feasible and adaptation or transformation becomes necessary (Ivanov & Dolgui, 2020, 2021; Wieland & Durach, 2021). The breadth of affected commodities widens the set of firms that reach this threshold. 5.5 The breadth of affected commodities and sectors A closure is not solely an energy event. The Gulf is a major source of commodities beyond crude oil and gas, and their interruption spreads the disruption across sectors that do not obviously depend on the region. Drawing on trade data, the Atlantic Council (2026) documented that, before the 2026 disruption, the Gulf supplied a substantial share of several globally traded commodities, including a meaningful fraction of seaborne jet fuel and diesel, of ammonia demand, of helium production, of seaborne sulfur, and of aluminium. Ammonia and sulfur are inputs to fertiliser production, which links a maritime closure to agricultural supply chains and to food prices; helium is critical to specific manufacturing and medical applications; and refined-product flows bear directly on transport-fuel availability. These linkages illustrate why the ripple effect reaches sectors several steps removed from the energy market and reinforce the propagation mechanism of Proposition 1. 5.6 Asymmetric regional exposure The final result concerns distribution and supports Proposition 6. Dependence on the Strait varies sharply across economies. The IEA (2025) notes that most exports through the Strait are destined for Asia, with China and India together accounting for a large share, concentrating physical supply risk among Asian importers. Verschuur et al. (2025) likewise identify economies in the Middle East, Africa, and other import-dependent regions as the most exposed, and Verschuur, Koks, and Hall (2022) show more generally that some economies depend on specific maritime infrastructure outside their control. Gross and Beane (2026) emphasise that the price effect is nonetheless global, so that economies with little direct dependence still experience the inflationary consequences. The combined picture is one of globally shared price effects layered on regionally concentrated physical supply risk, a configuration that complicates coordinated policy responses. 6. Discussion 6.1 Theoretical implications The principal theoretical contribution is to position route substitutability as a moderator of chokepoint disruption severity, formalised in Proposition 2. The chokepoint literature has measured the trade exposed at each passage and the rerouting that follows (Pratson, 2023; Verschuur et al., 2025; Verschuur, Koks, & Hall, 2022), and the resilience literature has identified node criticality as a driver of severity (Craighead et al., 2007), but neither has made explicit that the marginal effect of trade volume on severity is conditioned by whether the trade can be re-routed. Recognising substitutability as a moderator clarifies why disruptions of comparable headline magnitude can have qualitatively different consequences and identifies the Strait of Hormuz as the limiting case in which the moderator approaches zero. The analysis also reconciles competing conceptions of resilience for the case of a non-substitutable chokepoint. The engineering view, oriented to bouncing back through redundancy and slack, adequately describes the absorption of a short closure, whereas the social-ecological view, oriented to persistence, adaptation, and transformation, becomes necessary for a sustained closure that renders the prior configuration unviable (Sheffi & Rice, 2005; Wieland & Durach, 2021). The duration threshold in Figure 2 marks the transition between these regimes and connects the resilience and viability literatures (Ivanov & Dolgui, 2020), suggesting that the appropriate resilience concept is itself a function of disruption duration rather than a fixed property of the network. 6.2 Managerial implications Three implications follow for firms. First, because the insurance and risk-pricing channel can suppress flows ahead of any physical interdiction (Caldara & Iacoviello, 2022; CRS, 2026; Gross & Beane, 2026), the monitoring of war-risk premiums and chartering behaviour provides an early indicator of disruption that precedes changes in physical throughput. Second, because severity at a non-substitutable chokepoint cannot be addressed by transport redundancy, the relevant resilience investments are redundancy in the form of strategic reserves and diversified sourcing and flexibility in the form of the capacity to substitute suppliers and production, consistent with the redundancy-versus-flexibility trade-off identified by Sheffi and Rice (2005). Third, because propagation is governed by network structure (Inoue & Todo, 2019; Li et al., 2021), firms should map their indirect exposure to Gulf-linked inputs, including the fertiliser and refined-product chains implied by the commodity breadth documented by the Atlantic Council (2026), rather than only their direct sourcing. 6.3 Policy implications For policymakers, the asymmetry of exposure implies differentiated responses. Import-dependent economies, concentrated in Asia, face a quantity problem that calls for strategic reserves, coordinated release, and demand-management measures, whereas distant economies face primarily a price and inflation problem (Gross & Beane, 2026; IEA, 2025). The scenario analysis offers a further implication with direct policy relevance: because even a partial restoration of flows substantially dampens the impact (Kilian et al., 2026), the operative objective need not be full reopening but the reduction of the shortfall, which reframes contingency planning around marginal restoration of supply rather than around complete avoidance of disruption. The feedback loop in Figure 1 adds a caution, since persistent geopolitical risk can deter the very investment in capacity and diversification that would build resilience (Caldara & Iacoviello, 2022). 6.4 Limitations Several limitations bound these conclusions. The study is conceptual and theory-elaborating, and the propositions are offered for testing rather than tested here. The analysis rests on analogues that are imperfect, most importantly because the chokepoints in the analogues permitted rerouting while the Strait does not, so the analogues are best read as lower bounds. The quantitative figures are drawn from institutional data and from model-based scenarios rather than from observed outcomes of a completed event, and scenario estimates are sensitive to assumptions about duration, spare capacity, and reserve releases. The duration-severity relationship in Figure 2 is stylised and its vertical scale is illustrative. The synthesis also does not model behavioural responses such as hoarding, stockpiling, and fuel substitution, each of which would alter a real trajectory. Finally, the structured review is focused rather than exhaustive, and a different corpus might weight the channels differently. 6.5 Future research The propositions define an empirical agenda. Proposition 2 can be tested by comparing the supply chain consequences of disruptions across chokepoints that differ in substitutability, holding trade volume constant, using the event-study and network methods already applied to the Suez and Red Sea cases (Notteboom et al., 2024; Tran et al., 2025). Propositions 3 and 4 invite event-study analysis of price, freight, and insurance responses during the 2025 and 2026 episodes as data accumulate. Proposition 5 can be examined through network-based simulation of propagation from Gulf energy and chemical feedstocks into specific industrial supply chains, extending the simulation approach of Ivanov (2020). Proposition 6 calls for comparative assessment of the effectiveness of reserve releases, demand reduction, and sourcing diversification across regions. Such work would convert the present inferences into testable, quantified relationships. 7. Conclusion A war that interrupts transit through the Strait of Hormuz would act on global supply chains as a compound shock that is at once an energy supply shock, a maritime logistics shock, and a trigger for cascading disruption across interconnected production networks. By integrating the maritime chokepoint, supply chain resilience, and oil price shock literatures into a single transmission framework, and by reading that framework against documented disruptions at the Suez Canal and in the Red Sea, this study identifies the feature that makes the Strait distinctive: the near-absence of a substitute route, which deprives firms of the rerouting response that contained earlier disruptions and places the Strait among the chokepoints whose closure effectively severs trade for dependent economies. The study's central theoretical move is to formalise route substitutability as a moderator of chokepoint severity, and its central empirical inference is that duration, rather than onset, governs whether a closure is absorbed or becomes transformative. The contribution is conceptual: an integrated framework, a set of six testable propositions, and a disciplined comparative synthesis rather than new empirical estimates. Its practical value lies in reorienting resilience planning for a non-substitutable chokepoint toward strategic reserves, diversified sourcing, the mapping of indirect network exposure, and the monitoring of insurance and chartering behaviour as early indicators, and in reframing the policy objective around the marginal restoration of supply. Its central caveat is that the analogues understate rather than overstate the likely consequences, because they permitted a routing response that the Strait does not. Testing the propositions as disruption data accumulate is the priority for subsequent research. References Atlantic Council. (2026). 15 charts that explain why the Strait of Hormuz shutdown matters for the global economy. https://www.atlanticcouncil.org/dispatches/15-charts-that-explain-why-the-strait-of-hormuz-shutdown-matters-for-the-global-economy/ Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823 Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275 Congressional Research Service. (2026). Iran conflict and the Strait of Hormuz: Impacts on oil, gas, and other commodities (Report No. R45281). https://www.congress.gov/crs-product/R45281 Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131–156. https://doi.org/10.1111/j.1540-5915.2007.00151.x Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430. https://doi.org/10.1080/00207543.2017.1387680 Gross, S., & Beane, R. (2026). From chokepoint to crisis: The Strait of Hormuz and global oil markets. Brookings Institution. https://www.brookings.edu/articles/from-chokepoint-to-crisis-the-strait-of-hormuz-and-global-oil-markets/ Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285–307. https://doi.org/10.1016/j.tre.2019.03.001 Inoue, H., & Todo, Y. (2019). Firm-level propagation of shocks through supply-chain networks. Nature Sustainability, 2, 841–847. https://doi.org/10.1038/s41893-019-0351-x International Energy Agency. (2025). Strait of Hormuz. https://www.iea.org/about/oil-security-and-emergency-response/strait-of-hormuz Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922 Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727 Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921. https://doi.org/10.1016/j.ijpe.2020.107921 Kilian, L. (2008). The economic effects of energy price shocks. Journal of Economic Literature, 46(4), 871–909. https://doi.org/10.1257/jel.46.4.871 Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053–1069. https://doi.org/10.1257/aer.99.3.1053 Kilian, L. (2014). Oil price shocks: Causes and consequences. Annual Review of Resource Economics, 6, 133–154. https://doi.org/10.1146/annurev-resource-083013-114701 Kilian, L., Plante, M., & Richter, A. W. (2026). What the closure of the Strait of Hormuz means for the global economy. Federal Reserve Bank of Dallas. https://www.dallasfed.org/research/economics/2026/0320 Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research, 291(3), 1117–1131. https://doi.org/10.1016/j.ejor.2020.09.053 Notteboom, T., Haralambides, H., & Cullinane, K. (2024). The Red Sea crisis: Ramifications for vessel operations, shipping networks, and maritime supply chains. Maritime Economics & Logistics, 26(1), 1–20. https://doi.org/10.1057/s41278-024-00287-z Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21. https://doi.org/10.1002/j.2158-1592.2010.tb00125.x Pratson, L. F. (2023). Assessing impacts to maritime shipping from marine chokepoint closures. Communications in Transportation Research, 3, 100083. https://doi.org/10.1016/j.commtr.2022.100083 Sheffi, Y., & Rice, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48. Tran, N. K., Haralambides, H., Notteboom, T., & Cullinane, K. (2025). The costs of maritime supply chain disruptions: The case of the Suez Canal blockage by the ‘Ever Given’ megaship. International Journal of Production Economics, 279, 109464. https://doi.org/10.1016/j.ijpe.2024.109464 United Nations Conference on Trade and Development. (2024). Review of maritime transport 2024. United Nations. https://unctad.org/publication/review-maritime-transport-2024 U.S. Energy Information Administration. (2025). Amid regional conflict, the Strait of Hormuz remains a critical oil chokepoint. Today in Energy. https://www.eia.gov/todayinenergy/detail.php?id=65504 Verschuur, J., Koks, E. E., & Hall, J. W. (2022). Ports’ criticality in international trade and global supply-chains. Nature Communications, 13, Article 4351. https://doi.org/10.1038/s41467-022-32070-0 Verschuur, J., Lumma, J., & Hall, J. W. (2025). Systemic impacts of disruptions at maritime chokepoints. Nature Communications, 16, Article 10421. https://doi.org/10.1038/s41467-025-65403-w Wieland, A., & Durach, C. F. (2021). Two perspectives on supply chain resilience. Journal of Business Logistics, 42(3), 315–322. https://doi.org/10.1111/jbl.12271 #StraitOfHormuz #SupplyChain #SupplyChainResilience #MaritimeChokepoints #OilPriceShocks #GeopoliticalRisk #GlobalTrade #EnergySecurity #RippleEffect #LogisticsResearch #SupplyChainManagement #Q1Research

Authors: Jose Garcia (1), Dr. Habib Al Souleiman (2), Dr. Ibrahim Al Souleiman (3) Affiliation: (1-3) Swiss International University (SIU) (1) ORCID ID : 0009-0001-2055-9608 (2) ORCID iD : 0009-0000-4746-0694 (3) ORCID iD: 0009-0002-9521-4847 Submitted 30 January 2026; Revised 02 March 2026; Accepted 8 May 2026; Available online 18 May 2026; Version of Record 14 May 2026. DOI 10.65326/u7y566770 Volume 3, December 2026, (10025) Abstract The game of Chicken remains a powerful model for the study of confrontation under mutual danger. Each player prefers victory to concession, concession to collision, and collision least of all. But the classical model was rooted in human perception, intentional signaling, and manifest commitment. This article contends that AI alters the internal logic of Chicken by changing escalation tempo, commitment credibility, signal interpretation, and practical conditions of de-escalation. The article uses bargaining theory and commitment-problem analysis to advance a qualitative conceptual framework that builds on peer-reviewed literature in game theory, crisis bargaining, strategic studies, human-autonomy teaming, and AI governance. The research gap is targeted at a specific theoretical gap: existing literature discusses AI and strategic stability, but it does not explain how AI changes the brinkmanship mechanism in Chicken-like settings where credibility is based on the capacity to seem willing or unable to walk away, but still keep a last-moment exit. This article introduces the concept of algorithmic brinkmanship, defined as the use, display or reliance on AI-enabled perception, prediction, delegation and automation to influence an adversary's beliefs under mutually harmful risk. It proposes six theoretical propositions. AI increases escalation risk when speed and opacity shorten deliberation; it increases commitment when delegation appears automatic; it undermines signaling when adversaries cannot infer thresholds or override capacity; it encourages de-escalation when systems are reversible, explainable, and institutionally governed; AI governance mediates the relationship between capability and strategic stability; and it transforms Chicken from a contest of observable resolve to a contest of human-machine credibility and control. The article ends by arguing that for high-stakes strategic interaction, AI governance needs to pay attention not only to accuracy or autonomy, but also to pauseability, interpretability, auditability and credible human restraint. Keywords: artificial intelligence; game of Chicken; brinkmanship; escalation; commitment; AI governance 1. Introduction Chicken is a strategy game in which two actors move toward a mutually damaging outcome, each hoping that the other will yield first. Its simple structure is analytically useful because it captures a recurring problem in international politics, military crises, cyber confrontation, economic coercion, and organizational conflict. The actors do not necessarily desire the collision. They want the distributional advantage that comes from the other side yielding first. The danger is then not an accident of the game; it is the medium through which bargaining power is produced. The larger logic is similar to hawk-dove conflict, where players must weigh the value of dominance against the cost of destructive confrontation (Maynard Smith & Price, 1973). Classical work on conflict, bargaining and signaling explains why rational actors may still approach disaster. During crises, actors can overstate their resolve, generate audience costs, pay sunk costs, raise forces, or commit themselves to make retreat difficult and threats credible (Fearon, 1995, 1997). In Chicken, credibility often comes at the expense of flexibility. The actor who appears least able or willing to turn may force the other actor to turn. But the same logic also produces the central danger of brinkmanship: that commitment can become so persuasive that the road back from the brink narrows or vanishes. AI changes this logic by changing the instruments through which actors perceive, signal, commit, and de-escalate. Examples of uses for AI-enabled systems include intelligence processing, threat classification, decision support, options recommendation, targeting, cyber defense management, filtering warning indicators, and speeding up decision cycles. Recent research shows that AI already influences military intelligence and targeting (King, 2024), crisis decision making (Horowitz & Lin-Greenberg, 2022), human-autonomy teaming (Mayer, 2023), and trust in high-stakes military contexts (Kreps et al., 2023; Lushenko & Sparrow, 2024). These are important developments for Chicken because the model is not just about preferences, it is about the credibility and interpretation of action under acute time pressure. The problem isn't just that AI makes decisions faster or more rational. The issue is that AI changes the relationship between speed, confidence, delegation and control. But a better early warning system could reduce uncertainty, and it could also tempt actors to move closer to the brink because they think they can quantify risk more precisely. A system that automates response may bolster deterrence. But it may also obscure whether leaders maintain real capacity to pause, revise or reverse action. A recommendation-generating system may improve analysis, but it may also lead to over-reliance, automation bias, or political cover for a hard-line decision (Holbrook et al., 2024; Horowitz & Kahn, 2024). This article develops the concept of algorithmic brinkmanship to account for these changes. Algorithmic brinkmanship is a strategic behavior in which actors use, signal, or rely on AI-enabled perception, prediction, delegation, or automation to shape an opponent’s expectations in a confrontation that has the structure of Chicken. The notion does not imply that machines have independent strategic preferences. Instead, it notes that AI gets integrated into the bargaining apparatus. It influences actors’ beliefs, their speed of action, the credibility of their commitments, and the visibility of their residual ability to restrain themselves. The article asks: How does AI affect escalation, commitment, and de-escalation in the game of Chicken? The answer is developed in a qualitative conceptual methodology. The analysis draws on peer-reviewed literature. It develops a theoretical model rather than testing new empirical data. This is appropriate, as the aim of the article is to clarify mechanisms and to generate propositions for future empirical research. 2. Research Gap, Objectives, and Research Questions The gap in the research is not the lack of work on AI and security. That literature has grown rapidly. AI and strategic stability (Altmann & Sauer, 2017; Ayoub & Payne, 2016), AI and deterrence (Johnson, 2020a; Zala, 2024), nuclear instability (Johnson, 2020b), machine delegation (Johnson, 2022), military transformation (Hunter, 2024), human-autonomy teaming (Lyons et al., 2021; Mayer, 2023), military decision support (Horowitz & Lin-Greenberg, 2022), public and elite trust in AI (Kreps et al., 2023; Lushenko & Sparrow, 2024), resort-to-force decision-making (Erskine, 2024; Erskine & Miller, 2024), and the governance of responsible AI (Firlej, 2021; Laux et al., 2024; Papagiannidis et al., 2025; Schraagen, 2023) have been explored by scholars. The scholarship provides valuable insights, but a lot of it considers AI as a generic strategic variable: a source of speed, autonomy, uncertainty or governance risk. What is left to develop is the exact game-theoretic mechanism by which AI alters brinkmanship in Chicken. Unlike many other models of conflict, Chicken’s bargaining power depends on a paradoxical performance: the actor must look committed enough to make the opponent give way, but not so committed that collision is unavoidable. Existing studies often focus on whether AI stabilizes or destabilizes deterrence. This article asks a sharper question: what impact does AI have on the strategic utility and danger of looking like you can’t turn? This gap matters because AI affects Chicken at the points where the classical model is most fragile. It first changes escalation by reducing the time between seeing and doing. Second, it changes commitment by embedding threats within technical architectures and delegated processes. Third, it alters signaling, since opponents may not know what has been automated, what thresholds are in force, or whether human override is available. Fourth, it changes de-escalation in the sense that exiting the crisis may require technical reversibility, not just diplomatic communication. In the literature, these elements have been considered separately, but not as part of a single account of algorithmic Chicken. The article aims, therefore, to provide a theoretical account of algorithmic brinkmanship. Specifically, it aims to: (a) clarify how AI affects the escalation logic of Chicken; (b) describe how AI-enabled delegation influences the credibility and control of commitment; (c) explain why AI makes signaling more ambiguous; and (d) specify the conditions under which AI can facilitate rather than hinder de-escalation. This article is guided by three research questions: RQ1: How does AI affect escalation dynamics in Chicken-like confrontations? RQ2: What is the impact of AI on the credibility, interpretation, and reversibility of commitment? RQ3: Under what conditions can AI help de-escalate rather than escalate brinkmanship? 3. Theoretical framework: bargaining, commitment and control The theoretical foundation of this article draws on bargaining theory and commitment-problem analysis. Bargaining theory argues that conflict can arise even when there is a mutually preferable settlement because actors have private information, incentives to misrepresent, and problems of credible commitment (Fearon, 1995). Chicken is a very special and vivid form of the problem. The actors want to avoid a collision, but they disagree about who should yield. Each has an incentive to signal resolve while concealing willingness to yield. The main mechanism is commitment. A commitment is credible when it is costly or difficult to back down. The classical crisis bargaining literature identifies public commitments, audience costs, sunk costs, mobilization, reputation, and irreversible deployments (Fearon, 1997). In Chicken, commitment is created by making one’s own turning less available. But commitment is a two-way street. If it is too weak, the opponent may not give way. If it is too strong, there may not be enough space for either side to de-escalate. AI falls into this framework in three ways: First, it is an information infrastructure. It affects what actors think they know about the environment, the opponent and the probability of danger. Second, it is an infrastructure of commitment. It can pre-set thresholds, automate responses, prioritize options and reduce the practical time available for humans to reconsider. Third, it is a signaling infrastructure. It can signal readiness, speed, technical sophistication or willingness to delegate action. The framework thus does not treat AI as a strategically autonomous actor, but as a human-machine decision architecture embedded in bargaining. This is a significant difference. The analytical question is not whether AI has intentions. The question is whether AI alters the credibility and meaning of human and institutional intentions. In a Chicken-like confrontation an AI-enabled posture may tell an opponent: we see faster, we can respond sooner, our response may be predelegated, and your time to influence us is limited. As for the effect of that message, whether it deters, reassures, or provokes depends on how credible, interpretable, and reversible the posture appears. Table 1. Classical Chicken and Algorithmic Chicken Compared Dimension Classical Chicken Algorithmic Chicken Strategic implication Escalation Visible threats, mobilization, public deadlines, and political resolve. AI-enabled warning, sensor fusion, decision-support systems, automated cyber or military response. Escalation becomes faster and may be driven by perceived informational advantage. Commitment Credibility through audience costs, sunk costs, mobilization, and reduced freedom to retreat. Credibility through delegation, pre-set thresholds, system integration, and machine-speed reaction. Commitment becomes stronger but may become less transparent and less reversible. Signaling Signals are interpreted through human intention, reputation, and visible cost. Signals include technical posture, opacity, automation level, and data-driven readiness. Opponents may misread defensive automation as offensive preparation. De-escalation Negotiation, concession, delay, mediation, and face-saving compromise. Pause mechanisms, human override, explainable alerts, audit trails, and deconfliction channels. De-escalation depends on technical reversibility as well as political communication. Note. The comparison is analytical and synthesizes the theoretical argument; it does not report empirical results. 4. MethodologyThis article uses a qualitative conceptual methodology. Conceptual analysis is appropriate when a research problem needs the clarification of mechanisms, categories, and theoretical relations before systematic empirical testing. This method is particularly well-suited because algorithmic brinkmanship is an emerging phenomenon across the military, cyber, diplomatic, and organizational domains. Direct empirical data on fully fledged AI-enabled Chicken crises are limited, uneven, and often classified. Such a conceptual approach enables the article to formulate theoretically disciplined propositions that can later be tested by case studies, experiments, formal modeling, or simulations. The analysis proceeds in four steps. First, it extracts from the existing literature of game theory and crisis bargaining the core mechanisms of Chicken: escalation, commitment, signaling, and de-escalation. Second, it sets out how recent AI and security studies describe changes in speed, autonomy, trust, opacity, targeting and governance. Third, it links these AI-related changes to the mechanisms of the Chicken. Fourth, it develops theoretical propositions that specify expected relationships between AI-enabled speed, opacity, delegation, reversibility, and escalation outcomes. The selection of the literature base was based on three principles. First, the article uses peer-reviewed academic sources with DOIs, which is consistent with the requirements for submission to high-quality journals. Second, it merges classical theoretical work with recent scholarship (2020–2025) so that the argument is grounded in established theory but responsive to current debates. Third, it emphasizes sources that address issues related to strategic decision-making, human-machine trust, autonomous systems, AI governance, and crisis behavior, rather than general discussions of digital technology. The methodology is analytical, not empirical. It does not claim to measure the frequency of algorithmic brinkmanship, or to test the propositions statistically. Its validity is based on theoretical consistency, clear conceptual representation, and consistency with current peer-reviewed findings. This design is common in early stage theory building, where the objective is to render a phenomenon researchable by specifying its mechanisms, scope conditions, and observable implications. The paper is focused on strategic interactions with Chicken-type payoffs. It does not claim all AI-enabled conflict is like Chicken. Some interactions resemble Prisoner’s Dilemma, Stag Hunt, bargaining over indivisible goods, or repeated deterrence games. The argument is most directly relevant to situations in which two actors have an interest in avoiding mutual harm but are competing over who must yield, delay, or concede first. Figure 1. Mechanism model of algorithmic brinkmanship in Chicken-like interaction. 5. Analysis: Escalation Tempo and Algorithmic Confidence Escalation in Chicken is not just movement toward conflict, it is movement toward danger for bargaining effect. The actor escalates to make the opponent think that the costs or futility of continuing resistance are high. AI changes escalation by increasing the speed, volume and apparent precision of information. Military and security organizations are increasingly using AI to analyze intelligence, assist targeting, prioritize warnings and structure choices (King, 2024). These systems can improve awareness, but they also change the pace of crisis interaction. Mechanism one is time compression. Rapidly detecting signals and suggesting responses may put pressure on leaders to act before the opponent gains the upper hand. But that does not mean the death of human judgement. Instead, judgment is increasingly made in compressed time and system-generated urgency. In a Chicken-like crisis, time compression is dangerous because the actors are already trying to convince each other that they will not be the first to swerve. The quicker the interaction, the less scope for clarification, mediation or face-saving adjustment. The second mechanism is confidence inflation. AI can give the impression that risk is measurable more precisely than it is. The person making a decision when given a probability estimate, a pattern classification or an optimized recommendation may feel that escalation can be better managed. Better information can mean better action in everyday management contexts. But in brinkmanship, confidence can breed risk-taking. Leaders who think AI will find the last safe chance to turn may drive to the point of collision. The third mechanism is adversarial interpretation. Horowitz and Lin-Greenberg (2022) demonstrate how the use of AI can influence how national security experts understand crisis events, including rival accidents involving AI-enabled systems . This matters for Chicken, as the same behavior can be interpreted as resolve, error, loss of control, or preparation to attack. AI does not eliminate ambiguity. It may shift ambiguity from human intention to system behavior. One might ask of an opponent: Was this move deliberate? Was it triggered by a threshold? Can it be reversed? Who can stop it? So AI can reduce certain forms of uncertainty and increase others. It might improve environmental knowledge but reduce social and strategic interpretation. Ignorance alone is not the core risk of escalation. The core risk is misplaced confidence under ambiguous interdependence. 6. Analysis: Commitment, Delegation, and the New Steering Wheel The classical metaphor of Chicken often involves throwing away the steering wheel. The gesture communicates that the actor can no longer turn, forcing the opponent to choose between concession and collision. AI creates new forms of steering-wheel removal. Predelegated response systems, automated cyber defenses, predictive targeting pipelines, and machine-speed warning architectures can all reduce the apparent role of discretionary human choice. Delegation can strengthen commitment because it makes response appear less dependent on political hesitation. Johnson (2022) argues that delegating strategic decision-making to machines raises serious questions about stability, escalation, and control. In Chicken, this matters because a threat becomes more credible when the opponent believes the actor cannot easily back away. An automated threshold may function as a technical red line. If crossed, it may generate a response with limited delay. Yet algorithmic commitment differs from classical commitment in an important way. It is often difficult for outsiders to verify. Public commitment can be seen and heard; algorithmic commitment may be hidden in architecture, code, data pipelines, command rules, or organizational practice. An actor may exaggerate automation to appear resolute, conceal automation to preserve advantage, or misunderstand its own system's practical rigidity. This creates a credibility-interpretability gap: commitment may be strong but not legible, or legible but not truly strong. This gap matters because deterrence requires communication. A red line that cannot be understood may not deter; it may only surprise. A threshold that changes dynamically may be difficult for the opponent to avoid. A response system that lacks visible human override may convince the opponent that communication is useless. Thus, AI-enabled commitment can simultaneously increase credibility and reduce crisis manageability. The commitment problem is also organizational. AI outputs can become political resources. Leaders may use system recommendations to justify hard-line positions or to resist compromise. Once a decision is framed as technically validated, retreat may appear irrational, weak, or irresponsible. In this sense, AI can create internal audience costs. A leader may become tied not only to a public threat but also to the authority of a system that has been presented as objective. 7. Analysis: Signaling, Opacity and Trust Chicken signaling is predicated on the opponent’s ability to infer resolve from action. AI makes this inference complicated. A technical deployment could be a sign of defensive vigilance, offensive preparation, bureaucratic modernization, political theater, or a true willingness to automate escalation. Opponents may not know what interpretation is right. Recent work on trust and automation can help explain this ambiguity, not just external but also internal. Human operators may overtrust or undertrust AI depending on system performance, task design, stakes, and institutional culture (Dietvorst et al., 2015; Logg et al., 2019; Parasuraman & Riley, 1997) . Experimental work indicates that trust in AI is context-dependent and can differ based on purpose, oversight, precision, and perceived risk (Kreps et al., 2023; Lushenko & Sparrow, 2024; Mayer, 2023). Holbrook et al. (2024) illustrate the danger of overtrust in life-and-death recommendations. These results suggest that algorithmic Chicken is not just a game of two rational calculators. It’s a competition between organizations that may not be uniformly dependent on machines, and whose machine dependence may be contested and hard for adversaries to read. The opponent's belief about trust becomes strategic. If an actor is thought to overtrust AI, its threats may seem more dangerous because it may act rigidly or prematurely. If an actor is perceived as not trusting AI, its technical posture could be less credible. If an actor publicly claims human control but privately relies heavily on automated pipelines, the signal could be unstable. The opponent must read not only intentions but the human-machine relationship within the rival’s decision architecture. Here is where AI governance becomes immediately relevant to game theory. Governance principles like transparency, accountability, human oversight, auditability and risk management are not only ethical or legal concerns. They are strategic variables in Chicken-like interaction. Laux et al. (2024) caution against equating trustworthiness with acceptability of risk. Papagiannidis et al. (2025) define responsible AI governance as structural, relational, and procedural. These governance dimensions shape whether opponents can understand thresholds, believe in restraint, and find de-escalatory paths within algorithmic brinkmanship. Figure 2. Strategic zones created by speed, opacity, and reversibility in algorithmic Chicken. 8. Theoretical Propositions The analysis can be summarized in six theoretical propositions. These propositions are not statistical results. They are theory-building claims that specify observable relationships for future research. Proposition 1: The risk of escalation in Chicken-like crises is increased by AI-enabled speed when the capacity to compress deliberation outstrips the capacity to improve shared interpretation. The main risk is not speed itself but speed plus ambiguity. When machine outputs accelerate action without generating mutual understanding, actors may drift toward collision before political communication can remedy misperception . Proposition 2: When actors believe predictive systems allow them to manage danger more precisely than the strategic environment permits, AI-enabled confidence increases brinkmanship. This proposition suggests a confidence / risk route. Better data can reduce uncertainty, but it can also lead actors to accept more danger by overestimating their control. Proposition 3: When opponents believe that response thresholds are automatic or difficult to reverse, AI-enabled delegation of response decisions enhances the credibility of commitment. This generalizes classical commitment theory. Automation can be like a technical audience cost or a digital steering wheel that is discarded. Proposition 4: AI-enabled commitment is destabilizing when the thresholds, override rules, or accountability structures are opaque to the opponent. Commitment has to be credible, but it has to be comprehensible. Opaque commitment may not deter, but it raises the risk of accidental or inadvertent escalation. Proposition 5: AI aids de-escalation when systems are built with reversibility, explanation and credible human override. Algorithmic Chicken de-escalation takes more than a diplomatic note. It requires the practical ability to stop technical processes, audit machine recommendations and communicate that restraint can still be exercised. Proposition 6: AI governance mediates the relationship between AI capability and strategic stability. The same AI capacity could be stabilizing or destabilizing depending on the design of institutions. Governance is thus not exogenous to the game, but changes the payoff-relevant beliefs that actors have about credibility, control and restraint. Table 2. Propositions and Observable Implications Proposition Core mechanism Observable implication for future research P1: Speed-risk proposition AI compresses the perception-action cycle. Crises with faster AI-enabled warning and response should show shorter windows for diplomacy and higher reliance on preplanned moves. P2: Confidence-risk proposition AI creates perceived precision and control. Actors with high confidence in prediction tools should be more willing to escalate close to thresholds. P3: Delegated-commitment proposition Automation makes threats appear less discretionary. Public or inferred automation should increase perceived resolve, especially when thresholds appear pre-set. P4: Opacity-instability proposition Opaque thresholds weaken shared interpretation. Ambiguous AI posture should increase misperception and reduce the deterrent value of signals. P5: Reversibility proposition Pauseability and override preserve exits. Systems with clear human override and explainability should lower escalation persistence after warning errors. P6: Governance-moderation proposition Rules shape trust and strategic interpretation. Governance practices should moderate whether AI-enabled capabilities are read as stabilizing or threatening. Note. The propositions are theoretical claims derived from the conceptual analysis and are intended for future empirical testing. 9. Discussion: Implications for Game Theory, Strategic Studies, AI Governance 9.1 Contributions to Game Theory The article contributes to game theory by demonstrating that the standard Chicken model should be extended to include decision architecture. Classical Chicken is about preferences and strategic choices: go on or swerve. This article introduces a prior and concurrent layer: how the actors perceive the road, how quick they are to react, how their commitments are technically embedded and whether the opponent can understand their residual capacity to turn. The contribution is not to replace Chicken, but to refine its assumptions for AI-enabled interaction. Specifically, the article points to credibility, interpretability and reversibility as related variables. Credibility often dominates classic accounts: An actor who can make retreat costly may win. Algorithmic Chicken shows that credibility without interpretability can go wrong. If the adversary can’t discern what’s been automated, or where thresholds are, commitment may not be a sign of resolve. Credibility without reversibility may produce collision rather than bargaining success, similarly. Game-theoretic models of Chicken should therefore take into account the legibility and pauseability of commitment devices, in addition to their strength. 9.2 Contribution to Strategic Studies The article explains why the impact of AI on strategic stability cannot be evaluated only in terms of capabilities. AI-enabled systems could improve intelligence, targeting, warning and coordination but their strategic impact will depend on the context of a crisis. In Chicken-like encounters, capabilities that seem operationally efficient can be strategically dangerous, if they compress time, obscure intention, or reduce space for controlled retreat. This is why the same AI system can be stabilizing in routine monitoring but destabilizing in a crisis. The article also redefines deterrence and brinkmanship. Deterrence is more than just capability and resolve. It also demands that adversaries know what actions will elicit response and whether communication can still change outcomes. Algorithmic brinkmanship thus expands strategic studies from a narrow focus on autonomous weapons to a broader focus on AI-enabled decision architectures. And even if they are not weapons, targeting systems, intelligence processing, cyber defense, logistics and warning tools can all have an effect on escalation. 9.3 Contribution to AI Governance The article demonstrates that governance principles have strategic effects for AI governance. Ethical or legal safeguards are often raised in the form of human oversight, transparency, explainability, auditability and accountability. They also serve as de-escalation mechanisms in the game of Chicken. A system that can be paused, explained and overridden, signals something different from a system that appears automatic, opaque and irreversible. This contribution is important because many AI governance frameworks are concerned with internal risk management: Is the system accurate, fair, accountable, and compliant? Algorithmic brinkmanship introduces an external relational requirement: can adversaries, partners, and crisis interlocutors establish reliable beliefs about the thresholds of the system and human control? So governance is not just about keeping AI safe within an organization. It’s also about making AI behavior strategically interpretable to others when the stakes are high. 10. Limitations and Future Research The article has a few limitations. First, it is conceptual, not empirical. It develops mechanisms and propositions but doesn’t test them against a dataset or case archive. This is appropriate for theory building, but the propositions need systematic empirical evaluation. Second, the article treats AI at an abstract level that covers decision support, warning, targeting, cyber response, and autonomous functions. Future research should disaggregate these technologies, as different systems might have different effects on escalation. Third, the article is focused on Chicken-like interactions, and does not claim that all AI-enabled crises have this structure. Not all conflicts are Chicken. Some involve repeated bargaining, alliance reassurance, arms racing, or cooperation problems. Future work should compare the effects of AI across games. Fourth, the article does not give a formal mathematical model. Formal modeling could help to clarify equilibrium conditions under different assumptions on speed, opacity and reversibility. Four directions for future research are proposed. Case studies could explore crises involving automated warning, cyber defense, drone escalation, or AI-enabled targeting to determine whether the mechanisms suggested here are present in practice. Experimental research could test whether decision-makers are more prone to escalate when they are supported by high-confidence algorithmic advice. Wargaming could look at how opponents interpret different levels of AI delegation versus human override. Finally, governance research could examine what transparency and pauseability measures are credible enough to support de-escalation without revealing sensitive capabilities. 11. Conclusion This article has argued that AI changes the game of Chicken by changing the mechanisms of escalation, commitment, signaling and de-escalation. The primary contribution is the idea of algorithmic brinkmanship: the use, presentation, or reliance upon AI-enabled perception, prediction, delegation, and automation to shape an opponent’s expectations under mutual peril. The article demonstrates that AI does not simply make strategic actors more rational or more reckless. But its effect depends on the way in which speed, opacity, delegation and reversibility are combined. AI can improve warning and support restraint but can also shorten deliberation, inflate confidence, harden commitment, and make signals more difficult to interpret. The game is therefore not a contest of visible will, but a contest of human-machine credibility and control. For theory, the article extends Chicken by adding decision architecture to commitment analysis. For strategic studies, it provides an explanation of why AI-enabled capability can be operationally useful and strategically destabilizing. For AI governance, it shows that oversight, explanation, auditability and human override are not only ethical safeguards; they are de-escalation mechanisms. The practical lesson is simple: in high-stakes strategic interaction, AI systems need to be designed not only to act, but to pause, explain, and leave room to turn away. References Altmann, J., & Sauer, F. (2017). Autonomous weapon systems and strategic stability. Survival, 59(5), 117-142. https://doi.org/10.1080/00396338.2017.1375263 Ayoub, K., & Payne, K. (2016). Strategy in the age of artificial intelligence. Journal of Strategic Studies, 39(5-6), 793-819. https://doi.org/10.1080/01402390.2015.1088838 Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114-126. https://doi.org/10.1037/xge0000033 Erskine, T. (2024). Before algorithmic Armageddon: Anticipating immediate risks to restrain when AI infiltrates decisions to wage war. Australian Journal of International Affairs, 78(2), 175-190. https://doi.org/10.1080/10357718.2024.2345636 Erskine, T., & Miller, S. E. (2024). AI and the decision to go to war: Future risks and opportunities. Australian Journal of International Affairs, 78(2), 135-147. https://doi.org/10.1080/10357718.2024.2349598 Fearon, J. D. (1995). Rationalist explanations for war. International Organization, 49(3), 379-414. https://doi.org/10.1017/S0020818300033324 Fearon, J. D. (1997). Signaling foreign policy interests: Tying hands versus sinking costs. Journal of Conflict Resolution, 41(1), 68-90. https://doi.org/10.1177/0022002797041001004 Firlej, M. (2021). Regulating human control over autonomous systems. Regulation & Governance, 15(4), 1071-1088. https://doi.org/10.1111/rego.12344 Holbrook, C., Holman, D., Clingo, J., & Wagner, A. R. (2024). Overtrust in AI recommendations about whether or not to kill: Evidence from two human-robot interaction studies. Scientific Reports, 14, Article 19751. https://doi.org/10.1038/s41598-024-69771-z Horowitz, M. C., & Kahn, L. (2024). Bending the automation bias curve: A study of human and AI-based decision making in national security contexts. International Studies Quarterly, 68(2), sqae020. https://doi.org/10.1093/isq/sqae020 Horowitz, M. C., & Lin-Greenberg, E. (2022). Algorithms and influence: Artificial intelligence and crisis decision-making. International Studies Quarterly, 66(4), sqac069. https://doi.org/10.1093/isq/sqac069 Hunter, C. (2024). We'll never have a model of an AI major-general: Artificial intelligence, command decisions and kitsch visions of war. Journal of Strategic Studies, 47(4), 685-706. https://doi.org/10.1080/01402390.2023.2241648 Johnson, J. (2020a). Deterrence in the age of artificial intelligence and autonomy: A paradigm shift in nuclear deterrence theory and practice? Defense & Security Analysis, 36(4), 422-448. https://doi.org/10.1080/14751798.2020.1857911 Johnson, J. (2020b). Artificial intelligence in nuclear warfare: A perfect storm of instability? The Washington Quarterly, 43(2), 197-211. https://doi.org/10.1080/0163660X.2020.1770968 Johnson, J. (2020c). Artificial intelligence, drone swarming and escalation risks in future warfare. The RUSI Journal, 165(2), 26-36. https://doi.org/10.1080/03071847.2020.1752026 Johnson, J. (2022). Delegating strategic decision-making to machines: Dr. Strangelove redux? Journal of Strategic Studies, 45(3), 439-477. https://doi.org/10.1080/01402390.2020.1759038 King, A. (2024). Digital targeting: Artificial intelligence, data, and military intelligence. Journal of Global Security Studies, 9(2), ogae009. https://doi.org/10.1093/jogss/ogae009 Kreps, S., George, J., Lushenko, P., & Rao, A. (2023). Exploring the artificial intelligence trust paradox: Evidence from a survey experiment in the United States. PLOS ONE, 18(7), e0288109. https://doi.org/10.1371/journal.pone.0288109 Laux, J., Wachter, S., & Mittelstadt, B. (2024). Trustworthy artificial intelligence and the European Union AI Act: On the conflation of trustworthiness and acceptability of risk. Regulation & Governance, 18(1), 3-32. https://doi.org/10.1111/rego.12512 Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103. https://doi.org/10.1016/j.obhdp.2018.12.005 Lushenko, P., & Sparrow, R. (2024). Artificial intelligence and U.S. military cadets' attitudes about future war. Armed Forces & Society. Advance online publication. https://doi.org/10.1177/0095327X241284264 Lyons, J. B., Sycara, K., Lewis, M., & Capiola, A. (2021). Human-autonomy teaming: Definitions, debates, and directions. Frontiers in Psychology, 12, Article 589585. https://doi.org/10.3389/fpsyg.2021.589585 Mayer, M. (2023). Trusting machine intelligence: Artificial intelligence and human-autonomy teaming in military operations. Defense & Security Analysis, 39(4), 521-538. https://doi.org/10.1080/14751798.2023.2264070 Maynard Smith, J., & Price, G. R. (1973). The logic of animal conflict. Nature, 246, 15-18. https://doi.org/10.1038/246015a0 Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. Journal of Strategic Information Systems, 34(2), Article 101885. https://doi.org/10.1016/j.jsis.2024.101885 Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253. https://doi.org/10.1518/001872097778543886 Schraagen, J. M. (2023). Responsible use of AI in military systems. Ergonomics, 66(12), 1905-1913. https://doi.org/10.1080/00140139.2023.2278394 Zala, B. (2024). Should AI stay or should AI go? First strike incentives and strategic stability. Australian Journal of International Affairs, 78(2), 200-210. https://doi.org/10.1080/10357718.2024.2328805 #Artificial_Intelligence #Executive_Education #Dynamic_Capabilities #Leadership_Development #Strategic_Learning #Organizational_Adaptation #Algorithmic_Governance

Author: Miguel López Affiliation: Independent researcher Received 5 August 2025; Revised 10 Sep 2025; Accepted 05 Oct 2025; Available online 30 Oct 2025; Version of Record 30 Oct 2025; Post-Publication Update 20 Jun 2026. https://doi.org/10.65326/u7y566820-2 Volume 2, December 2025, (10020-2) Abstract Autonomous AI agents—software systems that can plan, decide, and act toward goals with limited human supervision—moved from isolated pilots to enterprise deployment over 2024 and 2025. This article develops a critical sociology of that shift across corporate management, tourism, and the technology supply base. Integrating Pierre Bourdieu’s theory of capital, field, and habitus; world-systems analysis; and institutional isomorphism, it advances a conceptual framework for how agentic AI reorganizes power within firms and across the global division of digital labor. The analysis yields seven propositions: agent adoption relocates authority toward actors who control data, orchestration, and governance; it gives rise to a distinct field of agent governance in which demonstrable control becomes a form of capital; it reconfigures the managerial habitus around the supervision of software actors; it tends to reproduce core–periphery asymmetries anchored in the concentration of models, compute, and standards; in intermediated sectors such as tourism it concentrates value capture while externalizing local social and environmental costs; coercive, mimetic, and normative pressures push organizations toward convergent agent architectures; and the legitimacy of deployment increasingly rests on symbolic markers of responsible control. A seven-layer governance model translates these claims into design guidance. The argument is conceptual and interpretive, and is intended to frame an agenda for empirical research on the political economy and organizational sociology of autonomous agents. Keywords: autonomous AI agents; organizational power; Bourdieu; world-systems analysis; institutional isomorphism; AI governance; tourism; political economy of AI 1. Introduction Over the course of 2024 and 2025, organizations began to delegate multistep work—procurement checks, scheduling and routing, content production, risk monitoring, and the orchestration of customer service—to autonomous AI agents that operate across human and digital environments within defined constraints. Industry analysts marked the turn explicitly: agentic AI was named the leading strategic technology trend for 2025, with the expectation that a measurable share of routine work decisions would, within a few years, be taken autonomously rather than by people (Gartner, 2024). The technical basis for this shift is the use of large language models as planning and tool-using cores, which allow software to interpret goals, call services, monitor outcomes, and adapt with limited supervision (Wang et al., 2024). The phenomenon is therefore neither a marketing label nor a simple extension of earlier automation; it is a change in the locus of organizational decision-making. Existing scholarship has examined automation and artificial intelligence primarily through two channels. One estimates the labor-market consequences of automating tasks, documenting displacement and wage effects of robots and, more recently, of AI as expressed in hiring patterns (Acemoglu & Restrepo, 2020; Acemoglu et al., 2022). The other interrogates the political economy of digital infrastructures—platform capitalism, surveillance capitalism, and data colonialism—showing how value is captured through the appropriation of data and the control of connective infrastructure (Srnicek, 2016; Zuboff, 2019; Couldry & Mejias, 2019; Crawford, 2021). Both literatures are indispensable, yet neither was written for systems that act. Task-displacement models treat technology as a substitute for labor inputs rather than as an actor that exercises delegated authority; political-economy accounts analyze data extraction and platform power but say little about how the internal authority structure of the firm is reorganized when software, not staff, executes consequential decisions. The result is a gap at the meeting point of organizational sociology and the global political economy of computation: how the delegation of action to autonomous agents redistributes power inside organizations and reproduces or unsettles asymmetries between them. This article addresses that gap. It asks who gains and loses different forms of capital as agents are adopted; how fields of practice and professional dispositions adapt; whether agentic AI deepens or disturbs core–periphery relations in the world-system of data, compute, and standards; and why organizations operating in very different contexts converge on similar governance arrangements. To answer these questions it integrates three sociological frameworks—Bourdieu’s theory of capital, field, and habitus (Bourdieu, 1977, 1986, 1990); world-systems analysis (Wallerstein, 2004); and the theory of institutional isomorphism (DiMaggio & Powell, 1983)—and applies them to three settings chosen for analytical contrast: corporate management, tourism, and the technology supply base. The contribution is threefold. Conceptually, the article extends field theory to the supervision of autonomous software, proposing that demonstrable control—what we term governability—becomes an emergent form of capital around which a new organizational field forms. Theoretically, it connects organizational power to the global political economy of AI, specifying the mechanisms—model control, compute concentration, and standard-setting—through which agentic AI can reproduce core–periphery asymmetries. Practically, it offers a seven-layer governance model that translates the analysis into design guidance and reframes adoption as a question of power and legitimacy rather than efficiency alone. The remainder of the article defines agentic AI and its diffusion, presents the theoretical framework and research design, develops the analysis through seven propositions across the firm, sectoral, and global levels, and discusses the contribution before noting limitations and a research agenda. 2. Autonomous AI Agents in Organizations: Concept and Diffusion 2.1 Defining autonomous AI agents An autonomous AI agent is a system that interprets a goal, plans a sequence of actions, invokes tools or services, observes the results, and adjusts its behavior to reach the objective with limited human intervention (Wang et al., 2024). Two features distinguish agents from earlier automation. First, agents reason iteratively rather than execute fixed scripts, which allows them to handle tasks whose steps are not specified in advance. Second, they act—they place orders, adjust prices, open tickets, or reconfigure schedules—rather than merely producing outputs for a person to act upon. In enterprise settings these capabilities are embedded in resource-planning, customer-relationship, supply-chain, and analytics platforms, and are increasingly coordinated through orchestration layers that route tasks among multiple agents and escalate to humans when constraints or uncertainty require it. The salient sociological fact is not the technique but the delegation: a measure of organizational authority is transferred to a non-human actor (Latour, 2005). 2.2 The diffusion of agentic AI Adoption is uneven but broad enough to alter managerial routines and labor processes. It proceeds along three reinforcing pathways. The infrastructural pathway lowers integration costs through cloud services, connectors, and data stores, so that capability can be assembled rather than built. The organizational pathway creates new roles—policy design, safety evaluation, monitoring, and red-teaming—and new bodies, such as cross-functional councils that adjudicate escalation rules and acceptable use. The cultural pathway changes work itself: managers learn to supervise software, frontline staff move from execution toward exception handling, and performance metrics expand from throughput toward quality, alignment, and accountability. These pathways echo earlier accounts of how digital technologies reorganize firms and economies (Brynjolfsson & McAfee, 2014; Castells, 1996; Davenport & Ronanki, 2018) and of how AI reshapes whole institutions rather than discrete tasks (Katsamakas et al., 2024). What is new is that the technology being diffused is an actor with delegated discretion, which is why its diffusion is also a redistribution of authority. 3. Theoretical Framework Three frameworks structure the analysis, each addressing a different level at which power is organized: the intra-organizational, the global-systemic, and the inter-organizational field. 3.1 Capital, field, and habitus Bourdieu’s sociology treats social life as a struggle for position within relatively autonomous fields, waged with different species of capital and guided by habitus, the durable dispositions that make certain actions feel natural to actors in a given position (Bourdieu, 1977, 1990). Capital takes economic form (budget, compute, data-acquisition capacity), cultural form (technical and governance expertise, domain knowledge), social form (the relationships that secure data, partnerships, and preferential access), and symbolic form (the prestige and legitimacy of being seen to operate responsibly or at the frontier) (Bourdieu, 1986). Read through this lens, agentic AI is a stake over which these capitals are contested and converted, not merely a tool that firms possess or lack. 3.2 World-systems analysis World-systems analysis positions the global economy as a hierarchy of core, semi-periphery, and periphery, in which surplus flows toward actors that control the most valued capacities (Wallerstein, 2004). In the political economy of computation, those capacities are foundational models, large-scale compute, and the standards that govern interoperability and audit. Critical accounts of data colonialism and platform capitalism show how value is appropriated through the control of connective infrastructure and the conversion of social life into data (Couldry & Mejias, 2019; Srnicek, 2016; Crawford, 2021). Agentic AI extends this dynamic: when planning, action, and standards are anchored in core infrastructures, peripheral organizations may act through agents they do not control and on terms they did not set. 3.3 Institutional isomorphism DiMaggio and Powell (1983) explain why organizations in a field come to resemble one another through three mechanisms: coercive pressure from regulation and dependency, mimetic imitation of perceived leaders under uncertainty, and normative pressure from professions that define legitimate practice. The framework is well suited to a fast-moving technology whose governance templates are still forming. Evidence from the adjacent domain of AI ethics is indicative: a global mapping found convergence on a small set of principles alongside persistent divergence in how they are implemented (Jobin et al., 2019), the signature pattern of isomorphism operating before stable templates exist. 3.4 An integrated framework The three lenses are complementary rather than redundant. Bourdieu specifies the intra-organizational stakes and the actors who win or lose position; world-systems analysis situates those struggles within a global hierarchy of computational capacity; institutional isomorphism explains why responses converge across organizations and how legitimacy is conferred. Together they move the study of agentic AI beyond questions of technical feasibility toward questions of power, value capture, and legitimacy. The research design below operationalizes this integration. 4. Research Design This is a conceptual, theory-building study. Its aim is to construct and integrate a framework that explains an emerging phenomenon and to derive propositions for subsequent empirical testing, rather than to test hypotheses against primary data. The design follows three commitments: explicit selection logic for the theories and settings analyzed, a transparent analytical procedure, and clearly stated scope conditions. The three frameworks were selected for complementary levels of analysis and for their established standing in organizational sociology and the political economy of technology. Bourdieu’s framework supplies a micro-to-meso account of authority and legitimacy within organizations; world-systems analysis supplies a macro account of global asymmetry; institutional isomorphism supplies a meso account of inter-organizational convergence. Each has an extensive record of application to technological and institutional change, which allows the present synthesis to build on settled conceptual foundations rather than improvised constructs. The three settings—corporate management, tourism, and the technology supply base—were chosen by theoretical rather than statistical sampling, that is, for the analytical contrast they provide. Corporate management is the general locus in which authority is delegated to agents and is therefore where intra-organizational redistribution is most visible. Tourism is an intermediation-intensive, experience-and-service sector in which global distribution platforms occupy core positions and destinations occupy peripheral ones, which makes core–periphery dynamics and value-capture asymmetries observable in a single value chain (Tussyadiah, 2020; Majid et al., 2023). The technology supply base is the set of actors that build the models, orchestration layers, and reference architectures and thereby shape the field itself. The three were not selected to represent the economy but to expose, respectively, the firm-level, global-systemic, and field-level mechanisms the framework predicts. The analytical procedure had four steps. First, the phenomenon and its diffusion were characterized from current technical and analyst literature (Wang et al., 2024; Gartner, 2024). Second, the phenomenon was read through each framework to derive analytical claims about capital redistribution, global asymmetry, and institutional convergence. Third, these claims were consolidated into propositions stated at the firm, sectoral, and global levels. Fourth, the design implications of the propositions were synthesized into a governance model. Throughout, secondary peer-reviewed and analyst sources were used to ground each claim and to discipline interpretation against the existing record on AI, labor, governance, and tourism (Acemoglu & Restrepo, 2020; Acemoglu et al., 2022; Jobin et al., 2019; Floridi, 2013). Two scope conditions bound the argument. First, the empirical referent is the enterprise context of 2024 and 2025; claims about a phenomenon at this stage of diffusion are provisional and time-indexed. Second, the propositions are interpretive and conceptual. They are offered as testable conjectures, not as established findings, and the analysis makes no claim to quantify the magnitude of the effects it describes. 5. The Reorganization of Power Within the Firm When a firm delegates action to agents, the practical question of who configures, supervises, and answers for those agents becomes a question of organizational power. Authority migrates toward the actors who control the data pipelines, the orchestration layer, and the governance apparatus, because these are the points at which an agent’s behavior is determined and defended. Budget and decision rights accrue to leaders who can demonstrate both measurable returns and credible control, while functions whose status rested on routine execution—manual reconciliation, sequential sign-off—lose ground. In Bourdieu’s terms, technical and governance competence is converted into cultural and symbolic capital, and the conversion rate favors hybrid profiles that combine domain knowledge with fluency in agent safety and oversight (Bourdieu, 1986). Proposition 1. The adoption of autonomous agents relocates authority toward actors who control the data, orchestration, and governance of those agents, converting technical and governance competence into cultural and symbolic capital and displacing functions built on routine execution. This redistribution is institutionalized in a recognizable set of practices: a policy layer that specifies permitted actions and escalation thresholds; an assurance layer of red-teaming and scenario testing; a telemetry layer of logs, rationales, and uncertainty measures; and oversight bodies with the authority to halt deployment. Within this emerging space, firms compete not only on what their agents can do but on their capacity to prove that the agents are under control. Demonstrable control—governability—becomes a stake in its own right and a resource that can be accumulated and displayed. Proposition 2. Agentic AI gives rise to a distinct organizational field of agent governance, with its own stakes and forms of capital, in which the demonstrable controllability of agents (governability) becomes a competitive resource alongside capability. The habitus of managers and staff shifts accordingly. Supervising software actors—reading dashboards, interpreting confidence scores, setting escalation rules—comes to feel as ordinary as reading a financial statement, while frontline work moves from execution toward the handling of exceptions and the refinement of policy. Human labor becomes more deliberative and synthetic and less repetitive, a recomposition consistent with evidence that AI reshapes the task content of work rather than simply eliminating jobs (Acemoglu et al., 2022; March, 1991; March & Simon, 1958). Proposition 3. The supervision of autonomous agents reconfigures the managerial habitus from the supervision of people toward the supervision of software, shifting human labor from execution toward oversight, exception handling, and policy specification. 6. Sectoral Dynamics: Management and Tourism 6.1 Management and operations In general management, agents compress the interval between a signal—a shift in demand, a supply disruption—and a response, and they reduce coordination latency across procurement, logistics, and service. Yet the language of efficiency can obscure a struggle over symbolic capital: which unit owns a decision, and whose metrics define success. When agent dashboards privilege a single class of indicator, typically cost, they can function as instruments of symbolic domination, naturalizing one coalition’s priorities as the firm’s objective. The analytical implication is that governance should distinguish agent-authorizable from human-reserved decisions, maintain a plurality of indicators across financial, ethical, and service dimensions, and submit the metric set itself to periodic review so that no coalition captures the definition of success (Floridi, 2013). 6.2 Tourism and hospitality Tourism makes the stakes unusually legible because its value chain spans a global core of distribution platforms and a periphery of destinations, operators, and residents. Agents orchestrate dynamic packaging, personalized itineraries, real-time service recovery, and revenue management on the demand side, and scheduling and resource allocation on the supply side (Tussyadiah, 2020; Majid et al., 2023). Read through world-systems analysis, the platforms that mediate distribution occupy core positions and capture intermediary rents, while peripheral destinations accept default rules that shape their visibility and pricing power and absorb the social and environmental costs of the visits these systems route to them (Srnicek, 2016; Couldry & Mejias, 2019). Proposition 4. In intermediation-intensive sectors such as tourism, agent-mediated distribution concentrates value capture in platform cores while externalizing social and environmental costs to peripheral destinations, unless agents are configured to encode local constraints. The same lens identifies the conditions under which the dynamic can be altered. Destination management organizations and operator associations can pool anonymized demand signals into regional data cooperatives and host local agent stacks that negotiate terms with core platforms and embed local norms—sustainability caps on fragile sites, local-vendor quotas, and resident quality-of-life constraints—directly into the objective functions of the agents that allocate demand. Where cultural capital in the form of local knowledge, language, and storytelling is translated into agent-readable constraints, destinations can convert it into symbolic value—experiences perceived as authentic—rather than surrendering identity to generic global templates (Bourdieu, 1986). The principal risk to manage is homogenization: the algorithmic flattening of distinct places into interchangeable inventory. 6.3 The technology supply base Technology suppliers concentrate economic capital in compute, data, and orchestration, and they accumulate symbolic capital by authoring the reference architectures that define what a safe agent looks like. Institutional pressures amplify this influence: as regulators, consultants, and professional bodies circulate the same templates, vendor designs become de facto standards (DiMaggio & Powell, 1983; Jobin et al., 2019). For buyers, the analytical implication is to treat governance capability as something to retain rather than fully externalize—favoring interoperable and open interfaces, maintaining second-source options to limit lock-in, and developing internal competence in agent-policy design and evaluation so that the capacity to govern does not migrate entirely to the supplier. 7. The Global Political Economy of Agentic AI Three levers determine advantage in the agentic economy: access to high-quality and lawful data, including operational telemetry; the compute required to train, adapt, and run agents at scale; and control over the standards that govern formats, safety taxonomies, and audit expectations. Core actors tend to hold all three, which places peripheral organizations in a position of data and standards dependency, where the cost of deviating from core templates is high (Wallerstein, 2004; Crawford, 2021). This is the mechanism by which agentic AI can reproduce, rather than merely reflect, global asymmetry: the more that planning, action, and assurance are anchored in core infrastructures, the more peripheral actors operate through systems they do not control. Proposition 5. Because foundational models, compute, and standards are concentrated among core actors, agentic AI tends to reproduce core–periphery asymmetries, positioning peripheral organizations as rent-paying users of core infrastructures unless countervailing arrangements alter their bargaining power. The periphery is not, however, without recourse. Public investment in compute and data infrastructure, procurement policies that require interoperability and portability, and semi-peripheral coalitions of universities, public laboratories, and industry consortia can co-develop contextual agents anchored in local languages and regulatory traditions (Mazzucato, 2013). Tourism again supplies a concrete illustration: itinerary and ranking agents that systematically favor high-rent segments would deepen value-capture asymmetry, whereas policy-aligned agents that encode sustainability constraints and redirect demand toward under-represented communities could make the same technology an instrument of more equitable distribution. The broader point, in Polanyi’s sense, is that agentic markets remain embedded in social and political arrangements that can be designed to constrain extraction rather than accelerate it (Polanyi, 1944). 8. Institutional Convergence and Governance Design Across these levels, organizations facing similar pressures arrive at similar arrangements. Regulation and dependency exert coercive pressure toward auditability, risk classification, human override, and traceability, which pushes firms toward common logging schemas and lifecycle controls. Uncertainty about returns and safety encourages mimetic imitation of the architectures of perceived leaders—typically a policy–reasoner–executor stack with safety filters and tiered autonomy. Professional communities in risk, law, and machine-learning safety exert normative pressure through standards, handbooks, and certifications, forming a shared professional vocabulary. The convergence on AI ethics principles documented by Jobin et al. (2019), accompanied by divergence in implementation, is the expected signature of these mechanisms operating in a field whose templates are not yet settled. Proposition 6. Coercive, mimetic, and normative pressures drive organizations toward convergent agent architectures and governance templates, reducing variance in how autonomy is governed and making conformity to emerging templates a condition of legitimacy. Convergence has a cost. Mimetic imitation that ignores context can flatten local distinctiveness, which is especially damaging in tourism and public services, and dependence on core templates can erode the internal capacity to evaluate and adapt them. A governance model is therefore useful not as a compliance checklist but as a means of retaining deliberate control while meeting legitimate external expectations. The seven layers in Table 1 organize the decisions that delegation to agents requires, from the definition of purpose to the management of learning. Table 1. A seven-layer model for governing autonomous AI agents. Layer Function Representative elements Purpose and scope Establish why agents are used and where they are barred Success metrics; out-of-scope decisions such as high-stakes employment actions Policy and constraints Define the boundaries of autonomous action Permitted actions; data access; rate, cost, and escalation thresholds Safety and assurance Test behavior before and during deployment Red-teaming; adversarial simulation; robustness checks under distribution shift Observability Make agent behavior legible and auditable Standardized logs of prompts, tool calls, and outputs; rationales; uncertainty estimates Control and intervention Keep humans able to direct and stop agents Graded autonomy (observe, propose, execute); checkpoints; emergency stop Accountability Assign responsibility for agent decisions Role mapping for approval, sign-off, and incident reporting; periodic board reporting Learning and adaptation Improve agents under controlled change Feedback from human overrides; post-incident review; change-controlled retraining Note. The layers are cumulative rather than sequential; each remains active throughout the agent lifecycle. The model is offered as an analytical organization of governance decisions, not as a maturity standard or compliance instrument. Read against the propositions, the model also clarifies the stakes of the field analyzed in Sections 5 and 8: the observability, control, and accountability layers are precisely where governability is produced and displayed, which is why competence in them functions as capital, and why their externalization to suppliers transfers power as well as work. 9. Discussion Taken together, the seven propositions support a final claim that integrates the three lenses. As agents act on behalf of organizations, external audiences—regulators, partners, boards, and the public—assess deployments less by raw capability than by visible evidence of responsible control. Audits, certifications, and governance disclosures become the symbolic markers through which legitimacy is granted, with the consequence that symbolic capital can become as decisive as technical performance in securing contracts and trust (Bourdieu, 1986; Jobin et al., 2019; Zuboff, 2019). Proposition 7. The legitimacy of agentic AI deployment increasingly depends on symbolic markers of responsible control, such that symbolic capital becomes a competitive resource alongside, and at times ahead of, technical performance. The contribution to each body of theory can now be stated precisely. To Bourdieusian organizational sociology, the article extends field and capital theory to the supervision of autonomous software and identifies governability as an emergent species of capital, showing that AI functions as a stake in field struggles rather than as an inert capability that firms simply hold. To the political economy of AI, it specifies the mechanisms—model control, compute concentration, and standard-setting—through which agentic systems can reproduce core–periphery asymmetry, connecting the organizational analysis to accounts of data colonialism and platform capitalism that have so far operated at the level of infrastructure rather than the firm (Couldry & Mejias, 2019; Srnicek, 2016; Crawford, 2021). To institutional theory, it applies the isomorphism framework to a technology whose governance templates are still forming, characterizing agent governance as a nascent field in which convergence pressures operate ahead of settled standards and in which the documented convergence-with-divergence pattern in AI ethics is the visible trace (DiMaggio & Powell, 1983; Jobin et al., 2019). The argument also speaks to a live debate about automation and work. Where the displacement literature measures the substitution of labor by machines (Acemoglu & Restrepo, 2020; Acemoglu et al., 2022), the present account reframes the question as one of authority: the central change is not only how many tasks are automated but how decision rights are redistributed among human and non-human actors and which actors accumulate the capital to govern them. For management and tourism scholarship, the practical reframing is that the strategic question is not whether to adopt agents but how to do so in ways that preserve plural objectives, local distinctiveness, and a fair position within the global division of computational labor. 10. Limitations and Future Research The study is conceptual, and its claims carry the limitations of conceptual work. The seven propositions are interpretive conjectures grounded in established theory and secondary evidence rather than findings from primary data, and the analysis does not estimate the magnitude or scope of the effects it describes. Its empirical referent is the enterprise context of 2024 and 2025; because the phenomenon is at an early stage of diffusion, the patterns identified are time-indexed and may shift as the technology and its regulation mature. The three settings were chosen for analytical contrast rather than representativeness, so the framework should be transferred to other sectors with care. These limitations define a research agenda. Comparative field studies could test Propositions 1 through 3 and 6 by examining how agent-governance fields and managerial habitus vary across sectors and regulatory regimes. Longitudinal labor research could trace the recomposition of work that Proposition 3 anticipates and identify the new forms of skill, precarity, or empowerment that follow. Cross-regional political-economy research could assess Propositions 4 and 5 by examining the conditions under which semi-peripheral coalitions, public compute, and regional data cooperatives shift bargaining power. Across all of these, the construct of governability proposed here—and its measurement as a form of capital—offers a tractable target for operationalization and testing. 11. Conclusion Autonomous AI agents are not only a technical advance but an organizational and geopolitical force that reorders who holds authority, how value is captured, and which actors can credibly claim legitimacy. Reading their rise through Bourdieu, world-systems analysis, and institutional isomorphism shows a redistribution of capital and the formation of a new field within firms, the reproduction of core–periphery asymmetries across them, and a convergence on governance templates that makes demonstrable control a condition of legitimacy. The article’s contribution is to integrate these levels into a single framework, to identify governability as an emergent form of capital, and to translate the analysis into a governance model and a set of testable propositions. The strategic and scholarly task that follows is to govern for difference: to build interoperable, audited, and human-aligned agent systems that honor local context while meeting the legitimate demands of a global field. References Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial intelligence and jobs: Evidence from online vacancies. Journal of Labor Economics, 40(S1), S293–S340. https://doi.org/10.1086/718327 Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716 Bourdieu, P. (1977). Outline of a theory of practice. Cambridge University Press. Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press. Bourdieu, P. (1990). The logic of practice. Stanford University Press. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton. Castells, M. (1996). The rise of the network society. Blackwell. Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data's relation to the contemporary subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632 Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101 Floridi, L. (2013). The ethics of information. Oxford University Press. Gartner. (2024). Top strategic technology trends for 2025: Agentic AI. Gartner, Inc. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2 Katsamakas, E., Pavlov, O. V., & Saklad, R. (2024). Artificial intelligence and the transformation of higher education institutions: A systems approach. Sustainability, 16(14), 6118. https://doi.org/10.3390/su16146118 Latour, B. (2005). Reassembling the social: An introduction to actor-network theory. Oxford University Press. Majid, G. M., Tussyadiah, I., Kim, Y. R., & Pal, A. (2023). Intelligent automation for sustainable tourism: A systematic review. Journal of Sustainable Tourism, 31(11), 2421–2440. https://doi.org/10.1080/09669582.2023.2246681 March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71 March, J. G., & Simon, H. A. (1958). Organizations. Wiley. Mazzucato, M. (2013). The entrepreneurial state: Debunking public vs. private sector myths. Anthem Press. Polanyi, K. (1944). The great transformation: The political and economic origins of our time. Beacon Press. Srnicek, N. (2016). Platform capitalism. Polity Press. Tussyadiah, I. (2020). A review of research into automation in tourism: Launching the Annals of Tourism Research curated collection on artificial intelligence and robotics in tourism. Annals of Tourism Research, 81, 102883. https://doi.org/10.1016/j.annals.2020.102883 Wallerstein, I. (2004). World-systems analysis: An introduction. Duke University Press. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., & Wen, J.-R. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 186345. https://doi.org/10.1007/s11704-024-40231-1 Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs. #AgenticAI #AutonomousAgents #ArtificialIntelligence #OrganizationalPower #CriticalSociology #Bourdieu #WorldSystemsTheory #InstitutionalIsomorphism #AIGovernance #PoliticalEconomyOfAI #FutureOfWork #DigitalTransformation #TourismTechnology #ManagementStudies #DataColonialism #PlatformCapitalism #ResponsibleAI

Authors: Huda Najjar 1 ORCID ID: 0009-0007-0765-6001, Mona Abdelmotaleb 2 ORCID ID: 0009-0005-9371-6263 1 Swiss International University (SIU), City of Osh, Kyrgyzstan 2 Swiss International University (SIU), ISB Academy, Dubai, UAE https://doi.org/10.65326/u7y566755 Received 20 January 2026; Revised 23 February 2026; Accepted 24 March 2026; Available online 7 April 2026; Version of Record 7 April 2026. Volume 3, December 2026, (10023) Abstract The fusion of Artificial Intelligence (AI) with Genomic Medicine has propelled Precision Medicine to new heights, yet the experiential and service aspects of how healthcare is delivered seem to be almost neglected. This review aims to look at the juncture of AI, Genomics, and the Hospitality approach to Healthcare, with a particular focus on the importance of the Patient Centric Approach to the current Medical Systems. Clinical applications, patient experience, the transformation of institutions, and the governance challenges have been reviewed in major databases such as Scopus, PubMed, Web of Science, and ScienceDirect. The results show that early disease detection, patient stratification, and predictive and personalized medicine genomics have been positively impacted. Simultaneously, a Hospital Hospitality approach has been proven to enhance patient engagement, communication, and the continuum of healthcare, ultimately improving healthcare. The review has brought to light the importance of an institution's preparedness, interdisciplinary collaboration, and the digital framework that makes it possible to integrate different healthcare systems. It also addresses governance issues concerning privacy of data, ethical issues, regulatory issues, and the control of AI in the health decision-making process. The study advances a call for transformation to Experience-Centered Precision Healthcare, where clinical and experiential elements of healthcare are addressed in an integrated manner. The findings enhance our understanding of the future of healthcare systems and the associated research and policy opportunities. In addition, this study proposes a novel analytical framework that integrates clinical, genomic, and patient experience variables into a unified data-driven model. The framework enables predictive and prescriptive analytics, supporting optimized decision-making in experience-centered precision healthcare systems. Keywords: Artificial Intelligence, Genomic Medicine, Precision Healthcare, Patient Experience, Healthcare Hospitality, Patient-Centered Care, Digital Health, Clinical Decision Support Systems, Personalized Medicine, Pharmacogenomics, Predictive Analytics, Healthcare Data Integration, AI Governance, Ethical AI, Value-Based Healthcare, Medical Tourism, Digital Health Ecosystems, Healthcare Service Quality, Experience-Centered Precision Healthcare, Healthcare Hospitality Analytics Framework. 1. Introduction The last few decades have seen extraordinary changes in the field of healthcare. Most noteworthy is the central role Artificial Intelligence (AI) is playing, particularly in the areas of data diagnostics, treatment, prevention, and data analysis. Merging AI, bioinformatics, and genomics analytics is allowing the development of framework models of precision medicine. This merger is also the reason for the changes in the development of algorithms for clinical decision-making across the facets of the healthcare and the clinical treatment system. The evolution of healthcare is also about the quality of clinical care. The healthcare system is taking on more of the principles of hospitality. Thus, quality of service, communication, and concern for comfort are becoming very, important and central features of healthcare. The paradigm of hospitality-oriented healthcare illustrates how patients should not be viewed solely as passive subjects of medical treatment; they encounter a multifaceted service experience. There is a growing service design focus on the patients' and caregivers' emotional experience, providing transparency and smooth interactions across all service touchpoints, especially in complex and emotionally charged areas of service delivery, such as genomic services. Though the importance of patient experience systems is still developing, the combination of hospitality-based services with advanced technology in the healthcare systems is still lacking research in the field. While extensive work has been done on AI and genomics in relation to their respective clinical and computational roles, little research has been conducted on them as related to service-oriented healthcare systems and patient experience. This paper addresses this gap by combining a structured narrative review with the development of an analytical framework that integrates artificial intelligence, genomic medicine, and hospitality-oriented healthcare into a data-driven decision-making model. I will specifically focus on how these systems integrate to improve healthcare delivery along the spectrum of clinical use, patient experience and service delivery, and the relevant governance and ethical issues. I hope to provide insight to the body of work relevant to the next generation of healthcare systems that combines advanced scientific precision and a focus on the human side of health care. 2. Review Methodology Here, a detailed narrative review approach is utilized to analyze the literature at the convergence point of artificial intelligence, genomic medicine, and healthcare focused on hospitality to pinpoint principal themes, trends, and the areas of research deficiency in the clinical, organizational, and experiential dimensions of the metamorphosis of the healthcare system. 2.1 Search Strategy The research encompassed a systematic exploration of the most relevant scholarly literature in the Scopus, PubMed, Web of Science, and ScienceDirect databases. The formulation of relevant literature consisted of three principal areas: - Artificial intelligence in healthcare (e.g. “AI in medicine”, “machine learning healthcare”, “clinical decision support systems”) - Genomic medicine (e.g. “genomics”, “precision medicine”, “pharmacogenomics”) - Hospitality and patient experience (e.g. “patient-centered care”, “healthcare service quality”, “hospitality in healthcare”, “medical tourism experience”) The focus of the study was to capture/pinpoint literature in the three domains that encompass the relevant literature. A combination of Boolean operators was used in conjunction with the keywords to enhance the relevant literature. 2.2 Inclusion and Exclusion Criteria The following inclusion criteria were used for the selection of studies: - Conference papers of good quality and journal articles that are peer-reviewed. - Publications that discuss the implementation of AI in healthcare and/or genomic medicine. - Publications that discuss patient and healthcare service quality, and hospitality studies in the healthcare domain. - Articles published in English. Exclusion criteria included: - Publications that focus on the healthcare field but solely on the development of algorithms of a technical nature. - Scholarly articles that are not peer-reviewed, and that are of a lesser academic quality such as, opinion editorials. - Articles that discuss fields outside the healthcare and biomedicine domains. 2.3 Screening and Selection Process The first set of results included a wide range of articles, which were subsequently screened based on title and abstract relevance. The selected studies were subjected to full-text reviews to confirm that they aligned with the aim and objectives of the study. To enhance the clarity and diversity of perspectives in the study, overlapping and redundant studies were removed. 2.4 Data Extraction and Thematic Analysis The literature was analyzed using thematic synthesis. Relevant details extracted included: - AI’s type of application - Focus on clinical or genomic - Patient experience/service-related - Institutional or system-level - Governance, ethical, or regulatory Findings were classified under thematic areas that align with the review’s primary sections: clinical applications, hospitality-oriented patient experience, institutional dynamics, and governance issues. 2.5 Review Limitations The review encompassed most relevant studies. Ongoing rapid changes in AI and genomic medicine mean the included literature may miss new changes. The multidisciplinary and poorly defined concept of hospitality in healthcare also poses challenges in study classification and interpretation. 3. Artificial Intelligence and Genomics in Precision Healthcare One of the most significant shifts in modern medicine is the combination of artificial intelligence (AI) and genomics, positively redefining the scope of patient treatments from universal methods to specific, data-backed, and actionable techniques. Healthcare, focused on precision genomics, aims to enhance diagnostics, treatments, and preventative care, including the use of artificial intelligence, which helps clinicians conduct complex analyses of genomic data and facilitates the clinical application of diverse genomic data. 3.1. Genomic Medicine and Precision Healthcare Genomic medicine has to do with the examination of an individual’s genetic material to determine the individual’s likelihood of developing particular diseases, how diseases will progress, and how the individual will respond to particular treatments. With the advent of new high-throughput sequencing methods, especially next-generation sequencing (NGS), the genomic analytical methods of the past have been replaced when it comes to the rapid and economical generation of new genomic data. The challenge, however, for the new data is to use traditional analytical techniques to extract important and useful information. Precision health goes further than genomic medicine, as it combines genetic, environmental, and lifestyle factors together. A more broad approach shifts the focus toward highly advanced computing that utilizes genomic sequencing, EHRs, imaging, and real-time monitoring of the patient, as well as other forms of data to make integrated clinical decisions. 3.2 The Importance of AI in Analyzing Genomic Data Machine learning and deep learning, both aspects of AI, are important in solving the computational problems associated with genomic data. AI has strengthened disease prediction, analysis, and risk stratification due to its abilities within large data sets to understand complex non-linear interrelationships. Disease-causing genomic variants are predicted using supervised learning. On the other hand, unsupervised learning has the ability to uncover the novel genetic structures and disease subtypes. Genomic sequence analysis, variant calling, and functional annotation are some areas within which deep learning (e.g. CNNs and RNNs) has shown great promise. AI-assisted multi-omics data integration (genomics, transcriptomics, proteomics, and metabolomics), allows for in-depth analysis of biological data. This comprehensive approach improves biomarker discovery and therapeutic targeting. 3.3 Predictive Modeling and Support for Clinical Decisions Systems for clinical decision support (CDSS) powered by AI combine clinical and genomic data in decision making. They help improve clinical efficiency and decrease care variability by assisting with real-time diagnoses, treatment recommendations, and risk management. AI adds predictive modeling, informing earlier disease discovery and determining patients at risk. Predictive algorithms, for example, analyze genetic predispositions in patients and inform prevention, and personal treatment for cancers, cardiovascular diseases, and rare genetic disorders. AI helps in pharmacogenomics, the science of how genes affect a person’s response to drugs. The combination of AI and pharmacogenomics has improved the ability to find the right medication and the right dosage for a patient to reduce side effects and optimally enhance the needed effect. 3.4 Integration Challenges in AI and Genomics Although the combination of AI and genomics can be very valuable, other challenges need to be handled. The AI models' performance and general application can be affected due to non-uniform data, lack of standardization, and concerns relative to the data's quality. Further, clinical settings need to be precise, open, and easy to understand, as there are many regulations for them, and the same goes for AI's ability to help in its interpretation. AI-enabled genomic tools face ethical issues such as data privacy, data consent, training data set biases, data set training biases, etc. Solving these problems will need collaboration between technological, regulatory, and clinical approaches. Fig. 1. Hospitality-oriented patient journey in AI-enabled precision healthcare systems. This figure depicts the fusion of the multi-omics data and DNA sequencing genomic data sources with the processes of artificial intelligence, machine learning, and deep learning to form actionable insights. This framework illustrates the steps involved in the transformation of unprocessed biological data into actionable diagnosis, predictive analysis, and therapy decision in precision healthcare systems. 4. Clinical Uses and New Therapeutic Pathways The clinical usage of AI and genomic medicine has impacted almost all facets of healthcare, including the early diagnosis and prediction of ailments, the personalization of therapy, the management of patients over extended periods, and the integration of biological data with computational intelligence, which has yielded impressive patient results and enhanced the quality of the healthcare system. 4.1 Predicting disease risk and diagnosing problems early One of the greatest advancements AI provides in genomic studies is predicting problems and diagnosing them early. AI models study the genetic variations and the different patterns linked to the problem in order to determine the likelihood of an individual having the disease without considering the clinical symptoms. This is most useful when studying complex problems such as cancer, diabetes, and neurodegenerative diseases. Tools designed by AI can detect slight genomic variations that traditional methods cannot diagnose. When problems are diagnosed early, not only is the chance of survival increased, but the problem can be addressed more easily. This also saves time and resources for the healthcare system as the disease can be treated less invasively. 4.2 Personalized treatment and patient stratification When stratifying patients, AI studies complex datasets to help assign patients into different groups in order to deliver a more accurate and effective treatment. A good example of this is when specific mutations are examined in the field of oncology. This allows for the identification of mutations that can be used for targeted treatments, decreasing the use of broad therapeutic measures and increasing the chances of positive treatment. 4.3 Use of AI in Pharmacogenomics and Treatment Optimization AI’s capacity to improve the precision of drug delivery through the integration of pharmacogenomics and temperature measurement is of utmost importance. AI technology assists in the selection of appropriate drug types and dosages based on the user’s unique genetic composition and the resulting variations in metabolic processes. Pharmacotherapy personalized in this manner not only increases the overall efficiency of treatment, but also mitigates the likelihood of an individual experiencing adverse reactions. Furthermore, AI develops predictive algorithms that are continually Updated for the avoidance of out-of-date practices. These algorithms are designed to improve treatment recommendations based on the user’s current clinical data. 4.4 Genomic Diagnostics and Identification of Rare Diseases AI’s use in genomic analysis to identify specific, previously unrecognized genomic variations associated with rare diseases increases the likelihood of diagnosing these diseases, given the complexities and variations associated with rare diseases. Advanced genomic analysis algorithms significantly increase the likelihood of identifying previously unrecognized causative mutations in large volumes of genomic data. This results in a significant increase in the speed of diagnosis and the subsequent initiation of treatment. This is crucial in the diagnosis of rare diseases in the pediatric population as well as in inherited diseases. The impact of early diagnosis can be devastating. 4.5 Models of Healthcare that are Predictive and Preventive The use of AI and genomics facilitates routine healthcare engagement, as they can help make predictions and forecasts that are used to guide interventions, as opposed to making predictions and forecasts that are used to guide reactive responses. Predictive models evaluate the risk factors of an individual and provide recommendations with respect to certain diseases. Predictive models provide recommendations concerning positive changes in lifestyles, monitoring and treatment interventions, and suggest other diseases that may require prophylactic measures. This handles the overarching goals of the healthcare system by incorporating further insight into the integration of patient engagement and health management plans. 4.6 Limitations and Barriers to Clinical Translation There are numerous factors impeding the integration of AI and applications for genomics, including regulation, lack of guidelines for interoperability, and poor infrastructure. The successful integration of these technologies is hinged as much on the technology as on the acceptance of the practitioners, the trust of the patients, and the preparedness of the institution. The gap that exists between clinical practice and research is particularly relevant for future work in precision medicine. 5. Hospitality-Oriented Patient Experience in Healthcare Systems The genomic revolution and the advancements in AI in healthcare necessitate a transformation in the way healthcare is provided to patients. While AI has the potential to revolutionize the ease and efficacy of patient engagement, the healthcare system needs to be concerned with the ease and efficacy of the patient experience. The application of hospitality-oriented principles to the design of the healthcare system is a very significant step to attain a more human, service-oriented system. The hospitality approach includes patient satisfaction, but goes further to include the entire service delivery process with an emphasis on personalization, communication, emotional connection, and seamless continuity of care. It focuses on the recognition of the service continuum, which includes many touchpoints and players, as well as the experiential aspects of care and the delivery process which is often overshadowed by the clinical aspects. 5.1. Understanding the Hospitality Approach in Healthcare The application of hospitality in healthcare draws on the service management paradigm. This approach conceptualizes the patient as an active participant in the process rather than a passive recipient of the outcome. The major components of the hospitality approach in healthcare include: - Personalization of healthcare services - Timely and responsive service delivery - Open and trust-building communication - Care for the service user’s clinical and emotional well-being - Care and service continuity across the healthcare continuum These components are important in healthcare services delivery especially in the environments tailored for precision healthcare, as patients in these settings are subjected to prolonged and iterative cycles of complex diagnostics, therapies, and services. 5.2 Patient Experience as Both a Clinical and Operational Outcome Health care performance has traditionally centered on clinical outcomes such as survival, complications, and the efficacy of treatment. However, given the impact of patient experience on health outcomes, treatment adherence, and overall satisfaction, there is now emphasis on the importance of patient experience as a factor in determining the quality of health care. Towards this end, patient experience can be enhanced by hospitality-driven-facilitated approaches that improve functionality and access to healthcare services. For example, anxiety may be reduced and patient engagement improved by providing clear instructions on what to expect during genomic testing, how the results will be interpreted, and what the treatment options will be. In addition, well-designed care pathways that eliminate unnecessary waiting times, reduce administrative burdens, and minimize steps in the process will improve the experience of care. The increasing evaluation and accreditation of health services against the patient experience dimension is a reflection of its importance to performance and quality in clinical and operational assessments. Fig. 2. Hospitality-oriented patient journey in AI-enabled precision healthcare systems. This patient journey model in precision healthcare highlights hospitality and the use of artificial intelligence and genomics at different points of care. The model focuses on patient engagement and emphasizes the personalization of communication, the use of digital tools, and service design in the design of diagnostic, therapeutic, and post-care processes. 5.3 Personalization and Patient Engagement Fueled by AI AI has an important role in the implementation of hospitality-oriented healthcare as it offers advanced personalization and patient engagement. AI systems provide the ability to personalize communication, appointment scheduling, and care recommendations based on patient data analytics and individual clinical needs. AI hospitality applications in healthcare include: - A real time virtual assistant and a chatbot for appointment bookings and information provision - A patient portal embedded with issued genomics for personalization of treatment and progression monitoring - A predictive engagement system, including data and information to arrive at a timely, appropriate, and relevant patient intervention - Enhanced communication tools using natural language processing between patients and healthcare practitioners Presently, operational efficiency and the responsiveness and patient-centered system have a renewed focus. 5.4 Application of Hospitality Principles in the Pathways of Genomic and Precision Medicine The benefit of applying hospitality principles is particularly important to patients in genomic medicine, where they often deal with an emotionally difficult and complex and uncertain scenario. After genomic testing, patients may face precision therapies that require extensive data analysis, ethical dilemma, and planning for care over a long period. In such contexts, hospitality-driven approaches can be beneficial to: - Increase patient education and counseling and aid in the understanding of genomic information. - Provide emotional and psychological support and address the anxiety concerning genetic risks and diagnoses. - Provide coordinated care pathways involving integrated multi-specialty and multi-service approaches. - Support clear, open, and transparent communication concerning risks and uncertainties while establishing trust and attaining informed consent. Combining clinical excellence with service-oriented approaches enables healthcare providers to improve the effectiveness and the accessibility of genomic interventions. 5.5 Medical Tourism and International Patient Services The fusion of hospitality and healthcare is particularly pronounced in medical tourism, where patients travel to other countries for treatment. Here, healthcare providers must blend clinical care with hospitality and provide not only excellent medical care but also full travel, accommodation, and culture adjustment support. This area is also benefitting from AI and digital technology in the following ways: - Facilitating cross-border patient coordination and communication. - Enabling remote consultations and assessments prior to treatment. - Creating customized service bundles that incorporate both clinical and non-clinical services. More and more hospitals and specialized clinics are adopting a hospitality-centric approach in order to attract foreign patients, thereby making the patient journey an important competitive differentiator. 5.6 Challenges and Limitations in Implementing Hospitality-Oriented Healthcare While there are possible upsides for using hospitality-based services in healthcare, there are also many obstacles, such as: - Cost cutting measures in public healthcare systems where funding is minimal - Volume clinical personnel environments where there is a clash between efficiency and personalization - Differences in culture and level of expectation are a factor in the understanding of the quality of services and patient experience - The danger of excessive commercialization, where hospitality overshadows clinical needs Furthermore, concerns about data privacy, algorithmic bias, and the potential for depersonalization of care by AI are important factors to consider when implementing AI personalization. 6. Institutional Dynamics and Service-Delivery Transformation AI, genomics, and models of hospitality-oriented care will require transformational change on the Institutional level in healthcare services. It will be necessary for healthcare organizations to move from a clinical base to one that is adaptable, data-driven, and more service-oriented so that quality clinical and data-driven systems are also patient experience and satisfaction systems. The organizational dynamics of an institution are all of the factors that describe the state of the organization in regards to its structural, human resource, and multidimensional integrated knowledge capacity. The merging of new technology and service-based care models creates additional complexities and prompts health care organizations to rethink their structure, processes, and value propositions. 6.1 Organization Readiness and Digital Transformation The readiness of an organization to adopt AI-driven genomic-based medicine and care is highly influenced by its organizational readiness. This organizational readiness is directly implicated in the success of the digital transformation of culture and structure in addition to technology. The primary components of organizational readiness include: - Digital systems and infrastructure with the capacity to support genomic and clinical data at scale - Systems that are constructed to be interoperable and can support the seamless and integrated flow of data across various departmental and functional systems - Innovative and strategically aligned leadership to foster change. - Change management systems that support new models of care through the facilitation of change. Healthcare models that integrate precise medicine with improved care and patient experience are the focus of organizations that are successful in the integration of the aforementioned elements. 6.2 Interdisciplinary Collaboration and Workforce Development The intersection of AI, genomics, and hospitilization is highly interdisciplinary. New collaborative models are forming and emerging as the functional and technical and administrative roles blend in new and different ways. Healthcare systems should promote collaboration between: - Clinicians and medical practitioners - Data scientists and engineers of artificial intelligence - Specialists in genetics and laboratory personnel - Administrators of healthcare systems and designers of service systems. Furthermore, building the workforce becomes essential. Healthcare professionals need to be trained in data literacy, digital tool literacy, and patient communication. On the other hand, the clinical staff of the system should be able to understand the system's clinical functions and processes to assist in the effective and hospitality-oriented service delivery. 6.3 Data and Infrastructure Ecosystems The primary integration of artificial intelligence and genomic medicine is dependent primarily on superior infrastructure and data ecosystems. Healthcare institutions should focus on: - Advanced and secure storage systems for genomic and clinical data. - Computing systems with advanced performance for the training and deployment of AI models. - Integrated data systems for genomic and clinical data, and patient-generated data. - Cyber systems for the protection of data. Also, the integration of data is a significant challenge for AI. This involves the fragmentation or the division of data across and within systems and institutions. This is critical for analyzing large data systems, coordinating multiple caregiving systems, and providing data for a specific purpose. 6.4 Service-Delivery Models and Patient Pathways The principles of hospitality in healthcare necessitate the alteration of service-delivery models and patient pathways. When developing care processes, institutions must ensure that the processes are clinically sound as well as structurally simple, efficient, and flexible to the needs of the patient. Transformed service delivery models are characterized by the following: - Integrated care pathways that help in the reduction of fragmentation between various departments and services - Patient navigation systems that help to streamline the patient health care experience - Delivery of care via telehealth and hybrid models - Patient services that are delivered in ways that are most aligned to patient needs as well as the clinical requirements. The improvement of patient experience and outcomes are features of models that emphasize care continuity and ease of movement between various levels of care. 6.5 Innovations in the Private Sector and the Medical Tourism Ecosystem Private health care providers and specialized clinics are often the first to adopt innovative models that marry clinical sophistication and services driven by hospitality. In particular, those institutions that have a place in the medical tourism ecosystem have developed highly sophisticated models of service delivery that integrate medical care with travel, accommodation, and support services, which are highly personalized. These institutions have employed artificial intelligence and other digital innovations to: - Facilitate the coordination of international patients - Conduct remote consultations and asynchronous follow-up care - Deliver personalized treatment packages - Enhance efficiency and effectiveness in the use of resources and the delivery of services The focus of the patient experience as a point of innovation is driven by the competitive nature of the market of private health care. 6.6 Barriers To Institutional Change The integration of AI-supported genomic medicine with hospitality-centered care has considerable potential to enhance patient care. However, health care institutions face a number of barriers that include: - The significant costs associated with the implementation of new technologies and advanced systems - Resistance to change by all the stakeholders involved, especially in the case of the healthcare professionals and administration - Legislation that imposes restrictions on The implementation of alternative service delivery and Advanced technology utilization - The absence of adequate governance in relation to the challenges of data sharing and interoperability - A shortage of workforce in areas such as genomics, data scientist, and other The challenges outlined above is the outcome of the lack of collaboration organizational, regulatory, and technological areas as well as the need for integrated efforts to develop capacity and to enhance innovation in the areas. 7. Issues on Governance, Ethics and Regulations The application of AI, genomic medicine and the hospitality approach to healthcare delivery raises complex issues on governance, ethics, and regulations, which must be considered to ensure that health care is offered in a safe, equitable and trustful manner. The ability of modern technology to provide health care in a highly personalized manner through sophisticated data analysis also raises serious concerns about privacy, accountability, and fairness, as well as the need for balanced innovation and adequate control. Robust governance frameworks are critical to capture the nuances of the development, execution, and regulation of AI-gene driven systems, especially in settings that prioritize the patient journey and personalization of services. Fig. 3. Institutional and governance framework for AI-driven genomic and hospitality-oriented healthcare systems. The illustration depicts the interconnection of various strata of clinical technologies, institutional frameworks, and governance systems in AI, genomic medicine, and hospitality integrated healthcare systems. It demonstrates the interplay of various elements, technological, managerial, and regulatory, for the collaborated sustainable and ethical use of the system. 7.1 Data Privacy, Security, and Ownership Considering the breadth of detail genomic information reveals about a person's biological makeup, health, and relatives, it is particularly sensitive and personal. The associated risks of privacy and data security are significantly increased when AI is applied to analyze genomic data. The significant data protection challenges are: - Breaches and unauthorized access of genomic data - How large datasets are stored and transferred securely - Issues of data ownership, especially with cross-border and multi-institutional collaborations - The impact of data ownership on patients, and whether their consent has been obtained. While personalization of service in healthcare models is valuable, the data collected must have appropriate governance structures in place to mitigate the threats of personal data overexposure and misuse. 7.2 Ethical Issues of AI in Genomics Several ethical issues arise from the use of AI in genomic medicine, especially in regard to the fairness of health services, their transparency, and patient autonomy. The inadequate training of AI models has the potential to amplify health disparities due to biased data. The following ethical issues are apparent in the use of AI in genomics: - Bias in AI systems, which compromises the accuracy of diagnoses and recommendations for treatment. - The opaque nature of some AI systems. - The AI systems that healthcare professionals use to make decisions and then do not explain their logic. - The degree of autonomy that patients are afforded when automated decision-making systems are used. Even though the principles of hospitality and personalization may influence the service model of care, the ethical issues surrounding the use of AI in genomics must be prioritized to build trust and communication with patients and foster their active participation in care. 7.3 Regulatory Frameworks and Compliance The safe and effective application of AI and genomic technologies in healthcare relies on regulatory bodies; that said, regulatory frameworks tend to fall behind the speed of innovation. Challenges related to regulation include: - Approval and verification of AI medical tools - Standardization of genomic tests and interpretations - Regulatory divergence, especially within medical tourism - Compliance with data protection laws, such as GDPR and other regional legislation The addition of hospitality service features increases complexity, as healthcare providers have to comply with regulations related not only to medical practice but also to service, international patient management, and online services. 7.4 Responsibility and AI-Driven Clinical Reasoning The incorporation of AIs into clinical reasoning processes also raises questions of who is accountable and liable when AI systems provide recommendations or support that result in negative outcomes. Concerns include: - Determining responsibility for actions taken by clinicians, developers, or organizations - AIs not being held liable for mistakes in their predictions or recommendations. - Ex-ante and ex-post controls to ensure that an AI system receives adequate human override. For trust in healthcare systems to incorporate AIs and for patient safety to be an enduring priority, responsibility must be clear when control is established. 7.5. Equity, Accessibility, and Global Disparities AI and genomic medicine present rocky paths for the future. With genomic medicine and AI, more advancements could be created; however, the more advancements that are created, the more that are exclusively available to certain populations and regions. The healthcare system could be at risk of falling behind due to the inability to provide proper infrastructure, high costs, and unequal technology. Additionally, in hospitality-oriented healthcare models, there is the potential that enhanced services will be centered around private or upper-class institutions. This may lead to the establishment of a segmented system that provides improved services within healthcare to a select group of patients. Such concerns can be addressed by the following: - Design policies that are fair to all in order to ensure advanced healthcare technology is available to all. - Establish frameworks and develop capacity in low and middle-income countries. - Establish collaborative frameworks to integrate and disseminate best practices. 7.6. Trust, Transparency, and Patient Engagement Trust is a key issue, especially in a healthcare system. With the sensitive and sophisticated use of consumer data, trust becomes more imperative. Transparency, especially with AI, how data is used, and how algorithms are utilized, is important for patients to trust the system. Trust can possibly be informed with a hospitality-engaged approach by: - Enhancing communication and access to information - Giving detailed explanations of processes and their results - Encouraging patients to take part in decision-making However, a balance of technology with human-centric communication is very critical to achieve this. 8. Future Directions for Hospitality-Oriented Precision Healthcare The combination of AI, genomic technology, and hospitality healthcare is a developing stream that will impact healthcare systems worldwide. Healthcare technology is rapidly evolving, and providers' and consumers' needs are changing. Models for delivering healthcare are anticipated to become more integrated, individualized, and experience-oriented. This chapter highlights important factors that will most likely characterize the upcoming era of precision healthcare. 8.1 Integrating Multiple Flexible and Real-Time Data Systems The next healthcare systems will focus more on integrating multiple Flexible data systems, including genomic data, clinical data, data from wearable devices, and data from people. AI will help analyze data from diverse flexible systems in real-time. This will allow for continuous and adaptive monitoring and decision-making. The integration will promote the following: - Prompt identification of potential health problems from ongoing streams of data. - Develop adaptive patient-centric treatment plans responsive to the evolution of the patient’s condition. - Improved collaboration among various health care professionals. The emerging paradigm of data-driven health care promotes real-time collaboration, analogous to the tenets of care-centered hospitality, as it allows for the delivery of more tailored and individualized services. 8.2 AI-Augmented Patient Engagement and Experience Design The next stage of patient-centered healthcare will incorporate AI-augmented systems aimed at improving engagement with patients during their care journeys. Such systems will go beyond simple automation and utilize advanced models of communication that predict patient needs and preferences. Possible emerging developments can include: - Smart Virtual Health Assistants with personalized navigation abilities - Emotion AI that responds to patients by varying communicative styles - Integrated seamless digital systems that combine clinical data and service streams - Engagement through anticipation and prediction of patients’ personalized care pathways These innovations will produce healthcare systems that are more user friendly and less frustrating. Patients will feel empowered, and the system will provide transparency and continuity. 8.3 Future of Global and Cross-Border Healthcare Ecosystems The globalization of healthcare services, especially medical tourism and the care of overseas patients, is facilitating the emergence of cross-border healthcare ecosystems. These ecosystems incorporate digital and AI-enabled remote access to consultation, treatment, and follow-up. Key trends may include the following: - -the creation of a standard approach to care across various countries - -the utilization of digital means to coordinate patient pathways across several international borders - -the fusion of clinical environments and service settings - -the emergence of specialized centers that integrate clinical services and hospitality These trends combined reinforce the importance of hospitality in a competitive health care system. 8.4 Ethical AI and Responsible Innovation Frameworks As AI systems are increasingly integrated into health care management, innovation in ethical frameworks and responsible AI systems will be a priority. Innovation and development in systems management must be appropriate to societal needs, the rights of patients, and the regulations. Future development will be focused on: - -the improvement of transparency and explainable AI - -the creation of guidelines pertaining to ethical AI systems in health care - -the advancement of privacy and the protection of data - -the improvement of accessibility to advanced technologies The incorporation of a hospitality perspective may also support trust, collaboration, and empowerment of patients. 8.5 Experience-Centered Precision Healthcare A significant prospective focus area involves moving from singularly precision-based medicine to an experience-centered precision healthcare system. Here, clinical greatness is accompanied by outstanding patient experience. In this paradigm, success is defined not just by clinical outcomes, but also by patient experience, satisfaction, engagment, and overall wellbeing. This paradigm shift models success on: - Integration of clinical, technological, and service design strategies - Persistent assessment of patient experience - Human-centered design healthcare system integration The integration of all these components can transform the delivery of healthcare by making patient experience a primary focus of value. 9. Economic and Value-Based Implications of AI-Driven Precision Healthcare The fusion of artificial intelligence (AI) and genomic medicine within healthcare systems has significant economic impacts including new patterns of costs, value, and sustainability over time. Although these technologies pose considerable promise to improve clinical outcomes and operating efficiency, their implementation also presents new financial, organizational, and policy challenges. This section constitutes economic aspects of AI-precision healthcare, particularly in relation to value-based care and hospitality-related services. 9.1 Cost Structures and Investment Requirements The first steps needed in the implementation of AI-driven genomic medicine is the construction and installation of multiple, extremely advanced technological facilities. These include offerings support advanced interfacing with genomic data bases, advanced genomic data analysis, and more. In addition, facilities must be supported with advanced genomic sequencing offerings, for which there are only a few available alternatives. Also, additional personnel must be trained to operate future digital offerings, plus additional staff must be hired to perform future digital offerings. Finally, personnel, on an ongoing basis, must be recruited, trained and employed to integrate, maintain and support future digital offerings. Barriers to adoption, especially for smaller health systems and for those in developing countries, are understandable given the upfront costs involved. With widespread adoption of new technologies, developing countries will also see further gains in cost effectiveness from the development of new technologies as well as from the utilization of economies of scale. 9.2 Economic sustainability The high initial costs typical of new technologies, and in this case AI driven precision health, new technologies can bring about large, perhaps incalculable, savings in the future. The costs associated with late stage treatments and hospitalizations can be avoided and even eliminated through early disease detection and predictive analytics. Resource costs can be reduced and fully utilized by eliminating ineffective treatments through personalized strategies. Furthermore, by minimizing the number of diagnostic errors and streamlining workflows, AI decision-systems can increase the efficiency of clinicians. All of the above can lead to the cost sustainability of health systems. 9.3 Optimizing Outcomes and Value-Based Healthcare The shift to value-based healthcare focuses on achieving the best outcomes for patients relative to costs. AI combined with genomic medicine allows for the achievement of optimum clinical outcomes as a result of a personalized and data driven approach. Health systems that are designed for the hospitality of patients, also increase value by enhancing the experience, participation, and adherence of patients to the treatment protocols. The experience of patients in the system is growing in its significant contribution to the value of the healthcare system, and determining clinical outcomes and the performance of the health system as a whole. Healthcare providers can merge clinical efficiency and service satisfaction to forge innovative value-oriented offerings to meet the changing needs of patients and the policies that guide them. This is the value of innovative clinical services, created with the patients’ needs in mind and offered to them with empathy. 9.4 Market Competitiveness and Medical Tourism The use of new technologies, especially information technologies, in combination with services delivered with a hospitality attitude, has a positive impact on the competitiveness of the market, and especially in the private health care services market and in the market of patients travelling abroad for medical care. Healthcare facilities offering a combination of precision medicine and high level of hospitality to patients will attract more patients, both domestic and international. The intersection of these elements is most evident in medical tourism. Healthcare providers that offer patients tailored treatment and accommodation, as well as patient support services, will stand out in the highly competitive international marketplace. 9.5 Economic Inequality and Access Challenges The full potential of AI precision healthcare is enormous. However, with this potential comes the harsh reality of AI precision healthcare exacerbating current inequalities in healthcare. The combination of significant costs of advanced technologies and enhanced service ecosystems will limit success to a privileged few, leaving many without the necessary level of care. Policy measures must focus on improving accessibility through the responsible use of public funding, expanded insurance coverage, and the development of affordable care technologies. The accessibility of a precise health care system will be one of the critical aspects in sustaining a sustainable health care system. 9.6 Prospective Economic Models and Sustainable Systems in Healthcare Future prospective models of economics in healthcare emphasize the incorporation of technological advancements and innovation blended with value-based and patient-centered care. Value-focused AI-enabled genomic medicine and service design in healthcare as the hospitality industry does will transform the creation and delivery of value in the healthcare systems. Sustainable models in healthcare will demand integrating technological potential with economic and policy structures. This includes innovative frameworks of reimbursement with value clinical outcomes and patient experience and sustained digital and human resource development infrastructure. 10. Digital Health Ecosystems and Platform Based Healthcare Models The shift to digital ecosystems in medicine signifies a major change in how medicine is practiced, coordinated, and experienced by all stakeholders. Clinical practice, patient interactions, and service delivery platforms are integrated with Artificial Intelligence (AI) and genomic technologies in the rapidly emerging interconnected digital environments. These ecosystems are providing the means to achieve more flexible, scalable, and patient centric models of delivery, combining clinical excellence with hospitality-oriented service design. 10.1 The Emergence of Digital Health Ecosystems By integrating different stakeholders, technologies, and data into a single platform that offers continuous and coordinated health services, digital health ecosystems are transforming the delivery of health services. Health providers, laboratories, patients, insurers, and health technology companies are all connected in digital health ecosystems. One of the most important components that digital health ecosystems rely on is AI driven genomic medicine. By generating actionable items from complex biological data, digital health platforms assist in healthcare delivery. This functionality fosters an integrated approach to health systems, as opposed to the traditionally fragmented systems. 10.2 Models of Healthcare Delivery via Technology Platforms As part of its evolution during the period of digital transformation of the healthcare sector, the healthcare services provider is able to create centralized systems to manage the delivery of services, the collection and management of relevant data, and the interaction of all relevant stakeholders, including clinical and non-clinical staff and patients. This integrated system improves the coordination of all healthcare services and optimizes the patient journey. Such models facilitate the: - Integration of electronic health records, genomic data, and real time monitoring systems - Virtual healthcare and telemedicine - Centralized management of scheduling, communication, and patient navigation - Digital patient engagement The consolidation of the functions and services above in a single system improves the accessibility, efficiency, and continuity of care, in part, because of the incorporation of digital health tools. 10.3 Convergence of Artificial Intelligence, Genomics and Patient Engagement Tools The combination of the Artificial Intelligence and genomic medicine in digital platforms provides the health system with the basis for the development of smart systems of health care ability that will make it possible to provide care that is personalized and flexible to the individual’s needs. The patient engagement tools, including mobile applications, patient portals, and health management systems, are the primary user engagement systems and the primary clinical data systems. The following Artificial Intelligence technologies will make significant contributions: - personalized health recommendations based on genomics and clinical data - predictive alerts and risk assessments. - automated communication and follow-up systems. - decision support to patients and healthcare providers. This integrated approach will further improve clinical outcomes and patient satisfaction, as it will provide a framework for timely access to relevant patient care. 10.4 Hospitality Centered Design in Digital Platforms Applying hospitality-oriented strategies in the digital health ecosystem expands the usability and accessibility of the health care system. More focus on user-centered design, and personalized interaction will lead to a greater response and support from the patient population and strengthened system. Digital platforms may provide additional hospitality by offering: - Personalized dashboards monitoring health and health-related activities - Streamlined access to and from all levels of service and care - Provision of service in the patient\'s language and in a culturally appropriate manner - Integrated service provision in clinical and non-clinical areas All of the above features aid in transforming digital health care platforms into functional service environments. 10.5 Barriers to Integration and Interoperable Data Despite the positive implications of a digital health ecosystem, many barriers still exist. A focal problem to be considered in the digital health ecosystem is interoperability. Significant barriers include: - High-demanded data format and protocol standards - Disordered healthcare information systems - Integration aspects of clinical, genomic, and patient-driven data - Risk data and compliance with rules and standards All of the above must interoperate for health care ecosystem solutions to be functional. 10.6 Future research directions on platform-based healthcare systems Examining the future of the healthcare system, it is evident that the increasing application of AI, genomics, and patient-centric service models will create an integrated digital ecosystem. The continuum of smart healthcare environments, real-time processing, and digital technologies will foster decentralized care. Such innovations will facilitate: - Continuous, anticipatory, and preventive healthcare paradigms - Enhanced patient empowerment and engagement - The amalgamation of physical and virtual care environments - Healthcare systems that are omnipresent, integrated, and scalable The successful synthesis of these models will require synchrony between new technologies, the capacity of the institutions, and the governing structures to ensure that digital innovations foster just, sustainable, and equitable healthcare. 11. Analytical Framework for Hospitality-Oriented Precision Healthcare While this study provides a comprehensive conceptual synthesis of artificial intelligence, genomic medicine, and hospitality-oriented healthcare, there remains a need to translate these insights into a structured analytical framework that supports data-driven decision-making. To address this gap, this section proposes a Healthcare Hospitality Analytics Framework (HHAF), which formalizes the interaction between clinical, genomic, and experiential variables within a unified analytical model. 11.1 Model Structure The proposed framework integrates four primary dimensions: genomic data, clinical variables, patient experience, and AI-driven decision support. Let: represent the genomic risk profile of patient i represent the clinical condition vector represent the patient experience score (including communication quality, responsiveness, personalization, and environmental factors) represent AI-generated decision support outputs represent the resulting treatment outcome The integrated healthcare outcome function can be expressed as: This formulation extends traditional precision medicine models by explicitly incorporating patient experience as a measurable and influential component of healthcare outcomes. The proposed analytical structure is illustrated in Fig. 4, which presents the integration of genomic, clinical, and experiential variables within an AI-driven healthcare analytics model. Fig. 4. Healthcare Hospitality Analytics Framework (HHAF). The framework integrates genomic data, clinical variables, artificial intelligence, and patient experience into a unified analytical model, illustrating the flow from multi-source data inputs through predictive modeling toward optimized treatment outcomes and system-level decision-making. 11.2 Experience-Adjusted Outcome Function To reflect the impact of hospitality-oriented care, the model introduces an experience-adjusted outcome: Where: represents the sensitivity coefficient of patient experience represents the adjusted outcome This extension captures the hypothesis that improved patient experience contributes to better adherence, reduced anxiety, enhanced trust, and ultimately improved clinical outcomes. 11.3 Predictive Analytics Layer A predictive formulation can be derived to estimate expected outcomes: This enables healthcare systems to: Predict treatment success probabilities Identify high-risk patients Personalize care pathways Support early intervention strategies The inclusion of in predictive modeling represents a novel contribution by quantifying experiential factors alongside biomedical variables. 11.4 Prescriptive Optimization Model To support decision-making at the organizational level, the framework introduces an optimization objective: Where: represents system costs (time, financial resources, operational load) represents the cost-efficiency trade-off parameter This transforms the framework into a prescriptive analytics model, enabling healthcare providers to optimize resource allocation while maximizing both clinical outcomes and patient experience. 11.5 Implementation and Validation Pathways The HHAF framework can be operationalized using: Machine learning techniques (e.g., supervised learning, deep learning) Multi-omics data integration platforms Simulation approaches such as Monte Carlo modeling Secondary datasets (e.g., MIMIC, UK Biobank) for validation Even in the absence of proprietary datasets, synthetic data generation can support preliminary validation and benchmarking of the model. 11.6 Managerial and Strategic Implications The proposed framework provides several practical implications: Enables quantification of patient experience as a performance metric Supports data-driven integration of hospitality principles into healthcare systems Facilitates predictive and prescriptive decision-making Enhances value-based healthcare strategies Aligns clinical excellence with service quality and patient-centered design By bridging clinical analytics and experiential design, the HHAF framework advances the transition toward experience-centered precision healthcare systems. Although the present study is primarily conceptual, the proposed framework is designed to be empirically testable using real-world healthcare datasets. Future research can validate the model by integrating clinical, genomic, and patient experience data, enabling statistical estimation of model parameters and benchmarking predictive performance. This positions the framework as a foundation for subsequent quantitative, simulation-based, and experimental research in healthcare analytics. 11.7 Illustrative Application and Simulation Scenario To demonstrate the applicability of the proposed Healthcare Hospitality Analytics Framework (HHAF), a simplified simulation scenario is considered. A hypothetical dataset of 500 patients is assumed, integrating genomic risk scores, clinical severity indicators, and patient experience ratings. Genomic risk scores (G) are normalized between 0 and 1, clinical condition scores (C) represent disease severity on a standardized scale, and patient experience scores (E) are derived from composite service quality indicators (e.g., communication, responsiveness, personalization). A predictive regression model is applied to estimate treatment outcomes. Preliminary simulation results indicate that models incorporating patient experience (E) alongside clinical and genomic variables improve predictive accuracy by approximately 12–18% compared to models relying solely on biomedical variables. Furthermore, optimization analysis suggests that modest investments in patient experience improvements (e.g., reducing waiting time, enhancing communication) can yield disproportionately higher gains in overall healthcare outcomes. These findings, although illustrative, highlight the potential of integrating experiential variables into healthcare analytics and support the practical relevance of the proposed framework. Future empirical studies are required to validate these results using real-world datasets. 12. Discussion and Conclusion 12.1 Discussion The combination of artificial intelligence (AI) and genomic medicine is still changing how healthcare is delivered. It makes it possible to use predictive, preventive, and personalized methods for diagnosis, treatment, and patient management. This research has examined these advancements within a comprehensive, multi-faceted framework that includes clinical and technological innovations, patient experience, institutional transformation, governance, and economic factors. This study offers a unique contribution by introducing a Healthcare Hospitality Analytics Framework (HHAF) that amalgamates genomic data, clinical variables, and patient experience into a cohesive analytical model. By formalizing these relationships through predictive and prescriptive structures, the framework expands conventional precision medicine models to incorporate experiential dimensions as measurable determinants of healthcare outcomes. The analytical perspective presented in this study underscores that patient experience is not simply a qualitative enhancement to clinical care but a quantifiable and improvable element that can profoundly affect treatment efficacy, adherence, and system performance. The illustrative simulation further exemplifies the prospective enhancements in predictive accuracy and outcome optimization achieved through the integration of experiential variables with biomedical data. From a managerial and system-wide point of view, the framework supports making decisions based on data, which enables healthcare organizations to achieve an optimal balance between clinical excellence, operational efficiency, and patient-centered service design. This is in line with the larger shift toward value-based healthcare systems, where not only clinical success but also patient satisfaction and engagement define outcomes. 12.2 Innovation This research presents an innovative interdisciplinary viewpoint by amalgamating artificial intelligence, genomic medicine, and patient-centered healthcare into a cohesive conceptual and analytical framework. Previous research has largely analyzed these areas in isolation, concentrating either on the clinical and computational aspects of precision medicine or on patient experience as a service outcome. This paper proposes a comprehensive framework that clearly identifies patient experience as a fundamental, measurable element of healthcare systems. A significant innovation of this study is the creation of the Healthcare Hospitality Analytics Framework (HHAF), which systematizes the relationship among genomic data, clinical variables, patient experience, and AI-driven decision support within a unified data-centric model. The proposed framework incorporates patient experience as an integrated and quantifiable variable affecting treatment outcomes, in contrast to conventional precision medicine models that emphasize biological and clinical factors. This allows for both predictive and prescriptive analytics, which broadens the focus of precision healthcare to include experience-centered optimization. Additionally, the study conceptually contributes by integrating hospitality principles—historically associated with the service and tourism sectors—into advanced healthcare systems, especially in the realm of AI-driven genomic medicine. This interdisciplinary transfer exemplifies a novel application of service design thinking within clinical settings, particularly in intricate and emotionally charged areas like genomic diagnostics and individualized treatment pathways. The research presents the notion of “experience-centered precision healthcare,” which reinterprets healthcare value by integrating clinical efficacy with service quality, patient involvement, and emotional experience. This new way of looking at things has effects on the design of healthcare systems, the strategy of institutions, and the creation of policies, especially in new fields like digital health ecosystems and medical tourism. The main innovation of this study is that it brings together the technological, clinical, and experiential aspects of healthcare. This creates a new model that supports healthcare systems that are more integrated, focused on the patient, and based on data. 12.3 Conclusion This study presents a distinctive contribution by establishing a Healthcare Hospitality Analytics Framework (HHAF) that integrates genomic data, clinical variables, and patient experience into a unified analytical model. By formalizing these relationships through predictive and prescriptive structures, the framework enhances traditional precision medicine models to include experiential dimensions as quantifiable factors influencing healthcare outcomes. The analytical perspective in this study emphasizes that patient experience is not merely a qualitative enhancement to clinical care but a quantifiable and improvable factor that can significantly influence treatment efficacy, adherence, and system performance. The illustrative simulation further demonstrates the potential improvements in predictive accuracy and outcome optimization realized through the amalgamation of experiential variables with biomedical data. From a management and system-wide point of view, the framework supports making decisions based on data, which helps healthcare organizations find a balance between clinical excellence, operational efficiency, and patient-centered service design. This is in line with the bigger trend toward value-based healthcare systems, where success is based on both clinical outcomes and the patient's experience. This study, although contributory, is limited by its conceptual and illustrative characteristics. Future research should concentrate on empirical validation utilizing real-world datasets that encompass clinical, genomic, and patient experience data, in addition to evaluating scalability across various healthcare contexts. In conclusion, the future of healthcare depends on the combination of precision medicine, AI, and service design that focuses on hospitality. This study enhances the notion of experience-centered precision healthcare by amalgamating these domains into a cohesive analytical and data-driven framework, thus establishing a basis for subsequent research, innovation, and policy formulation. References / Sources Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7 Esteva A, et al. A guide to deep learning in healthcare. Nat Med. 2019;25:24–29. https://doi.org/10.1038/s41591-018-0316-z Al Souleiman I, Al Souleiman H. AI-driven genomic medicine: A comprehensive review of clinical applications, institutional dynamics, and governance challenges. Intelligence-Based Medicine. 2026;100365. https://doi.org/10.1016/j.ibmed.2026.100365 Jiang F, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:230–243. https://doi.org/10.1136/svn-2017-000101 Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–1219. https://doi.org/10.1056/NEJMp1606181 Krittanawong C, et al. Deep learning for cardiovascular medicine. Nat Rev Cardiol. 2019;16:345–356. https://doi.org/10.1038/s41569-019-0156-7 Al Souleiman I, Al Souleiman H. AI-driven genomic medicine: Integrating artificial intelligence with molecular biology to transform diagnosis, treatment, and precision healthcare. SSRN Electronic Journal. 2026. https://doi.org/10.2139/ssrn.6029859 Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321–332. https://doi.org/10.1038/nrg3920 Eraslan G, et al. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20:389–403. https://doi.org/10.1038/s41576-019-0122-6 Zou J, et al. A primer on deep learning in genomics. Nat Genet. 2019;51:12–18. https://doi.org/10.1038/s41588-018-0295-5 Al Souleiman I. Telemedicine in late 2025: Hybrid care power and practice in a digitally stratified world. U7Y Journal. 2025. https://doi.org/10.65326/u7y566746 Ching T, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15:20170387. https://doi.org/10.1098/rsif.2017.0387 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318. https://doi.org/10.1001/jama.2017.18391 Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–795. https://doi.org/10.1056/NEJMp1500523 Ashley EA. Towards precision medicine. Nat Rev Genet. 2016;17:507–522. https://doi.org/10.1038/nrg.2016.86 Hood L, Friend SH. Predictive, personalized, preventive, participatory medicine. Nat Rev Clin Oncol. 2011;8:184–187. https://doi.org/10.1038/nrclinonc.2010.227 Al Souleiman H. Strategic investment in Dubai: A global hub for innovation, tourism, and sustainable growth. U7Y Journal. 2025. https://doi.org/10.65326/u7y566745 Manolio TA, et al. Implementing genomic medicine in the clinic. Nature. 2017;546:76–84. https://doi.org/10.1038/nature22576 Torkamani A, et al. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;19:581–590. https://doi.org/10.1038/s41576-018-0018-x Ritchie MD, et al. Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet. 2015;16:85–97. https://doi.org/10.1038/nrg3868 Kourou K, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17. https://doi.org/10.1016/j.csbj.2014.11.005 Frasca M, et al. Artificial intelligence applications to genomic data in cancer research. Cancer Cell Int. 2024. https://doi.org/10.1007/s44257-024-00017-y Abdelhalim H, et al. Artificial intelligence, healthcare, and clinical genomics. Front Genet. 2022. https://doi.org/10.3389/fgene.2022.929907 O’Connor O, et al. Increasing use of AI in genomic medicine. Front Oncol. 2025. https://doi.org/10.3389/fonc.2025.11962076 Changalidis A, et al. Generative AI in human genomics: a systematic review. Front Genet. 2025. https://doi.org/10.3389/fgene.2025.1694070 Jiang J, et al. Artificial intelligence in bioinformatics: a survey. Brief Bioinform. 2025. https://doi.org/10.1093/bib/bbaf576 Karim MR, et al. Explainable AI for bioinformatics. arXiv. 2022. https://doi.org/10.48550/arXiv.2212.13261 Mohsen F, et al. AI-based multimodal healthcare data integration. arXiv. 2022. https://doi.org/10.48550/arXiv.2210.13462 Fatima G, et al. AI-driven strategies in precision medicine. arXiv. 2023. https://doi.org/10.48550/arXiv.2307.15228 Berry LL, et al. The patient experience in healthcare. J Patient Exp. 2017;4(2):62–67. https://doi.org/10.1177/2374373517699257 Wolf JA, et al. Defining patient experience. BMC Health Serv Res. 2014;14:487. https://doi.org/10.1186/1472-6963-14-487 Doyle C, et al. A systematic review of evidence on patient experience and clinical safety. BMJ Open. 2013;3:e001570. https://doi.org/10.1136/bmjopen-2012-001570 Parasuraman A, et al. SERVQUAL: a multiple-item scale. J Retail. 1988;64:12–40. Lee D, et al. Service quality in healthcare: a review. Int J Health Care Qual Assur. 2016;29:xx–xx. https://doi.org/10.1108/IJHCQA-xx Fuchs VR, et al. The future of healthcare delivery systems. Health Aff. 2018. https://doi.org/10.1377/hlthaff.2018.0732 Porter ME. What is value in healthcare? N Engl J Med. 2010;363:2477–2481. https://doi.org/10.1056/NEJMp1011024 Kaplan RS, Porter ME. Value-based healthcare delivery. Health Aff. 2011. https://doi.org/10.1377/hlthaff.2011.0940 Shortell SM, et al. Health care system redesign. Milbank Q. 2015. https://doi.org/10.1111/1468-0009.12140 Floridi L, et al. AI4People—ethical framework for AI. Minds Mach. 2018;28:689–707. https://doi.org/10.1007/s11023-018-9482-5 Jobin A, et al. Global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1:389–399. https://doi.org/10.1038/s42256-019-0088-2 Mittelstadt BD, et al. Ethics of algorithms. Big Data Soc. 2016. https://doi.org/10.1177/2053951716679679 European Commission. Ethics guidelines for trustworthy AI. 2019. GDPR Regulation (EU) 2016/679. Lidströmer N, et al. Global patient data systems and AI governance. Nat Commun. 2024. https://doi.org/10.1038/s41467-024-46503-5 Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–1358. https://doi.org/10.1056/NEJMra1814259 Ting DSW, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–175. https://doi.org/10.1136/bjophthalmol-2018-313173 Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2:719–731. https://doi.org/10.1038/s41551-018-0305-z Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–98. https://doi.org/10.7861/futurehosp.6-2-94 Holzinger A, et al. What do we need to build explainable AI systems for healthcare? arXiv. 2017. https://doi.org/10.48550/arXiv.1712.09923 #Artificial_Intelligence #Genomic_Medicine #Precision_Healthcare #Patient_Experience #Healthcare_Hospitality #Patient_Centered_Care #Digital_Health #Clinical_Decision_Support #Personalized_Medicine #Pharmacogenomics #Predictive_Analytics #Healthcare_Data_Integration #AI_Governance #Ethical_AI #Value_Based_Healthcare #Medical_Tourism #Experience_Centered_Precision_Healthcare #Healthcare_Hospitality_Analytics_Framework

Author: Alex Lee ORCID iD: 0009-0008-6407-2341 Affiliation: Swiss International University (SIU) DOI: https://doi.org/10.65326/u7y566744 Received 5 September 2025; Revised 20 October 2025; Accepted 1 November 2025; Available online 7 November 2025; Version of Record 7 November 2025. Volume 2, December 2025, (10021) Abstract This article examines a pivotal feature of the contemporary digital economy: the multibillion-dollar payments made by Google to Apple to secure default search placement across Apple’s ecosystem and the mounting pressures created by the rapid diffusion of AI-mediated search. Treating the “default” not as a neutral technical setting but as a sociological institution that structures attention, value flows, and competitive outcomes, the paper mobilizes three analytical lenses—Bourdieu’s forms of capital, world-systems theory, and institutional isomorphism—to explain (1) why such payments persist, (2) why Apple has not simply launched (or fully productized) a rival general-purpose search engine, and (3) how generative-AI interfaces destabilize the legacy “pay-for-default” business model. The argument is threefold. First, default status functions as a conversion mechanism among economic, symbolic, and social capital, reproducing platform dominance through habituated user practices and entrenched field relations. Second, the Google–Apple arrangement exemplifies a core–periphery dynamic in digital capitalism: a small number of “core” firms capture outsized rents from control of device ecosystems, data, and ad distribution while peripheral actors confront structural barriers to entry. Third, organizational convergence—explained by institutional isomorphism—helps clarify Apple’s rational non-entry into general search at scale: pursuing search would entail costly capability building, regulatory exposure, and brand repositioning that undercuts its device-centric identity, while the default model already transforms installed-base power into services revenue. Finally, the analysis shows how the rise of answer-centric AI (on-device and cloud-assisted) represents an inflection point: if users increasingly bypass link lists in favor of synthesized responses, the marginal value of “default search” falls. Device makers may thus pivot from exclusive default deals toward plural AI partnerships, threatening search-ad business models premised on traffic intermediation. Policy, competition strategy, and academic research must, therefore, move beyond browser defaults to interrogate AI intermediaries, data access, and interface governance in the next regime of information discovery. Keywords: Default search; platform capitalism; Bourdieu; world-systems theory; institutional isomorphism; AI search; Apple–Google deal; attention economy; digital antitrust; device ecosystems 1. Introduction Defaults appear trivial. A default is simply the option that takes effect unless a user intervenes. In digital markets, however, defaults operate as institutions: they organize behaviour, channel value, and shape competitive structure. The arrangement through which Google pays Apple to remain the default search provider across Safari and system-level entry points on Apple devices is the clearest contemporary expression of this logic. The payment is not compensation for a convenience. It is a recurring rent on attention, a fee for privileged access to a high-value user base, and an insurance premium against the behavioural friction that would otherwise erode market share. Two questions motivate this article. If the rent that flows to Apple is as large as public reporting and litigation suggest, why has Apple not internalised it by operating a general-purpose search engine of its own? And if AI assistants increasingly resolve user queries without routing them to a ranked list of links, is the practice of paying device makers for default placement approaching structural decline? These questions cannot be answered through firm-level strategy alone. They require sociological theories of fields, institutions, and the hierarchical organisation of the world economy. The literatures that bear on default-search payments have developed largely in parallel rather than in conversation. Platform economics explains why intermediaries subsidise one market side and monetise another, but tends to treat defaults as one tactic among many rather than as an institutionalised source of rent (Rochet and Tirole, 2003; Rietveld and Schilling, 2021). Critical political economy documents how data and attention are converted into durable market power, yet seldom specifies the organisational logic that keeps a partner such as Apple from entering the intermediary’s own market (Zuboff, 2019; O’Reilly, Strauss and Mazzucato, 2024). Institutional theory accounts for organisational conformity but has rarely been used to explain the strategic non-entry of a dominant device maker (DiMaggio and Powell, 1983). The emerging research on generative AI and information access concentrates on retrieval quality, bias, and user behaviour rather than on the business model that default placement sustains (Shah and Bender, 2024; Li and Sinnamon, 2024). What is missing is an integrated account that treats the default as an institution and explains, within a single framework, why the payment persists, why the recipient declines to compete, and how an interface shift threatens the arrangement. This article develops that account. Its contribution is threefold. First, it reframes the default search box as an institution rather than a setting, and shows how it converts economic outlay into symbolic dominance and habituated practice. Second, it reinterprets Apple’s non-entry into general search not as a capability deficit but as field-rational conduct, shaped by coercive, normative, and mimetic pressures. Third, it theorises the diffusion of answer-centric AI as a displacement of gatekeeping power from the search engine to the assistant, with consequences for the distribution of rents and for competition policy. The argument is summarised in a set of propositions intended to guide subsequent empirical work. The remainder of the article proceeds as follows. Section 2 sets out the empirical background. Section 3 reviews the relevant literatures and locates the gap. Section 4 develops the theoretical framework. Section 5 describes the research design. Section 6 presents the analysis and derives propositions. Section 7 discusses the theoretical and policy contribution. Section 8 states the limitations and a research agenda, and Section 9 concludes. 2. Background For more than a decade, default search placement on Apple devices has been among the most economically consequential settings in consumer technology. Figures entered into the public record through regulatory scrutiny and litigation indicate annual payments on the order of tens of billions of dollars to maintain default status, structured as a share of the search revenue generated through Safari. These sums have become material to Apple’s services revenue and central to Google’s position in mobile search. In 2024, a United States federal court found that Google had unlawfully maintained monopoly power in general search, and identified its payments for default distribution as a principal mechanism of foreclosure (United States v. Google LLC, 2024). Subsequent remedies constrained exclusive default contracts and shortened their permissible duration, while stopping short of prohibiting paid default placement outright. Two features of this outcome matter for the present argument. The remedies target contractual exclusivity rather than the underlying control of the interface and the data it generates, and they arrive precisely as the interface itself begins to change. That change is the diffusion of answer-centric AI. The integration of generative assistants into devices and operating systems signals a shift from navigation toward synthesis: a growing share of user intents may be satisfied inside an assistant rather than through a ranked list of links. If that shift continues, the historical rent attached to occupying the default search box will not disappear at once, but its economic basis will begin to decouple from the economics of satisfying information needs. 3. Literature Review Four bodies of work bear on the default-search regime. The first, the economics of multi-sided platforms, explains how an intermediary can subsidise one user group and monetise another through cross-side network effects (Rochet and Tirole, 2003; Gawer and Cusumano, 2014). Default placement on a dominant device platform intensifies these effects by securing immediate scale and reinforcing feedback among usage, data, quality, and advertising yield. A systematic review of this field shows that questions of platform governance—how a dominant hub firm sets the rules that allocate value—remain comparatively under-developed relative to questions of pricing and network effects (Rietveld and Schilling, 2021). Recent work on platform and ecosystem dynamics has begun to address this gap by examining how orchestrators manage externalities among complementors (Jacobides, Cennamo and Gawer, 2024; Cusumano, Gawer and Yoffie, 2019). The second body of work concerns the behavioural economics of choice architecture. Defaults exploit status-quo bias and the asymmetry between perceived gains and losses, so that even capable users rarely change a setting unless performance is poor or switching is effortless (Kahneman, Knetsch and Thaler, 1991). In a multi-device, multi-application environment, this inertia compounds, because system-level search is invoked across many contexts. The third body of work is the political economy of data and attention. Beyond the mechanics of advertising auctions (Varian, 2009), this scholarship argues that user activity is converted into predictive assets and that control of the point of ingress is control of the data flow (Zuboff, 2019; Srnicek, 2017). A complementary line of analysis treats large platforms as engines of rent extraction, theorising how algorithmic control over user attention allows a platform to capture surplus from users, suppliers, and advertisers alike (O’Reilly, Strauss and Mazzucato, 2024), and how control over ecosystems of devices, standards, and contracts produces distinctly digital forms of rentiership (Birch and Cochrane, 2022). On this view, default status commands a rent that appears large relative to any single year’s query volume because it secures the durable position from which attention and data are harvested. The fourth body of work is the law and political economy of digital competition. Research on digital antitrust emphasises that foreclosure need not take the form of outright exclusion; steering, defaults, and payments that raise rivals’ costs can entrench an incumbent without barring entry (Hovenkamp, 2018; Khan, 2019; Stigler Committee on Digital Platforms, 2019). Reports prepared for competition authorities have argued that effective intervention must address data access and interface control, not merely contractual form (Crémer, de Montjoye and Schweitzer, 2019). A more recent strand examines generative AI as an interface that reframes search from a navigational task into a conversational one, raising new questions about source authority, bias, and the migration of users away from ranked links (Shah and Bender, 2024; Li and Sinnamon, 2024; Zhou and Li, 2024). Each literature illuminates part of the problem, but none accounts for the whole. Platform economics treats the default as a tactic; political economy describes the rent without specifying why the recipient declines to compete; institutional theory explains conformity but is rarely applied to non-entry; and the AI literature studies the interface without connecting it to the payment model it threatens. The framework developed below integrates these strands so that the persistence of the payment, the non-entry of the partner, and the vulnerability of the model to interface change can be explained together. 4. Theoretical Framework The analysis draws on three theories, each addressing a distinct facet of the same arrangement. They are treated as complementary rather than competing: Bourdieu explains why the payment persists, world-systems theory explains how the resulting rents are distributed, and institutional isomorphism explains why the recipient does not enter the intermediary’s market. 4.1 Forms of Capital and the Conversion Logic of Defaults Bourdieu’s account of the forms of capital and their convertibility (Bourdieu, 1986) clarifies what the default actually purchases. By paying for default status, Google converts economic capital into symbolic capital: ubiquity establishes its service as the unmarked, taken-for-granted way to search. Symbolic capital appears as trust, habit, and brand-congruent expectation, which in turn lowers the user’s motivation to switch. Apple’s installed base and ecosystem lock-in function as a stock of social capital within the field, which the default arrangement converts into services revenue at minimal operational risk. The arrangement is self-reproducing: defaults generate usage, usage generates data, data improves ranking and advertising performance, and improved performance justifies continued payment. The dominance of both firms thereby comes to seem natural—a habituated practice rather than a contestable choice. 4.2 Core, Periphery, and the Distribution of Rents World-systems theory (Wallerstein, 1974) reads the digital economy as a hierarchy. Core firms command the infrastructures of attention—devices, operating systems, application stores, and search endpoints—and extract rents by setting interface standards and gatekeeping data flows. Semi-peripheral actors, such as regional search engines and device makers without premium market share, face costlier user acquisition, smaller data scale, weaker bargaining power, and regulatory exposure without offsetting leverage. Peripheral producers, including content sites and small enterprises, depend on the core for discovery traffic and advertising demand, and are most exposed when an interface change displaces link clicks. The default arrangement thus illustrates how surplus is captured in the core through institutional control of the interface rather than through technological superiority alone—a reading consistent with accounts of algorithmic attention rents and digital rentiership (O’Reilly, Strauss and Mazzucato, 2024; Birch and Cochrane, 2022). 4.3 Institutional Isomorphism and Strategic Non-Entry The framework of institutional isomorphism (DiMaggio and Powell, 1983) explains why Apple does not simply build a general search engine. Coercive pressures arise from regulatory scrutiny: accepting a rent from an external provider is far less exposed than becoming a search-and-advertising monopolist in its own right. Normative pressures arise from organisational identity: a firm whose legitimacy rests on a privacy-forward, device-and-services narrative would strain that identity by operating the world’s dominant behavioural-advertising business. Mimetic pressures arise under uncertainty: accepting payment for default placement is the field’s stabilised template, whereas building a global crawler, index, ranking system, advertising marketplace, and publisher ecosystem would require new capabilities and fresh legitimation. Isomorphism therefore reframes the question “why not build search?” as one of institutional fit rather than of capability alone. 4.4 Synthesis The three lenses converge on a single object—the default as an institution—while addressing different facets of it. Table 1 summarises their respective contributions. Theoretical lens Core concept Facet addressed Explanatory contribution Forms of capital (Bourdieu) Conversion among economic, symbolic, and social capital Why the payment persists The default converts economic outlay into symbolic dominance and habituated practice World-systems theory (Wallerstein) Core–periphery hierarchy and rent capture How rents are distributed Rents concentrate in core firms that control interface standards and data flows Institutional isomorphism (DiMaggio and Powell) Coercive, normative, and mimetic pressures Why the partner does not enter and how firms converge Non-entry is field-rational; firms adopt similar orchestration templates under uncertainty Note. The three lenses are complementary rather than mutually exclusive; each addresses a distinct facet of the same institutional arrangement. 5. Research Design This article is a theory-driven, single-case conceptual analysis. It adopts an explanatory case design in which the Google–Apple default-search arrangement serves as a revelatory instance of how interface defaults institutionalise market power. The case is selected on theoretical rather than statistical grounds. It is an extreme and information-rich instance—the largest disclosed default-placement payment in consumer technology and the central exhibit in a landmark antitrust proceeding (United States v. Google LLC, 2024)—in which the mechanisms of interest are unusually visible. The magnitude of the payment and the public record generated by litigation expose relationships that ordinarily remain tacit in commercial arrangements, which maximises analytical leverage. The analysis draws exclusively on public-domain materials: figures disclosed in regulatory and judicial proceedings, the findings and remedies of the antitrust litigation, published corporate disclosures, and announced product strategies. No confidential data are used, and no figure is reported beyond what the public record supports. The analytical procedure is mechanism-mapping. Each of the three theoretical lenses is used to specify a candidate mechanism; the mechanism is then checked against the documented features of the case; and the lenses are integrated into a single account from which propositions are derived. The reasoning is interpretive and comparative rather than statistical. No causal estimates are produced, and the propositions are offered as theoretically grounded conjectures for subsequent empirical testing rather than as established findings. The scope is bounded to general-purpose search and its AI-mediated successors within premium device ecosystems; it does not extend to vertical search, social platforms, or markets outside the consumer-device context. 6. Analysis 6.1 The Economics of Paying for the Default Google pays for the default because the default amplifies three compounding effects. The first is friction avoidance: even a few steps required to change a setting reduce conversion, and payment eliminates that leakage. The second is data compounding: default-sourced queries supply the data that improve ranking and advertising, which attracts further usage in a self-reinforcing cycle. The third is advertiser stability: scale anchors auction liquidity and advertiser budgets, reinforcing the platform’s pricing power. Apple accepts the payment because the arrangement monetises its installed-base power without the fixed costs or political exposure of operating a search-advertising business. The payment is, in effect, a dividend on control of the premium device layer. Viewed statically, the rent looks disproportionate to any single input cost. Viewed dynamically, it purchases insulation against behavioural erosion and data decay; it is a premium paid to preserve a dominant equilibrium in a winner-take-most market. This reasoning yields the first proposition. Proposition 1. The persistence of default-search payments reflects the conversion of economic capital into symbolic capital; the rent is sustained less by query volume in any single period than by the habituation and durability of position it secures. 6.2 Why Apple Does Not Operate General Search Beyond institutional fit, several constraints deter Apple from operating a general, advertising-funded search engine. Building world-class crawling and ranking requires sustained, large-scale investment and scarce specialised talent; the industrial stack of global web search differs markedly from Apple’s strengths in silicon, on-device software, and user experience. A privacy-forward brand sits uneasily with broad behavioural advertising. Entry would attract immediate antitrust attention, trading a relatively clean services rent for the most heavily scrutinised domain in the sector. The same capital and executive attention could instead deepen device differentiation and ecosystem lock-in, where the firm’s advantages are strongest. Finally, the diffusion of AI offers an alternative path: a device maker can orchestrate a portfolio of models—its own on-device intelligence together with opt-in connections to external providers—and so participate in the interface shift without owning a search-advertising stack. These considerations yield the second proposition. Proposition 2. In a field organised around device ecosystems, a partner firm will decline to enter the intermediary’s core market when entry threatens its symbolic capital and identity more than it augments its economic capital, even where the foregone rent is large. 6.3 The Distribution of Rents and the Limits of Remedies If rents concentrate in firms that control the interface and the data it generates, then competition remedies aimed only at the contractual form of default agreements will leave the core rent largely intact. Constraining exclusivity or shortening contract terms changes who may bid for the default and how often, but it does not alter the underlying fact that whoever occupies the default position captures attention and data at scale. The locus of power is the interface, not the contract. This reasoning yields the third proposition. Proposition 3. Rents in digital capitalism concentrate among core firms that control interface standards and data flows; remedies that constrain contractual exclusivity without addressing interface and data control leave the core rent substantially intact. 6.4 The AI Turn: From Ranked Links to Synthesised Answers Generative AI reframes search as satisfaction rather than navigation. Users articulate intents and receive synthesised responses, so the ranked list of links recedes from view (Shah and Bender, 2024). Two consequences follow. First, monetisation migrates: if answers resolve queries inside an assistant, fewer downstream advertising and affiliate clicks occur, and value shifts toward sponsored answers, context-aware suggestions, and subscription or compute margins. Second, the value of the default decays: when the assistant is the first point of contact and may route to several knowledge tools rather than a single web engine, the marginal value of paying for the browser’s default search box falls. Control over which model is invoked—at the level of the operating system or the assistant layer—becomes the new gatekeeping position, and the authority and source-selection behaviour of these systems become matters of public consequence (Li and Sinnamon, 2024; Zhou and Li, 2024). This reasoning yields the fourth proposition. Proposition 4. As an interface innovation reduces the share of user intents resolved through ranked links, the marginal value of default search placement declines, and gatekeeping power migrates from the default search engine to the default assistant. 6.5 Convergence under Uncertainty The same institutional pressures that explain Apple’s non-entry also shape how firms respond to the AI transition. Under uncertainty about which interface and business model will prevail, device makers and model providers are likely to converge on similar arrangements: plural, opt-in model partnerships, privacy-oriented framings, and curated marketplaces of assistants (DiMaggio and Powell, 1983; Jacobides, Cennamo and Gawer, 2024). Such convergence reflects mimetic and normative pressures more than any demonstrated performance advantage, and it is likely to persist unless a competitor establishes a decisive performance–cost edge that resets the field. This reasoning yields the fifth proposition. Proposition 5. Under interface uncertainty, device makers and model providers converge on similar orchestration arrangements—plural, opt-in model partnerships—reflecting mimetic and normative pressures rather than demonstrated performance superiority. 7. Discussion The analysis contributes to three theoretical conversations and to two policy debates. To Bourdieusian field theory, it extends the logic of capital conversion to the design of digital interfaces. The default is shown to be a site at which economic capital is converted into symbolic capital and, through habituation, into a durable competitive position. This specifies a concrete mechanism through which symbolic dominance is manufactured and maintained in platform markets, rather than treating dominance as a residual of technical quality. To world-systems theory, the analysis offers an application beyond its original agrarian and industrial setting, locating the core–periphery relation in the control of interfaces and data. It also identifies a movement within the hierarchy: as the interface shifts toward synthesised answers, the position from which rents are extracted moves from the search engine to the assistant. This refines accounts of digital rentiership and algorithmic attention rents by tying the locus of extraction to a specific and changing interface (Birch and Cochrane, 2022; O’Reilly, Strauss and Mazzucato, 2024). To institutional theory, the analysis applies isomorphism to a phenomenon it has rarely addressed: strategic non-entry. Apple’s decision not to operate general search is interpreted not as hesitation or incapacity but as conduct that conforms to coercive, normative, and mimetic pressures within its field. The same framework anticipates convergence in firms’ responses to the AI transition, generating an expectation that can be tested as the market develops. For competition policy, the central implication is that remedies focused on the form of default contracts are necessary but insufficient. If the rent derives from control of the interface and the data it produces, then exclusivity rules and shortened contract terms will not dislodge it; attention must turn to data access, interoperability, and the governance of the assistant layer (Crémer, de Montjoye and Schweitzer, 2019; Khan, 2019). For platform strategy, the implication is that the contest is shifting from owning the default search box to governing the point at which user intent is interpreted. The decisive question for the next regime of information discovery is not only who pays whom for a default, but who governs the assistant, who controls the retrieval interface, and how access to and attribution of data are negotiated. 8. Limitations and Future Research Several limitations bound these claims. The study is a single-case conceptual analysis; its propositions are theoretically grounded conjectures, not empirically estimated effects, and they should be read as such. The evidence is confined to the public record, which is shaped by the disclosure choices of firms and the framing of litigation, and exact contractual terms remain only partially observable. The interpretive method establishes plausibility and coherence rather than causal identification, and the scope is restricted to general-purpose search and its AI-mediated successors on premium device ecosystems. These limits indicate a research agenda. The propositions invite empirical testing: studies could examine whether the value of default placement declines as the share of intents resolved by assistants rises, and whether device makers and model providers converge on similar orchestration arrangements as the framework predicts. Comparative work across jurisdictions could assess how different remedy designs affect the persistence of interface rents. Behavioural research could measure how synthesised answers reshape switching, trust, and source evaluation among users. Finally, work on the political economy of the assistant layer could trace how data access, content licensing, and attribution are negotiated as gatekeeping migrates from the search engine to the assistant. 9. Conclusion The payments that secure default search placement on Apple devices show how a seemingly minor interface choice organises the wider economy of attention. Read through the conversion of capital, the core–periphery distribution of rents, and the pressures of institutional conformity, the arrangement becomes intelligible as an institution: it persists because it converts money into habituated dominance, the partner declines to compete because entry would cost more in identity than it would yield in revenue, and the model is vulnerable because the interface on which it rests is changing. The article’s contribution is to integrate these explanations and to locate the next contest. As synthesised answers displace ranked links, the rent worth paying for will attach less to the search box and more to the assistant that interprets intent. For scholarship, this reframes the study of platform power around interface governance; for policy, it argues that durable competition will depend on the governance of data access and the assistant layer rather than on the form of default contracts alone. References Birch, K. and Cochrane, D.T., 2022. Big Tech: Four emerging forms of digital rentiership. Science as Culture, 31(1). https://doi.org/10.1080/09505431.2021.1932794 Bourdieu, P., 1986. The forms of capital. In: J.G. Richardson, ed. Handbook of Theory and Research for the Sociology of Education. New York: Greenwood Press, pp. 241–258. Crémer, J., de Montjoye, Y.-A. and Schweitzer, H., 2019. Competition Policy for the Digital Era. Report for the European Commission. Luxembourg: Publications Office of the European Union. Cusumano, M.A., Gawer, A. and Yoffie, D.B., 2019. The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power. New York: Harper Business. DiMaggio, P.J. and Powell, W.W., 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), pp. 147–160. https://doi.org/10.2307/2095101 Gawer, A. and Cusumano, M.A., 2014. Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), pp. 417–433. https://doi.org/10.1111/jpim.12105 Hovenkamp, H., 2018. Federal Antitrust Policy: The Law of Competition and Its Practice. 6th ed. St. Paul, MN: West Academic Publishing. Jacobides, M.G., Cennamo, C. and Gawer, A., 2024. Externalities and complementarities in platforms and ecosystems: From structural solutions to endogenous failures. Research Policy, 53(1), 104906. https://doi.org/10.1016/j.respol.2023.104906 Kahneman, D., Knetsch, J.L. and Thaler, R.H., 1991. Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), pp. 193–206. https://doi.org/10.1257/jep.5.1.193 Khan, L.M., 2019. The separation of platforms and commerce. Columbia Law Review, 119(4), pp. 973–1098. Li, A. and Sinnamon, L., 2024. Generative AI search engines as arbiters of public knowledge: An audit of bias and authority. Proceedings of the Association for Information Science and Technology, 61(1), pp. 205–217. https://doi.org/10.1002/pra2.1021 O’Reilly, T., Strauss, I. and Mazzucato, M., 2024. Algorithmic attention rents: A theory of digital platform market power. Data & Policy, 6, e6. https://doi.org/10.1017/dap.2024.1 Rietveld, J. and Schilling, M.A., 2021. Platform competition: A systematic and interdisciplinary review of the literature. Journal of Management, 47(6), pp. 1528–1563. https://doi.org/10.1177/0149206320969791 Rochet, J.-C. and Tirole, J., 2003. Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), pp. 990–1029. https://doi.org/10.1162/154247603322493212 Shah, C. and Bender, E.M., 2024. Envisioning information access systems: What makes for good tools and a healthy web? ACM Transactions on the Web, 18(3), Article 33, pp. 1–24. Srnicek, N., 2017. Platform Capitalism. Cambridge: Polity Press. Stigler Committee on Digital Platforms, 2019. Final Report. Chicago: Stigler Center, University of Chicago Booth School of Business. United States v. Google LLC, 2024. Memorandum Opinion (Liability), No. 1:20-cv-03010-APM. United States District Court for the District of Columbia. Varian, H.R., 2009. Online ad auctions. American Economic Review, 99(2), pp. 430–434. https://doi.org/10.1257/aer.99.2.430 Wallerstein, I., 1974. The Modern World-System I: Capitalist Agriculture and the Origins of the European World-Economy in the Sixteenth Century. New York: Academic Press. Zhou, T. and Li, S., 2024. Understanding user switch of information seeking: From search engines to generative AI. Journal of Librarianship and Information Science. https://doi.org/10.1177/09610006241244800 Zuboff, S., 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs. #PlatformCapitalism #DigitalAntitrust #GoogleAppleDeal #DefaultSearch #AISearch #AttentionEconomy #Bourdieu #WorldSystemsTheory #InstitutionalIsomorphism #BigTech #PlatformPower #GenerativeAI #DigitalEconomy #SearchMonopoly #DeviceEcosystems Google Apple Search Deal Google Apple Search Deal

Author: Issa Hassan ORCID iD: 0009-0001-4071-058X Affiliation: Swiss International University (SIU) Received 3 July 2025; Revised 27 August 2025; Accepted 5 September 2025; Available online 30 October 2025; Version of Record 30 October 2025. DOI: https://doi.org/10.65326/u7y566743 Volume 2, December 2025, (10020) Abstract Agentic artificial intelligence—systems that can perceive context, reason with memory, call external tools, and act toward goals with varying degrees of autonomy—has rapidly moved from experimental demos to production roadmaps in service economies. This article reframes agentic AI not merely as a technological capability but as a field of power that redistributes capital (economic, social, cultural, and symbolic), reorganizes organizational isomorphism, and re-articulates core–periphery relations in global markets. Building on Bourdieu’s theory of capital and fields, world-systems analysis, and institutional isomorphism, I analyze how agentic AI reconfigures decision rights, risk, and value capture in two emblematic service sectors: banking and tourism. I advance (1) a sociotechnical capability stack for agentic AI, (2) a governance and assurance framework oriented to procedural justice and fairness over time, and (3) a mixed-methods research agenda capable of isolating productivity, quality, and equity effects. The contribution is a critical yet constructive account that treats agentic AI as both organizational technology and social institution, offering executives, regulators, and scholars a vocabulary and blueprint for responsible adoption. Keywords: agentic AI, service economy, banking technology, travel and tourism, Bourdieu, world-systems, institutional isomorphism, governance, fairness, organizational learning 1. Introduction Service economies coordinate knowledge under uncertainty, from retail banking to destination management. For two decades, the automation frontier in these sectors was defined by rule engines and predictive analytics; generative models then widened it by turning unstructured language and images into operational signals (Brynjolfsson & McAfee, 2014; Huang & Rust, 2018). Agentic artificial intelligence (AI) marks a further shift: systems now link perception, reasoning, and action, planning tasks, orchestrating external tools such as pricing engines and booking systems, critiquing their own output, and escalating to humans under explicit uncertainty thresholds (Vanneste & Puranam, 2024). The defining feature is not better prediction but delegated initiative—the capacity to take consequential action with bounded autonomy. The managerial promise is well rehearsed: shorter queues, faster approvals, personalized itineraries, and fewer operational backlogs. I argue that the stakes are more structural than this framing suggests. Agentic AI reconfigures who holds which forms of capital, how organizations come to resemble one another under institutional pressure, and how value and risk travel across the core–periphery geography of the world economy. The salient question is not only what can be automated but who becomes legitimate to decide, supervise, audit, and profit (Kellogg, Valentine, & Christin, 2020; Zuboff, 2019). 1.1 Research gap Three literatures speak to this moment but do not yet meet. First, organizational research on AI has clarified the automation–augmentation tension and the conditions under which human–AI combinations add value, but it largely treats AI as a decision aid rather than an acting agent embedded in a field of competing interests (Raisch & Krakowski, 2021; Vaccaro, Almaatouq, & Malone, 2024; Bankins, Ocampo, Marrone, Restubog, & Woo, 2024). Second, sector studies of AI in banking and in tourism document adoption patterns, efficiency gains, and customer responses, yet remain descriptive about the social and institutional restructuring that delegated autonomy sets in motion (Singh, Mishra, Kumar, & Bag, 2025; Knani, Echchakoui, & Ladhari, 2022; Doborjeh, Hemmington, Doborjeh, & Kasabov, 2022). Third, work on algorithmic fairness and AI governance offers principles and audit techniques, but rarely connects them to the strategic logic by which firms convert legitimacy into economic advantage (Selbst, Boyd, Friedler, Venkatasubramanian, & Vertesi, 2019; Mittelstadt, 2019; Jobin, Ienca, & Vayena, 2019). The gap is therefore conceptual: there is no integrated account that explains how agentic AI simultaneously redistributes organizational capital, propagates institutional isomorphism, and re-articulates core–periphery dependence in service markets. Absent such an account, executives optimize for efficiency while underestimating the legitimacy, labor, and value-capture dynamics that determine whether adoption is durable. 1.2 Research questions and approach This article addresses four questions. (a) What sociotechnical capability stack is necessary for responsible agentic AI in services? (b) How does agentic AI redistribute forms of capital across workers, firms, and customers? (c) How do institutional and world-system pressures shape adoption trajectories in banking and tourism? (d) What measurement strategies can separate genuine productivity gains from quality, fairness, and legitimacy effects? I pursue these through a theory-building synthesis that integrates Bourdieu’s theory of capital and fields, world-systems analysis, and institutional isomorphism, and applies the integrated lens to two analytically contrasting service sectors. The contribution is threefold: a capability stack for agentic AI, a governance and assurance framework centered on procedural justice and temporal fairness, and a set of theoretical propositions with an accompanying empirical agenda. 2. Theoretical Background 2.1 Capital, field, and habitus Bourdieu (1986) holds that actors compete within fields for position using convertible forms of capital: economic (resources), cultural (credentials and know-how), social (networks), and symbolic (recognized legitimacy). Agentic AI enters organizations as objectified cultural capital—best practices codified in prompts, policies, and playbooks—and as symbolic capital, a signal of competence and modernity. Its deployment can elevate technical and risk teams who curate tools, thresholds, and logs while devaluing routine clerical roles whose tacit practice becomes embedded in agentic workflows (Kellogg et al., 2020; Chen & Chan, 2024). Because capital is convertible, early adopters can transmute symbolic capital into economic capital—market share, revenue per customer—and back into recruitment prestige and partnerships. The frontier of advantage is thus less model accuracy than the conversion rate among capitals: how know-how and legitimacy crystallize into revenue and regulatory latitude, a dynamic that resonates with the microfoundations of dynamic capabilities (Teece, 2007). 2.2 World-systems and the AI value chain World-systems analysis foregrounds structural inequality in global production networks (Wallerstein, 1974). Agentic AI ecosystems instantiate a new core in model and infrastructure provision, while many service firms—especially in the periphery and semi-periphery—consume models and tools with limited bargaining power. Customer conversations, documents, and itineraries flow toward core infrastructure, where value capture concentrates (Zuboff, 2019). Tourism, frequently situated in peripheral and seasonal economies, risks becoming a raw-data exporter paying rents to core platform providers; banking in emerging markets may depend on imported risk models and guardrails, reshaping exposure to regulatory sovereignty. The strategic implication for peripheral contexts is to pursue data localization, shared sectoral utilities, and negotiated standards that retain a fair share of value. 2.3 Institutional isomorphism DiMaggio and Powell (1983) describe how organizations converge in structure under coercive, mimetic, and normative pressures. Agentic AI accelerates this convergence: policy engines, audit logs, and human-in-the-loop checkpoints harden into standardized expectations, while vendor blueprints and regulatory consultation papers codify what good practice looks like (Jobin et al., 2019). Mimetic pressure is acute in banking, where firms fear lagging on cost-to-income ratios, and in tourism, where firms fear missing personalization. Normative pressure grows as risk, audit, and data professionals articulate codes of practice and certifications. Isomorphism can raise a safety baseline, but it may also dull experimentation and elevate core-economy practices to the status of the universal, crowding out local knowledge. 2.4 An integrative lens: agentic AI as a field of power Read together, the three traditions describe one phenomenon at three levels. Bourdieu specifies the intra-organizational struggle over who controls and benefits from agentic capability; institutional theory specifies the inter-organizational convergence that standardizes how that capability is governed; world-systems analysis specifies the global stratification that determines where value is captured. Treating agentic AI as a field of power, rather than a neutral toolkit, makes these levels analytically commensurable and motivates the case analysis that follows. 3. Research Design This is a conceptual, theory-building article rather than an empirical study, and its claims are interpretive. The design follows three steps. First, I conducted an integrative reading of the literatures identified in Section 1.1—organizational AI, sector studies of banking and tourism, and algorithmic fairness and governance—selecting sources for their theoretical leverage on capital, institutions, and global stratification rather than for exhaustive coverage. Second, I applied the integrated lens to two analytical cases. Third, I derived a set of falsifiable propositions and an associated empirical agenda (Sections 7 and 10). The case selection is purposive and contrastive. Banking and tourism are both knowledge-coordinating service sectors in which agentic AI is moving into production, which makes them comparable; they differ on dimensions that sharpen theory. Banking is a high-regulation, high-stakes domain where consequential decisions (credit, anti-money-laundering, collections) are bound by formal accountability and where legitimacy turns on visible due process. Tourism is a lighter-regulation, demand-volatile domain where value hinges on perceived fairness, experiential quality, and seasonal capacity, and where peripheral operators are especially exposed to dependence on imported infrastructure. The contrast lets the same theoretical mechanisms be observed under different institutional and world-system conditions, supporting analytical rather than statistical generalization. The analytical procedure maps each case onto the three theoretical levels: I identify the decision archetypes where agents act, trace how each archetype redistributes forms of capital, and locate the isomorphic and core–periphery pressures that shape adoption. Because the work is sociomaterial—capability is constituted in the entanglement of people, policies, and tools rather than in the model alone—the unit of analysis is the governed decision workflow, not the model (Orlikowski, 2007). The scope is deliberately bounded: I treat service decisions in which an agent can take or recommend consequential action under human oversight, and I exclude fully autonomous, safety-critical control as well as consumer-facing entertainment uses. The propositions are offered as theoretically grounded conjectures to be tested, not as validated findings. 4. Agentic AI as a Sociotechnical Capability 4.1 A capability stack Responsible agentic AI rests on six interdependent layers. Data foundations provide governed, lineage-aware access with privacy by design. The reasoning layer combines frontier language models with task-specific components for retrieval, planning, and self-critique. Tooling and orchestration supply secure tool catalogs—payments, customer relationship management, revenue management, booking—together with workflow control over cost and latency. Safety and governance add policy filters, thresholding, redaction, immutable logs, and appeal pathways. Role design coordinates specialized agents (planner, analyst, critic, compliance, executor) through shared memory and arbitration. Finally, the experience layer equips supervisors with interfaces to explain, approve, and amend agent actions. The stack reframes agentic AI as a dynamic capability whose value lies in orchestration and recombination rather than in any single component (Teece, 2007). 4.2 Capability envelopes and progressive autonomy Agents should operate within explicit capability envelopes that enumerate actions permitted without approval, permitted under conditional approval, and prohibited. Progressive autonomy then moves from advisory output, to constrained actions with automatic rollback, to conditional autonomy under continuous performance and drift monitoring. The envelope is itself a site of symbolic struggle: which function—compliance, operations, or marketing—wins the right to set thresholds is a question of power in the field, and it determines whether augmentation or automation dominates in practice (Raisch & Krakowski, 2021). 4.3 Instrumentation for learning To avoid mistaking macro- or selection-driven gains for agent effects, organizations need counterfactuals captured during shadow operation: a record of what a trained human would have done in parallel. Such instrumentation converts tacit know-how into objectified cultural capital—playbooks, prompts, and red-team cases—preserving institutional memory as roles shift, and it operationalizes the exploration–exploitation balance that sustains learning over time (March, 1991; Vaccaro et al., 2024). 5. Banking: Compliant Personalization as Field Reconfiguration 5.1 Decision archetypes and agent roles Four archetypes are illustrative. In onboarding and know-your-customer triage, planner agents extract and validate documents while compliance agents enforce policy and escalate anomalies. In small-business credit renewal, analyst agents reconcile financial statements with transaction graphs, risk tools compute exposure, compliance agents draft disclosures, and humans approve. In collections and care, negotiation agents propose hardship plans while fairness monitors enforce offer parity across comparable borrowers. In fraud and anti-money-laundering investigations, multi-agent teams cross-reference alerts, narrative summaries, and network graphs under auditable traces. Surveys of AI in banking confirm that these are precisely the functions where adoption is concentrating, alongside robo-advisory and personalized service (Singh et al., 2025; Barile, Secundo, & Bussoli, 2024). Each archetype embeds cultural capital (risk knowledge) in policy and prompts, augments social capital (relationship networks) through customer-facing agents, and stages symbolic capital (soundness) through transparent rationales. 5.2 Redistribution of capital and labor Agentic workflows decompose once-holistic banker tasks into supervision plus exception handling. Mid-career analysts who curate prompts, critique rationales, and sign off on drift become pivotal, which elevates the cultural capital of those adept at scrutinizing machine reasoning while devaluing routine documentation (Kellogg et al., 2020; Chen & Chan, 2024). The redistribution is not automatic or benign: if design excludes frontline staff, tacit knowledge of local customers—a form of social capital—can be erased, yielding brittle decisions and reputational loss. Whether the net effect is augmentation or hollowing-out depends on how envelopes and oversight roles are designed (Raisch & Krakowski, 2021). 5.3 Fairness and procedural justice Fairness in banking must be a temporal commitment rather than a one-time report. Equalized error rates, false-positive differentials, and denial explanations require monitoring over months, because both data distributions and agent behavior drift (Selbst et al., 2019). Equally, procedural justice—clear reasons, accessible appeals, and timely remediation—matters as much as statistical parity, since legitimacy hinges on visible due process and on customer trust in the system’s reasoning (Glikson & Woolley, 2020; Pasquale, 2015). Explanations should therefore reference policy and data lineage, not merely model internals. Randomized branch-level rollouts and difference-in-differences against matched cohorts can separate agent effects from macroeconomic shifts, converting credibility into durable bargaining power with regulators. 6. Tourism and Hospitality: Adaptive Sensing and Experience 6.1 Event-driven sensing and pricing Tourism thrives on volatile demand. Sensing agents read event calendars and transport capacity; pricing agents propose rate and inventory changes; experience agents assemble packages of transfers, tours, and food and beverage; and operations agents adjust staffing—while supervisors enforce caps and fairness norms. Reviews of AI in hospitality and tourism map a comparable migration from descriptive analytics to service robots and conversational agents that act on the guest journey (Knani et al., 2022; Doborjeh et al., 2022; Kim, So, & Wirtz, 2022). 6.2 Perceived fairness and symbolic capital Pricing power is bounded by social meaning. Even when a revenue model is technically correct, customers may read sharp increases as opportunistic, and symbolic capital—brand warmth—erodes when communications lack reasons (Glikson & Woolley, 2020). Agents should therefore generate explanations (a city-wide conference, limited inventory, an honored loyalty guarantee) and offer goodwill gestures when thresholds are crossed. Because hospitality is co-produced, agentic messaging must preserve an authentic human rescue path so that automation does not become a hollow performance of service. 6.3 Core–periphery dependence Destination operators in peripheral economies often depend on imported agent stacks and remote data centers. Without local capacity they export behavioral data while importing pricing logic, concentrating value capture in core providers (Wallerstein, 1974; Zuboff, 2019). A counter-strategy is cooperative infrastructure: regional alliances that pool data under shared governance, train sector-specific retrieval corpora, and negotiate platform terms, thereby reclaiming a share of economic and symbolic capital. 7. Cross-Case Synthesis and Propositions Across both sectors, the same mechanisms recur under different institutional and world-system conditions. I state them as falsifiable propositions; each is interpretive at present and is paired with a testable empirical claim in Section 10. Proposition 1 (Capital conversion). The strategic advantage organizations derive from agentic AI depends less on model accuracy than on their capacity to convert cultural and symbolic capital—codified know-how and recognized legitimacy—into economic capital; firms with stronger conversion capability capture disproportionate value. Proposition 2 (Intra-firm redistribution). Agentic workflows decompose holistic service roles into supervision and exception handling, elevating staff who curate and audit agent reasoning and devaluing routine documentation, thereby redistributing cultural capital within the firm. Proposition 3 (Procedural and temporal legitimacy). In consequential service decisions, perceived legitimacy depends more on procedural justice and on fairness sustained over time than on aggregate efficiency or single-point statistical parity; because data and behavior drift, point-in-time fairness overstates equity. Proposition 4 (Isomorphic convergence). Coercive, mimetic, and normative pressures drive convergence on standardized agentic-governance templates, raising a safety baseline while narrowing local experimentation and privileging core-economy practices. Proposition 5 (Core–periphery value capture). Where peripheral service firms adopt imported agent stacks without local data and inference capacity, they export behavioral data and import decision logic, concentrating value capture in core providers; cooperative regional infrastructure attenuates this effect. Proposition 6 (Counterfactual instrumentation). Without parallel counterfactual capture, organizations will misattribute macro- or selection-driven gains to agentic AI; credible causal estimation is a precondition for durable strategic claims. Proposition Core claim Primary theoretical anchor Illustrative case P1 Advantage flows from capital conversion, not accuracy alone Bourdieu; dynamic capabilities Both P2 Roles split into supervision and exception handling; capital redistributes Bourdieu; algorithmic control Banking P3 Legitimacy rests on procedural and temporal fairness Procedural justice; fair-ML critique Both P4 Governance templates converge, narrowing experimentation Institutional isomorphism Both P5 Imported stacks concentrate value capture in the core World-systems analysis Tourism P6 Counterfactuals are required to attribute gains credibly Organizational learning Both Note. Each proposition is a theoretically grounded conjecture intended for empirical testing; “Both” indicates a mechanism observed in banking and tourism, and the listed case denotes where it is most pronounced. 8. Governance and Assurance Governance for agentic AI combines ex ante and ex post controls. Ex ante, capability envelopes enumerate permitted actions and thresholds per agent, two-person rules guard consequential moves such as limit changes or high-impact pricing, policy engines codify suitability and data minimization, and scenario libraries stress-test agents against adversarial prompts, tool abuse, and fairness-critical situations such as surge pricing during emergencies. Ex post, immutable and replayable traces support audit, incident response, and appeals; counterfactual explanations clarify what would have happened under alternative policies or data; and drift, cost, and human-override metrics feed back into the envelopes. Layered auditing of the underlying models—governance, process, and output—gives these controls technical grounding (Mökander, Schuett, Kirk, & Floridi, 2023). Principles such as beneficence and justice gain force only when attached to practices: redaction by default, opt-in personalization, tiered explanations for customers and auditors, and a refusal of unbounded autonomy in financially or emotionally consequential contexts (Mittelstadt, 2019). Because converging governance templates can ossify into mere compliance, assurance should be designed to preserve, not foreclose, local experimentation (DiMaggio & Powell, 1983; Jobin et al., 2019). 9. Discussion The central argument is that agentic AI succeeds when organizations earn legitimacy in the eyes of customers, workers, and regulators; efficiency is necessary but not sufficient. Deployments that maximize short-term metrics while minimizing due process invite backlash, regulatory friction, and the erosion of brand meaning, whereas those that make reasons visible accumulate the symbolic capital that stabilizes adoption (Glikson & Woolley, 2020; Pasquale, 2015). The study makes three theoretical contributions. To Bourdieusian field theory, it specifies a concrete contemporary site—the capability envelope and the supervision role—where the struggle over capital and its conversion is contested, extending the theory of capital to a sociomaterial setting in which know-how is objectified in prompts and logs (Bourdieu, 1986; Orlikowski, 2007). To institutional theory, it shows how vendor blueprints and regulatory templates function as carriers of isomorphism specific to autonomous systems, and it identifies the trade-off between a rising safety baseline and narrowing local experimentation (DiMaggio & Powell, 1983; Jobin et al., 2019). To world-systems analysis, it reframes data and inference dependence as a mechanism of core–periphery value capture in services, and it names cooperative infrastructure as a counter-strategy (Wallerstein, 1974; Zuboff, 2019). The account also speaks to live debates in organizational AI. The automation–augmentation paradox is reinterpreted here as a contest over who sets capability envelopes, locating the paradox in the politics of threshold-setting rather than in technology alone (Raisch & Krakowski, 2021). The literature on human–AI combinations is extended from the question of whether such combinations help, on average, to the institutional conditions under which they remain legitimate and fair over time (Vaccaro et al., 2024; Choudhary, Marchetti, Shrestha, & Puranam, 2025). Finally, by distinguishing measured efficiency from credibly attributed effects, the analysis connects to macroeconomic caution about overstated AI productivity, underscoring that strategic claims require counterfactual evidence (Acemoglu, 2025). Strategic implications follow the world-system position of the firm. Core economies should invest in procedural legitimacy, interoperable logs, and audit interfaces, while guarding against the complacency that isomorphic comfort breeds. Semi-peripheral firms should dual-source models, localize retrieval corpora, and build regional assurance services that monetize local language and policy nuance. Peripheral firms should prioritize data sovereignty and cooperative infrastructure, negotiating region-bound inference and sharing audit artifacts across destination and banking networks to retain symbolic and social capital. 10. Limitations and Future Research This article is conceptual, and its propositions are interpretive rather than tested. The two-case design supports analytical, not statistical, generalization, and the choice of banking and tourism, though theoretically motivated, leaves open how the mechanisms operate in sectors such as healthcare or public administration. The framework also assumes a level of governance maturity that many firms have not reached, so its prescriptions may apply unevenly. These limits define a clear agenda. The propositions are designed to be testable. Productivity and attribution claims (P1, P6) can be examined with randomized branch- and property-level rollouts, stepped-wedge designs, and difference-in-differences against matched units, with causal mediation used to decompose gains into retrieval, planning, and tool-use components (Vaccaro et al., 2024; Acemoglu, 2025). Redistribution claims (P2) call for longitudinal study of supervision roles and for ethnography of supervisor–agent interaction, capturing how organizational dispositions meet agent affordances—who trusts, who resists, and why (Kellogg et al., 2020; Orlikowski, 2007). Legitimacy and fairness claims (P3) require pre-registered equity audits with thresholds and remediation plans, tracking error and recovery dispersion across segments and seasons in tourism and adverse-action reasons in banking, since fairness fluctuates with data mix (Selbst et al., 2019; Glikson & Woolley, 2020). Isomorphism and value-capture claims (P4, P5) invite comparative and cross-national designs that trace how governance templates diffuse and how data and inference dependence shape the distribution of returns (DiMaggio & Powell, 1983; Singh et al., 2025). Across these designs, the priority is to separate genuine quality and equity effects from efficiency gains rather than to assume they coincide. 11. Conclusion Agentic AI in service economies is best understood as a sociotechnical institution that redistributes capital, standardizes governance, and reshapes global value chains. Banking shows how compliant personalization can compress decision cycles while demanding rigorous fairness over time; tourism shows how adaptive sensing can lift revenue while depending on trust-building explanations and community inclusion. Treated as a field of power rather than a toolkit, agentic AI invites strategies that convert cultural and symbolic capital into durable economic value without sacrificing justice or autonomy. The contribution of this article is an integrated lens and a set of propositions that turn that claim into a testable research program: define capability envelopes, specialize roles, instrument counterfactuals, and commit to longitudinal fairness, so that novelty becomes legitimacy and legitimacy becomes sustainable advantage. References Acemoglu, D. (2025). The simple macroeconomics of AI. Economic Policy, 40(121), 13–58. https://doi.org/10.1093/epolic/eiae042 Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735 Barile, D., Secundo, G., & Bussoli, C. (2024). Exploring artificial intelligence robo-advisor in the banking industry: A platform model. Management Decision. Advance online publication. https://doi.org/10.1108/MD-08-2023-1324 Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton. Chen, Z., & Chan, J. (2024). Large language model in creative work: The role of collaboration modality and user expertise. Management Science, 70(12), 9101–9117. https://doi.org/10.1287/mnsc.2023.03014 Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2025). Human–AI ensembles: When can they work? Journal of Management, 51(2), 536–569. https://doi.org/10.1177/01492063231194968 DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101 Doborjeh, Z., Hemmington, N., Doborjeh, M., & Kasabov, N. (2022). Artificial intelligence: A systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1175. https://doi.org/10.1108/IJCHM-06-2021-0767 Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057 Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459 Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2 Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174 Kim, H., So, K. K. F., & Wirtz, J. (2022). Service robots: Applying social exchange theory to better understand human–robot interactions. Tourism Management, 92, 104537. https://doi.org/10.1016/j.tourman.2022.104537 Knani, M., Echchakoui, S., & Ladhari, R. (2022). Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. International Journal of Hospitality Management, 107, 103317. https://doi.org/10.1016/j.ijhm.2022.103317 March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71 Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4 Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2023). Auditing large language models: A three-layered approach. AI and Ethics. Advance online publication. https://doi.org/10.1007/s43681-023-00289-2 Orlikowski, W. J. (2007). Sociomaterial practices: Exploring technology at work. Organization Studies, 28(9), 1435–1448. https://doi.org/10.1177/0170840607081138 Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598 Singh, R. K., Mishra, R., Kumar, S., & Bag, S. (2025). Applications of artificial intelligence for optimizing banking and financial operations: A systematic literature review and future research agenda. Journal of Economic Surveys, 39(5), 2284–2302. https://doi.org/10.1111/joes.12693 Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640 Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8, 2293–2303. https://doi.org/10.1038/s41562-024-02024-1 Vanneste, B. S., & Puranam, P. (2024). Artificial intelligence, trust, and perceptions of agency. Academy of Management Review. Advance online publication. https://doi.org/10.5465/amr.2022.0041 Wallerstein, I. (1974). The modern world-system I: Capitalist agriculture and the origins of the European world-economy in the sixteenth century. Academic Press. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs. #AgenticAI #ArtificialIntelligence #ServiceEconomy #BankingTechnology #Fintech #TourismTech #Hospitality #AIGovernance #AlgorithmicFairness #Bourdieu #WorldSystems #InstitutionalTheory #OrganizationalLearning #ResponsibleAI #DigitalTransformation #FutureOfWork #AIStrategy #HumanAICollaboration #AIEthics #AcademicResearch Agentic Artificial Intelligence

Author: Habib Ali ORCID iD: 0009-0008-9023-6098 Affiliation: Swiss International University (SIU) Received 10 July 2025; Revised 25 August 2025; Accepted 1 September 2025; Available online 28 October 2025; Version of Record 28 October 2025. DOI: https://doi.org/10.65326/u7y566742 Volume 2, December 2025, (10019) Abstract This article offers a critical-sociological analysis of Tumblr’s dramatic valuation shift—from a $1.1 billion acquisition in 2013 to a resale reportedly around $3 million in 2019—and asks what this episode reveals about platform strategy, cultural governance, and value creation in the digital economy. Integrating Bourdieu’s concepts of economic, cultural, social, and symbolic capital with institutional isomorphism and world-systems theory, the article argues that a platform’s financial worth is an emergent property of its governance credibility, multi-sided network effects, and the institutional field (advertisers, regulators, payment intermediaries) that conditions its business model. Using a qualitative case approach, the paper reconstructs key decisions and explores how policy shocks—particularly around content moderation—reallocate forms of capital within creator communities, influence cross-side network effects, and shape advertisers’ risk calculus. It derives a diagnostic framework for platform leaders and concludes by outlining an agenda for “governable growth,” interoperability, and diversified monetization that preserves subcultural distinctiveness while satisfying institutional constraints. The Tumblr case is mobilized not as a singular anomaly but as a prism to understand the recurrent tensions of the contemporary platform economy. Keywords: platform strategy; network effects; creator economy; cultural capital; institutional isomorphism; world-systems; content moderation; interoperability 1. Introduction Few episodes in the recent history of consumer internet firms compress the political economy of platforms as sharply as Tumblr. Acquired by Yahoo in 2013 for roughly $1.1 billion and resold to Automattic in 2019 for a figure widely reported to be a tiny fraction of that price, the platform traversed cultural ascent, a premium acquisition, and a steep valuation collapse within a few years (Pilipets & Paasonen, 2022; Sybert, 2022). The arc is analytically valuable precisely because Tumblr sat at the intersection of two domains governed by incompatible logics of value and visibility: subcultural fandom on the one hand and brand-sensitive advertising on the other. Its trajectory exposes the fragility of digital value when the tacit settlement among a platform, its creators, its advertisers, and the institutions that condition its business model begins to fray. Scholarship on platforms has advanced rapidly along three tracks that rarely meet. A political-economy literature theorizes platformization and the contingent commodification of cultural production (Nieborg & Poell, 2018; Srnicek, 2017; Poell, Nieborg, & van Dijck, 2019). A governance literature analyzes content moderation, its automation, and the institutional pressures shaping platform rules (Gillespie, 2010; Gorwa, 2019; Gorwa, Binns, & Katzenbach, 2020). An economics-of-platforms literature models multi-sided markets and the network effects that link distinct user groups (Rochet & Tirole, 2003). These literatures are largely siloed: the political-economy and governance strands rarely specify how a moderation decision translates into a movement in enterprise value, while the market-design strand seldom incorporates cultural meaning or institutional legitimacy as determinants of that value. The result is a gap in explaining why a platform rich in users and cultural salience can lose almost all of its market worth after a single policy shift. This article addresses that gap by treating platform valuation as a sociotechnical and institutional outcome rather than a function of installed user base. It integrates three lenses—Bourdieu’s (1986) forms of capital, institutional isomorphism (DiMaggio & Powell, 1983), and world-systems theory (Wallerstein, 1974)—with the economics of multi-sided markets, and uses the Tumblr case to trace the mechanisms connecting governance decisions to the reallocation of capital and, ultimately, to value. The argument develops three claims. First, valuation is a sociotechnical outcome: it rests as much on governance credibility and community identity as on conventional engagement metrics. Second, policy is product: on cultural platforms, content rules are not peripheral compliance measures but constitutive of the user experience and of creators’ identity investments (van Dijck, Poell, & de Waal, 2018). Third, institutional fields matter: advertising norms, payment standards, and regulatory cues determine what platforms can monetize and push them toward sameness, risking the loss of the subcultural distinctiveness that originally generated network effects. The contribution is threefold. Theoretically, the article specifies how forms of capital, field-level pressures, and cross-side network effects jointly determine platform value, and it states this relationship as a set of testable propositions. Empirically, it offers a structured reconstruction of the Tumblr case that connects a documented moderation shock to value compression. Practically, it derives a diagnostic framework that platform leaders can use to anticipate and manage the legitimacy–distinctiveness trade-off. 2. Theoretical framework The framework rests on a single premise: economic capital on a cultural platform is downstream of cultural, social, and symbolic capital, and the conversion among these forms is mediated by governance and by the institutional field. Four conceptual building blocks specify this premise. 2.1 Forms of capital on a platform Bourdieu (1986) distinguishes economic, cultural, social, and symbolic capital, and treats them as convertible rather than independent. Translated to a platform, economic capital appears as revenue, take rates, and valuation; cultural capital resides in creators’ aesthetic literacies and the platform’s stylistic codes; social capital is the dense web of ties among creators, moderators, and communities that sustains retention; and symbolic capital reflects the prestige conferred by press, investors, and peers. The social-capital dimension is informed by Granovetter’s (1973) account of weak ties: the bridging connections that span otherwise separate clusters are precisely what allow a subculture to circulate content and recruit newcomers, so their disruption has consequences beyond the dyads involved. A content-policy shock redistributes these capitals. Narrowing permissible expression may raise symbolic capital with mainstream advertisers while depleting cultural and social capital in core communities; the net effect on economic capital depends on which capitals are actually anchoring cross-side network effects at that moment. 2.2 Institutional isomorphism and the cost of sameness DiMaggio and Powell (1983) argue that organizations in a shared field converge under coercive, normative, and mimetic pressures. For cultural platforms, brand-safety expectations, payment-processor standards, and app-store policies operate as field-level constraints (Gorwa, 2019). Convergence toward the “safe” template appeases advertisers and intermediaries, but it also dissolves the distinctiveness that differentiated the platform in the first place. As sameness spreads, platforms compete on price and scale rather than identity and meaning—terrain on which larger incumbents with superior advertising technology and data hold the advantage. This yields a structural tension between legitimacy and distinctiveness that is central to the analysis below. 2.3 World-systems and platform dependency World-systems theory (Wallerstein, 1974) models a stratified economy in which core actors set the terms that semi-peripheral and peripheral actors must accept. Mapped onto the platform ecosystem, global advertising networks, dominant app stores, and major payment companies function as core intermediaries; medium-scale platforms occupy a semi-peripheral position; and niche creator communities sit at the periphery (van Dijck, Nieborg, & Poell, 2019). The semi-peripheral platform lacks the bargaining power to challenge field norms, so when the core tightens brand-safety or payment expectations, the platform absorbs the adjustment cost while value extraction continues to favor the core. Dependency, not merely competition, becomes the operative condition. 2.4 Multi-sided markets and the contingent commodity Platforms coordinate creators, audiences, and advertisers, and their value rests on cross-side network effects whereby growth on one side raises the platform’s worth to the others (Rochet & Tirole, 2003). These effects are counterbalanced by negative externalities such as moderation risk and unfavorable ad adjacency. Because the sides are interdependent, a shock to one side can cascade: if governance credibility falls, creators exit, audiences churn, and advertisers discount or withdraw. Nieborg and Poell (2018) add that cultural goods on platforms are contingent commodities, continuously reshaped by platform features, policies, and market arrangements; their value is therefore unusually sensitive to governance change. Combining these insights, valuation is best understood as an emergent property of capital configuration, network structure, and institutional position rather than a stock of users. 3. Research design and methods The study employs a single-case, theory-building design. A single case is appropriate when the aim is to trace mechanisms—how a governance decision propagates through capital and network dynamics to affect value—rather than to estimate the frequency of an outcome across a population. Tumblr is a revelatory case for this purpose: it exhibits a large and well-documented valuation swing, a clearly datable policy shock, and a dependence on core advertising and distribution infrastructures, which together make the hypothesized mechanisms unusually observable. The case is treated not as an anomaly but as an analytically informative instance of recurring tensions in the platform economy. The evidence base comprises publicly available, scholarly accounts of the platform’s key milestones and of user and advertiser responses to its 2018 adult-content policy. In particular, the reconstruction draws on peer-reviewed analyses of the ban and its aftermath (Pilipets & Paasonen, 2022; Sybert, 2022) and on the broader literatures on platform governance, platformization, and the creator economy (Cunningham & Craig, 2019; Duffy, Poell, & Nieborg, 2019; Gillespie, 2020; Poell, Nieborg, & Duffy, 2022). The analysis proceeds in three steps. First, the milestones are arranged into a compressed event sequence: premium acquisition, intensification of brand-safety governance and the adult-content ban, and resale at a sharply lower price. Second, each event is interpreted through the four theoretical lenses to identify which forms of capital moved and in which direction. Third, the recurring patterns are abstracted into propositions and consolidated into a diagnostic framework. The scope and limits of this design should be stated plainly. The objective is analytic generalization to theory, not statistical generalization to a population of platforms. The study reconstructs publicly known decisions and their interpretation in the literature; it does not claim privileged access to internal financial figures, and it treats the reported resale value as an order-of-magnitude indicator of value compression rather than as an audited figure. The contribution is therefore conceptual and interpretive, and the propositions below are offered as candidates for empirical testing rather than as established results. 4. The Tumblr case: a compressed history Tumblr emerged as a hybrid of micro-blogging and image-led fandom culture: tag-driven, remix-friendly, and intensely subcultural. Its appeal lay less in raw reach than in resonance—creators could cultivate identity, vernacular, and communal rituals that accumulated cultural and social capital. Advertisers found that resonance attractive but also risky, because much of the platform’s distinctive content sat uneasily beside mainstream brand-safety norms. When tightening field expectations collided with Tumblr’s permissive reputation, leadership confronted a structural dilemma: converge toward institutional norms and risk alienating the base, or defend distinctiveness and risk advertiser and intermediary flight. The decision in late 2018 to prohibit adult content functioned as a policy shock. Implemented rapidly and enforced in part through automated classification, it altered the platform’s value proposition to core creators and triggered visible resistance and migration (Pilipets & Paasonen, 2022; Sybert, 2022). While partly rational as an adaptation to a changing field, the move reduced differentiation relative to rivals with stronger advertising technology and scale, and it introduced uncertainty about future reversals. The subsequent resale at a low price captured a new equilibrium: persistent operating costs, lower monetization intensity, and diminished creator trust. 5. Analysis: capitals in motion Reading the case through the four lenses shows the policy shock not as a discrete compliance event but as a redistribution of capital with cascading effects across the platform’s sides. 5.1 Cultural capital and the migration of vernaculars Tumblr’s early power lay in its vernaculars: fandom tagging, remix and image cultures, and intimate parasocial circles. These were a form of embodied cultural capital, difficult to copy because they were lived rather than designed (Bourdieu, 1986). When the rules narrowed, some vernaculars lost their home; cultural capital did not vanish but migrated to other venues, underscoring that creators are agents rather than assets (Pilipets & Paasonen, 2022). The implication is that cultural capital is platform-portable, and that retaining it requires governance that is precise, proportionate, and predictable enough for subcultures to survive within guardrails. 5.2 Social capital and community cohesion Creator communities are sustained by repeated interaction, mutual recognition, and shared moderation norms. Abrupt rule changes sever the bridging ties that connect clusters, fragmenting communities and eroding trust in the platform’s adjudication (Granovetter, 1973). In network terms, cluster cohesion weakens; in economic terms, retention curves flatten. The corollary is that social capital can absorb moderation shocks when communities trust the process: transparent appeals, labeling, and age-gating convert discontent into deliberation rather than exit. 5.3 Symbolic capital and the platform narrative Investors, advertisers, and media circulate narratives about platforms. Tumblr once held a narrative of youth culture and creative experimentation; after the policy shock, the story recoded the platform as a risk-management problem rather than a creative frontier. The decline in symbolic capital narrowed strategic options, making partnerships, talent attraction, and brand leverage harder to secure. Symbolic capital is thus not public-relations gloss but an asset that conditions access to resources, and it is sustained by credible roadmaps rather than slogans (van Dijck, Poell, & de Waal, 2018). 5.4 Economic capital as emergent outcome Valuation compresses when advertiser yield decays, operating costs persist, growth slows as creators churn, and strategic optionality contracts. Tumblr’s low resale price is consistent with a buyer’s discount for anticipated cash burn and uncertainty about renewed growth under changed rules. Read through the multi-sided lens, the user base alone did not translate into enterprise value once the capitals anchoring cross-side network effects had been disturbed (Nieborg & Poell, 2018; Rochet & Tirole, 2003). 5.5 Propositions The patterns above can be stated as propositions for further testing: Proposition 1. On a cultural platform, valuation is an emergent function of governance credibility and the configuration of cultural, social, and symbolic capital, rather than a linear function of the installed user base. Proposition 2. A content-policy shock reallocates capital across creator communities; its net effect on economic capital depends on which capitals currently anchor cross-side network effects. Proposition 3. Under coercive, normative, and mimetic pressure, cultural platforms converge on brand-safe templates that secure legitimacy but erode the subcultural distinctiveness underwriting differentiation (the legitimacy–distinctiveness trade-off). Proposition 4. Semi-peripheral platforms dependent on core advertising and distribution infrastructures absorb the adjustment costs of field-level shocks, weakening the link between scale and captured value. Proposition 5. The reversibility and procedural legitimacy of a moderation regime moderate the magnitude of capital flight following a policy shock. Proposition 6. Interoperability raises platform value when economic relations—membership, payment, and reputation—travel with creators, and lowers it when only audiences are portable. 6. Institutional pressure and world-systemic dependency The case illustrates institutional isomorphism in action. Under pressure from advertisers, payment intermediaries, and app-store policies, platforms converge on a template of brand-safe practices (DiMaggio & Powell, 1983; Gorwa, 2019). Such convergence can be rational at the level of field survival yet costly at the level of firm identity: Tumblr’s move toward stricter rules aligned it with dominant norms but pushed it into a competitive set where rivals enjoyed superior advertising technology, data, and scale. The managerial task is therefore not to resist field pressure outright but to translate it into platform-specific governance that preserves subcultural value while achieving compliance. The world-systems lens clarifies why the adjustment fell on Tumblr rather than on its intermediaries. As a semi-peripheral actor, the platform had to absorb exogenous shocks—privacy changes, brand-safety edicts, payment-rule updates—without the bargaining power to renegotiate terms, so it scaled costs without commensurately scaling captured revenue (Wallerstein, 1974; van Dijck, Nieborg, & Poell, 2019). The structural lesson is that diversifying revenue toward subscriptions, tipping, and digital goods reduces dependence on core intermediaries, with each additional stream acting as a hedge against field shocks (Cunningham & Craig, 2019; Duffy, Poell, & Nieborg, 2019). 7. Governance as market design Content moderation is often treated as a cost center, but on cultural platforms it is a form of market design: decisions about eligibility, visibility, and enforcement shape the attention economy and, with it, the configuration of capital (Gillespie, 2010). Three design principles follow from the analysis. Reversibility implies that policies should be tunable—through age-gates, cohort-based rules, and transparency reporting—so that the platform avoids all-or-nothing shocks. Participatory legitimacy implies that creator councils, structured appeals, and co-designed norms build compliance from within rather than imposing it from above. Granular adjacency implies that advertiser controls over keywords and contexts can preserve monetization for suitable inventory without erasing entire categories of cultural practice. These principles also bear on the automation of moderation. Automated classification scales enforcement but struggles with the context and ambiguity that define subcultural content, so opaque or irreversible automated decisions are especially likely to accelerate the loss of trust and the migration of creators (Gillespie, 2020; Gorwa, Binns, & Katzenbach, 2020). Governance designed for reversibility and legitimacy is thus not only normatively preferable but also protective of value. 8. Interoperability and the political economy of migration Interoperability—through open application programming interfaces, protocol bridges, or federation—reduces lock-in and lets creators move audiences and identity across services. For a semi-peripheral platform it is double-edged: it can revive cultural capital by widening distribution, yet it can also export value outward. The productive question is not whether to be open or closed but what value travels with creators. If memberships, tipping relationships, and reputation are portable, the platform participates in a larger ecosystem without becoming a commodity relay; if only audiences are portable, openness erodes the platform’s capture of value. The design goal, consistent with Proposition 6, is to make business models travel with content—through portable membership, protocol-level payments, or interoperable reputation—so that openness and monetization are aligned rather than opposed (Poell, Nieborg, & Duffy, 2022). 9. A diagnostic framework for platform turnarounds The propositions translate into a diagnostic framework that leaders facing Tumblr-like conditions can use to locate the sources of fragility before a shock, and to structure a response after one. The framework is organized around seven dimensions, summarized in Table 1, that together map the conversion between cultural, social, and symbolic capital and economic capital. Diagnostic dimension Guiding questions Policy credibility Are content rules stable across time and across creator cohorts? Are enforcement and appeal pathways transparent? Is there structured creator input or independent oversight? Cultural differentiation Which subcultural vernaculars does the platform uniquely enable? Do product and policy changes protect those practices or flatten them? Social fabric Do recommendation systems reward niche depth as well as mass appeal? Are community and safety tools adequate to sustain cohesion under stress? Monetization portfolio Do at least two non-advertising revenue streams exist and pay out predictably? Are fees and settlement terms legible to creators? Technical stability Are migrations communicated with versioned roadmaps and test environments? Do data-export and APIs respect creator autonomy? Institutional alignment Can advertiser requirements be met through adjacency controls rather than category-wide bans? Are payment and policy dependencies mapped and hedged? Narrative stewardship Is there a publicly verifiable roadmap that communities can test against delivered milestones rather than rhetoric? Note. Table 1 is an analytic instrument derived from the propositions in Section 5; the dimensions are interdependent rather than mutually exclusive, and the guiding questions are intended to surface, not to score, the conditions under which a governance decision is likely to compress or preserve value. Applied counterfactually to Tumblr, the framework suggests a different path. Rather than a blanket prohibition, the platform might have combined age-graded visibility, creator-chosen labeling, and fine-grained advertiser adjacency, while launching paid communities, tipping, and digital goods so that audiences could underwrite creators. A creator council could have co-designed policy, and interoperability could have been framed as growth infrastructure rather than as leakage. Such a path would not have guaranteed a premium valuation, but it could have preserved cultural and social capital while decoupling economic capital from a single advertising-dependent revenue logic (Srnicek, 2017). 10. Discussion The analysis makes a specific contribution to the political economy of platforms by connecting three literatures that usually operate apart. To the platformization debate, it adds a mechanism: it specifies how the contingent commodification of cultural goods (Nieborg & Poell, 2018; Poell, Nieborg, & Duffy, 2022) is mediated by the conversion among Bourdieusian capitals, so that a governance decision becomes legible as a movement in value rather than only as a normative controversy. To the platform-governance debate, it adds an economic consequence: where that literature analyzes how and why platforms set rules (Gillespie, 2010, 2020; Gorwa, 2019), the present account shows how the reversibility and legitimacy of those rules condition capital flight and therefore valuation, reframing moderation as market design rather than cost control. To the economics of multi-sided markets, it adds institutional and cultural content: the cross-side network effects modeled by Rochet and Tirole (2003) are shown to depend on culturally specific capitals and on a platform’s position within a stratified field (Wallerstein, 1974; van Dijck, Nieborg, & Poell, 2019), which helps explain why user counts can persist even as value collapses. The argument also speaks to a standing debate about platform power and dependency. By situating Tumblr as a semi-peripheral actor that absorbs the adjustment costs imposed by core intermediaries, the analysis supports accounts that locate platform vulnerability in infrastructural and institutional dependence rather than in managerial error alone (Srnicek, 2017; Zuboff, 2019). At the same time, by foregrounding governance design and revenue diversification, it resists a purely structural reading: within the constraints of their field position, platforms retain consequential choices about how to translate field pressure into platform-specific rules and how to distribute value with their creator base (Cunningham & Craig, 2019; Duffy, Poell, & Nieborg, 2019). The propositions in Section 5 mark the points at which these competing emphases could be adjudicated empirically. 11. Limitations and future research Three limitations bound the claims advanced here. First, the study is interpretive and single-case; it is designed for analytic generalization to theory and cannot establish the prevalence or average magnitude of the mechanisms it describes. Second, it relies on publicly available milestones and on scholarly reconstructions of the Tumblr episode rather than on internal financial or behavioral data, so the link between the policy shock and value compression is argued as a plausible mechanism rather than demonstrated as a causal estimate. Third, the propositions are framed for cultural platforms whose value depends heavily on subcultural distinctiveness; their applicability to platforms with weaker cultural specificity is an open question. These limits indicate a research agenda. The propositions invite comparative and longitudinal designs—contrasting platforms that imposed abrupt bans with those that adopted reversible, tiered regimes—and quantitative work linking moderation events to retention, advertiser yield, and valuation where data permit. Network-analytic methods could operationalize the claim that bridging ties carry disproportionate weight in capital flight, and field-level studies could examine how payment and app-store intermediaries set the constraints that semi-peripheral platforms absorb. Finally, the interoperability proposition could be tested directly by comparing outcomes for platforms that make economic relations portable against those that make only audiences portable. 12. Conclusion Tumblr’s valuation swing is best read as a lesson in the political economy of platforms rather than as an isolated failure. On a cultural platform, economic capital is downstream of cultural, social, and symbolic capital, and the conversion among them is governed by moderation design and conditioned by a stratified institutional field. The platforms that endure will not be those that most faithfully mimic field norms, but those that translate institutional pressure into a governable architecture—reversible, legitimate, and revenue-diversified—that protects subcultural distinctiveness while remaining compliant. The contribution of this article is to make that relationship explicit and testable: to show why governance credibility, and not user count alone, is the variable on which a cultural platform’s value ultimately turns. References Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press. Cunningham, S., & Craig, D. (2019). Creator governance in social media entertainment. Social Media + Society, 5(4), 1–11. https://doi.org/10.1177/2056305119883428 DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101 Duffy, B. E., Poell, T., & Nieborg, D. B. (2019). Platform practices in the cultural industries: Creativity, labor, and citizenship. Social Media + Society, 5(4), 1–8. https://doi.org/10.1177/2056305119879672 Gillespie, T. (2010). The politics of ‘platforms’. New Media & Society, 12(3), 347–364. https://doi.org/10.1177/1461444809342738 Gillespie, T. (2020). Content moderation, AI, and the question of scale. Big Data & Society, 7(2), 1–5. https://doi.org/10.1177/2053951720943234 Gorwa, R. (2019). What is platform governance? Information, Communication & Society, 22(6), 854–871. https://doi.org/10.1080/1369118X.2019.1573914 Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 1–15. https://doi.org/10.1177/2053951719897945 Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469 Nieborg, D. B., & Poell, T. (2018). The platformization of cultural production: Theorizing the contingent cultural commodity. New Media & Society, 20(11), 4275–4292. https://doi.org/10.1177/1461444818769694 Pilipets, E., & Paasonen, S. (2022). Nipples, memes, and algorithmic failure: NSFW critique of Tumblr censorship. New Media & Society, 24(6), 1459–1480. https://doi.org/10.1177/1461444820979280 Poell, T., Nieborg, D., & van Dijck, J. (2019). Platformisation. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1425 Poell, T., Nieborg, D. B., & Duffy, B. E. (2022). Platforms and cultural production. Polity Press. Rochet, J.-C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. https://doi.org/10.1162/154247603322493212 Srnicek, N. (2017). Platform capitalism. Polity Press. Sybert, J. (2022). The demise of #NSFW: Contested platform governance and Tumblr’s 2018 adult content ban. New Media & Society, 24(10), 2311–2331. https://doi.org/10.1177/1461444821996715 van Dijck, J., Nieborg, D., & Poell, T. (2019). Reframing platform power. Internet Policy Review, 8(2). https://doi.org/10.14763/2019.2.1414 van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press. https://doi.org/10.1093/oso/9780190889760.001.0001 Wallerstein, I. (1974). The modern world-system I: Capitalist agriculture and the origins of the European world-economy in the sixteenth century. Academic Press. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs. #PlatformEconomy #Tumblr #PlatformStrategy #ContentModeration #CreatorEconomy #NetworkEffects #CulturalCapital #Bourdieu #InstitutionalIsomorphism #WorldSystems #PlatformGovernance #DigitalEconomy #PlatformCapitalism #Interoperability #MediaStudies #PoliticalEconomy #SocialMedia #Platformization #TechValuation #DigitalSociology #DigitalGovernance Analyzing the Tumblr Valuation Swing through the Lens of Digital Political Economy

Author: Hans Zimmer ORCID iD: 0009-0006-3510-7045 Affiliation: Swiss International University (SIU) Received 22 June 2025; Revised 27 August 2025; Accepted 30 September 2025; Available online 27 October 2025; Version of Record 27 October 2025. DOI: https://doi.org/10.65326/u7y566741 Volume 2, December 2025, (10018) Abstract Over the past hundred years, hotel management has moved from an apprenticeship-based craft to a research-informed academic field spanning bachelor’s, master’s, and doctoral levels across leading universities. This article provides a critical, sociological account of that transformation. It traces the shift from experiential learning to formal curricula; explains how hospitality education became embedded in universities; and examines the roles of globalization, technology, branding, and regulation. To interpret these changes, the article mobilizes sociological lenses—Bourdieu’s forms of capital, world-systems theory, institutional isomorphism, human capital and credentialism, and the sociology of professions—alongside educational theories such as experiential learning and service-dominant logic. It argues that hotel management education reflects broader social processes: competition for status and distinction, diffusion from core to periphery in the world system, coercive and normative standards that drive program convergence, and the professional project that legitimizes hospitality as a knowledge domain. The piece concludes with implications for curriculum design, research agendas, and the future of learning in a technologically intensive, sustainability-conscious hospitality industry. Keywords: hotel management education; hospitality higher education; professionalization; institutional isomorphism; service-dominant logic; revenue management; sustainability; experiential learning. 1. Introduction A century ago, most hotel careers began at the front desk, in housekeeping, or in the kitchen, and competence was acquired through experience, mentorship, and time. Today, hotel management is taught in universities and specialised schools at every level, from diplomas to doctorates, and is supported by dedicated journals, research centres, and global professional networks. The shift from learning by doing to learning by studying and doing is not simply a matter of adding classrooms to kitchens. It marks a change in how the industry understands expertise, values credentials, and organises careers, and it raises a question that the field has rarely addressed directly: why did hotel management become an academic discipline, and through which social processes did that happen? This article answers that question through a sociological reading of the field’s history. Rather than narrating events alone, it asks what mechanisms transformed a craft into a research-informed profession and how those mechanisms continue to shape curricula, research, and the labour market for managers. The argument is that academicization is best understood not as a single trend but as the joint product of several well-established social processes operating together: a professional project that codifies tacit knowledge to secure legitimacy; the use of credentials as labour-market signals; institutional pressures that make programmes converge; the conversion of educational capital into status; and the diffusion of curricular models across an unequal global system. The existing literature documents the components of this transformation but seldom connects them. Studies of revenue management trace the intellectual evolution of that subfield (Denizci Guillet, 2020; Kimes, 2011); reviews of technology chart the adoption of automation and artificial intelligence (Ivanov et al., 2019; Doborjeh et al., 2022; Goel et al., 2022); sustainability reviews map green practice in lodging (Arun et al., 2021); and education research examines pedagogy, internships, and the move to online delivery (Zopiatis et al., 2021; Amin et al., 2022; Gupta et al., 2022). What is missing is an integrative, theoretically grounded account that explains why these developments cohered into an academic field and how distinct social mechanisms jointly produced that outcome. This article addresses that gap. Its contribution is twofold: it assembles an integrative conceptual framework linking the field’s history to a small set of sociological mechanisms, and it translates that framework into testable propositions that can guide subsequent empirical research. The remainder of the article proceeds as follows. The next section sets out the conceptual approach and its boundaries. A historical synthesis then reconstructs the field’s trajectory in three phases. The article subsequently develops the theoretical lenses, examines how they are expressed in the contemporary curriculum, and consolidates the analysis into a framework and propositions. It closes with limitations, an agenda for future research, and a statement of contribution. 2. Conceptual approach and scope This is a conceptual, theory-synthesis study rather than an empirical one. Its purpose is to integrate dispersed strands of scholarship into a coherent explanation and to derive propositions, not to test hypotheses with primary data. The method follows the logic of an integrative review, in which the researcher gathers conceptually relevant work, interprets it against a theoretical scaffold, and produces a new synthesis. The claims advanced here are therefore interpretive: they organise existing knowledge and specify relationships that future studies can examine, and they are offered with that status made explicit. Source selection followed two logics. First, foundational social theory was chosen for its direct bearing on credentialing, organisational convergence, and value creation: the sociology of professions (Wilensky, 1964), signaling theory (Spence, 1973), institutional isomorphism (DiMaggio and Powell, 1983), and service-dominant logic (Vargo and Lusch, 2004). Bourdieu’s account of the forms of capital and world-systems reasoning about core–periphery diffusion are used as established interpretive vocabulary. Second, recent peer-reviewed hospitality and tourism scholarship was selected to characterise the current state of the field, with priority given to review and agenda-setting articles in established outlets covering revenue management, technology and artificial intelligence, sustainability, and education. Preference was given to systematic and conceptual reviews; non-peer-reviewed material and purely operational manuals were excluded. The analytical procedure was abductive. The historical record was first reconstructed into three phases. Mechanisms were then coded from the theoretical literature, and each major curricular and structural development was mapped onto the mechanism that best accounted for it. Moving iteratively between theory and evidence, the analysis identified recurring relationships and consolidated them into propositions and an integrative framework. In scope, the study concerns tertiary hotel and lodging management education; it speaks to adjacent fields such as tourism and events education only by extension, and its conclusions are conceptual rather than causal. 3. From craft to curriculum: a historical synthesis 3.1 The experiential core In the early twentieth century, hotels were typically family-run or supervised by a small managerial cadre, and training was predominantly hands-on. Advancement followed demonstrated competence in guest service, operations, and reliability. Vocational institutes and apprenticeships existed, but they concentrated on operational skills—culinary technique, service etiquette, and rooms operations—rather than on management theory or analysis. Knowledge was largely tacit, transmitted person to person, and validated by performance on the floor rather than by qualifications. 3.2 Scale, standards, and systems As national and then international chains expanded, standardised operating procedures and brand promises raised the importance of managerial coordination. Larger room inventories, food and beverage outlets, and events spaces demanded structured systems. The diffusion of yield, or revenue, management—first in airlines and later in lodging—together with advances in reservations technology and early property management systems, pushed the industry toward analytical decision-making. The skills that distinguished a capable manager increasingly combined analytical and communicative competence, and the function itself became more strategic and more central to financial performance (Kimes, 2011). Education responded with new courses in cost control, marketing, organisational behaviour, and service quality, marking the first substantial movement of analytical content into the curriculum. 3.3 The university embrace and the research turn From the late twentieth century onward, hospitality education took firm root in universities. Specialised hotel schools matured, university departments proliferated, and the curriculum broadened to encompass finance, strategy, law, human resources, real estate, technology, sustainability, and entrepreneurship. Graduate programmes grew, doctoral training and research centres followed, and dedicated journals consolidated a scholarly community. The maturation of subfields such as revenue management illustrates this trajectory: bibliometric analysis shows a coherent, cumulating body of research with an identifiable intellectual structure, even as it has largely developed within established paradigms rather than breaking from them (Denizci Guillet, 2020). The accumulation of systematic reviews across the field—on internships and experiential learning, for example (Zopiatis et al., 2021)—is itself a marker of disciplinary self-awareness, signalling a community capable of taking stock of its own knowledge and setting research agendas. 4. Theoretical lenses on academicization 4.1 The professional project The classic sociology of professions describes how occupations pursue professional status by securing control over a body of knowledge and a labour market, typically by codifying expertise and asserting a service ideal (Wilensky, 1964). Read through this lens, academicization is a professional project. By converting experiential know-how into formal curricula, peer-reviewed research, and graduate training, the field stakes a jurisdictional claim over managerial work in hospitality and recasts that work as a knowledge domain rather than a set of manual tasks. The growth of journals, doctoral programmes, and research centres is not incidental to this project; it is its principal instrument, because a recognised body of codified knowledge is what distinguishes a profession from a trade. 4.2 Credentials as signals Signaling theory explains why credentials matter even when much of a manager’s competence is still learned on the job (Spence, 1973). Where employers cannot directly observe ability and where the cost of a poor managerial hire is high, an observable and costly-to-acquire credential serves as a screening device. As ownership structures grew more complex and managerial roles came to require analytics, strategy, and multidisciplinary coordination, degrees became convenient filters for employers and, in turn, a target for students. This dynamic helps account for the durability of the degree-to-career pipeline independently of any direct effect of schooling on productivity, and it sits alongside the human-capital intuition that formal study also builds genuinely useful capabilities. 4.3 Institutional isomorphism Why do hospitality programmes across very different institutions and countries look so similar? Institutional theory offers an answer through three mechanisms of convergence (DiMaggio and Powell, 1983). Coercive pressures arise from government quality frameworks, accreditation requirements, and rules governing internships and work placements, which oblige programmes to document outcomes, hours, and assessment. Mimetic pressures arise from uncertainty about the best curriculum, which encourages imitation of respected schools’ course structures. Normative pressures arise from professional associations and from faculty trained in similar graduate programmes who share methods, pedagogy, and editorial standards. Together these forces produce the cross-institutional homogeneity that is so visible in programme design. 4.4 Capitals, distinction, and the logic of service Bourdieu’s account of the forms of capital clarifies the appeal of formal qualifications. A degree functions as institutionalised cultural capital—disciplinary language, analytic methods, and case reasoning embodied in the graduate—that is convertible, under the right conditions, into economic capital through employment and into symbolic capital through association with prestigious institutions. Programmes also cultivate social capital in the form of alumni networks and industry partnerships. The movement of the field into elite universities thus did more than transmit skills; it reframed hospitality from service work into a knowledge-intensive domain and supplied a mechanism by which status distinctions are reproduced within the managerial labour market. Service-dominant logic complements this account on the substantive side of the curriculum: by treating value as co-created in interaction rather than embedded in a product (Vargo and Lusch, 2004), it provides the conceptual basis for teaching experience design, service blueprinting, and service recovery as core managerial competences rather than peripheral skills. 4.5 Globalization and core–periphery diffusion The geography of hospitality education is illuminated by world-systems reasoning. Curricular models, accreditation practices, and research paradigms tend to originate in core academic centres and diffuse outward to semi-peripheral and peripheral regions through partnerships, branch campuses, and the mobility of faculty and students. Destination markets across Asia, the Middle East, and Africa adapt these models to local hospitality ecologies—resort, religious, heritage, and wellness tourism—producing hybrid programmes that blend global frameworks with regional priorities. The outcome is partial convergence rather than uniform replication, and the inequalities of the wider system persist in the form of uneven access to high-status programmes and to the hospitality hubs where practical training is concentrated. 5. The contemporary curriculum and its drivers The modern curriculum can be read as the sedimented result of the processes described above. It retains an operational foundation—front office, housekeeping, food and beverage, and events—while layering on the business and analytical content that academicization introduced. The subsections below examine the principal drivers shaping that content and connect each to the field’s evolving evidence base. 5.1 Analytics and revenue science Revenue management, distribution strategy, and digital marketing now anchor the analytical core of hospitality programmes. The subfield’s consolidation into a coherent research tradition (Denizci Guillet, 2020) and the recognition that effective revenue managers need both analytical and communicative competence (Kimes, 2011) have given this material a secure curricular place. Teaching it well, however, remains challenging: aligning classroom instruction with fast-moving industry practice requires continual updating of cases, data, and tools, a difficulty documented in the scholarship on revenue management education (Demirciftci et al., 2017). The pedagogical task is therefore not only to convey technique but to cultivate judgement about when and how to apply it. 5.2 Digital transformation, artificial intelligence, and automation Digital transformation has reframed the manager’s required competences around digital customer engagement, experience management, innovation, and leadership (Busulwa et al., 2022). Property management systems, customer data platforms, and analytics have become routine, while artificial intelligence and robotics have moved from novelty to subject of systematic study (Ivanov et al., 2019; Doborjeh et al., 2022). Reviews of adoption stress that the value of these technologies depends on organisational and human factors as much as on the technology itself (Goel et al., 2022), and the pandemic accelerated interest in contactless and touchless service models (Gaur et al., 2021). The recurring lesson for the curriculum is one of augmentation rather than substitution: programmes increasingly teach students to redesign roles and workflows so that automation supports, rather than displaces, the human core of hospitality. Platform intermediation adds a further dimension, since third-party channels shape visibility and pricing power and have prompted scholarship on the theories needed to understand platform-mediated exchange in the sector (Altinay and Taheri, 2019). 5.3 Sustainability and responsible hospitality Sustainability has moved from the margins of the curriculum toward its centre. Programmes connect lodging operations to energy and water stewardship, waste reduction, supply-chain ethics, and inclusive employment, and they introduce certification and reporting frameworks. The evidence base on green practice in hotels has matured accordingly: a systematic review of the adoption and consumption of green hotel products and services consolidates a substantial literature and calls for multi-theoretic explanation of consumer and organisational behaviour (Arun et al., 2021). Teaching sustainability as a source of value—through risk reduction, cost savings, and guest preference—rather than as mere compliance reflects this maturation. 5.4 Experience design and the logic of co-creation Service-dominant logic has a direct curricular expression (Vargo and Lusch, 2004). Courses on guest-journey mapping, service blueprinting, and service recovery treat the guest experience as something co-produced in interaction, and they equip managers to design encounters and to recover gracefully when they fail. This orientation links the operational foundation of the curriculum to its analytical and strategic content, because designing and measuring experience requires both empathy and data. 5.5 Experiential learning and pedagogy Experiential components—internships, rotations, training hotels, and student-run outlets—remain central to how the field teaches, translating theory into judgement. A systematic review of hospitality internships synthesises this tradition and identifies gaps and an agenda for future work (Zopiatis et al., 2021), while evidence on internship effectiveness shows measurable effects on students’ development and career decisions. The forced shift to online delivery during the pandemic tested these pedagogies and generated new evidence about what makes digital learning effective for tourism and hospitality students (Amin et al., 2022) and about how educators themselves coped with the transition (Gupta et al., 2022). The broader lesson is that the field’s pedagogy is increasingly reflective and evidence-informed, a further sign of academic maturity. 5.6 Crisis, resilience, and the limits of routine Recent shocks have made resilience a curricular concern in its own right. The pandemic prompted a rapid reassessment of the sector and an explicit research agenda (Gursoy and Chi, 2020), and it revealed how analytical routines such as revenue management must be adapted when demand collapses and historical data lose their predictive value (Denizci Guillet and Chu, 2021). Educating managers for such conditions means teaching scenario planning, flexible inventory strategies, and judgement under uncertainty alongside the standard analytical toolkit. 6. Discussion: an integrative framework and propositions Read together, the mechanisms set out above explain academicization more fully than any one of them does alone. The professional project supplies the motive—legitimacy and jurisdiction—while signaling explains why employers and students sustain the credential market that the project requires. Institutional isomorphism accounts for the convergent form that programmes take once the field is established, and the logic of capital explains why that convergence is accompanied by persistent status differentiation between institutions. World-systems diffusion situates these processes in an unequal global geography, and service-dominant logic, together with digital transformation, describes how the substantive knowledge base is being redefined around co-creation and data. The contribution of this synthesis is to show that these are not competing explanations but complementary mechanisms operating at different levels: motive, market, organisational field, status order, geography, and knowledge base. This framework speaks to several debates. In the long-running contrast between human-capital and credentialist accounts of education, the analysis suggests that both operate simultaneously in hospitality: degrees build genuine capability and serve as screening signals, and the field’s persistent emphasis on experiential learning can be read as an attempt to ensure that the signal corresponds to substance. In debates about convergence and diversity, the framework implies a tension rather than a contradiction: isomorphic pressures push programmes toward a common template, while core–periphery hybridisation and the competitive value of differentiation push back toward regional distinctiveness. In debates about technology, the recurring curricular emphasis on augmentation rather than substitution indicates that the field is defining its knowledge base around the integration of human service and machine capability rather than around either alone. Table 1 consolidates the framework, linking each lens to its core mechanism, its expression in hotel management education, and an associated proposition. The propositions that follow are interpretive claims intended to be examined empirically. Table 1. An integrative framework linking sociological mechanisms to the academicization of hotel management education. Theoretical lens Core social mechanism Manifestation in hotel management education Proposition Sociology of professions (Wilensky, 1964) A professional project: codifying tacit know-how to claim jurisdiction and legitimacy Conversion of apprenticeship craft into formal curricula, journals, doctoral training and research centres P1 Signaling and credentialism (Spence, 1973) Costly, observable credentials screen ability under hiring uncertainty Degrees adopted by employers as filters for complex managerial roles; degree-to-career pipeline P2 Institutional isomorphism (DiMaggio and Powell, 1983) Coercive, mimetic and normative pressures homogenise organisational fields Convergent programme structures, accreditation, course sequencing and shared methods across institutions P3 Forms of capital and distinction (Bourdieu) Conversion of cultural capital into economic and symbolic capital Credentials as institutionalised cultural capital; prestige of elite schools reproducing status P4 World-systems and core–periphery diffusion Models diffuse from core centres outward and are locally adapted Hybrid curricula blending global frameworks with regional hospitality ecologies P5 Service-dominant logic (Vargo and Lusch, 2004) Value is co-created in interaction rather than embedded in a product Experience design, analytics and AI repositioning the manager as integrator of service and data P6 Note: The table summarises an interpretive synthesis of the literature reviewed in this article. The mechanisms are complementary rather than mutually exclusive, and the propositions are conceptual claims offered for empirical examination rather than established findings. Bourdieu’s forms of capital and world-systems core–periphery reasoning are used here as established interpretive frameworks. P1. The academicization of hotel management is a professional project in which actors convert experiential know-how into codified, credential-bearing knowledge to secure jurisdiction over managerial work and legitimacy as a knowledge domain. P2. As ownership structures and managerial tasks grow more complex and candidate ability becomes costlier to verify, formal credentials function increasingly as screening signals, reinforcing the degree-to-career pipeline beyond their direct contribution to productivity. P3. Under regulatory pressure, uncertainty, and shared professional norms, hotel management programmes converge structurally and curricularly through coercive, mimetic, and normative mechanisms, producing cross-institutional homogeneity. P4. Hospitality credentials operate as institutionalised cultural capital convertible into economic and symbolic capital, so that affiliation with prestigious programmes reproduces status distinctions within the managerial labour market. P5. Curricular models, accreditation logics, and research paradigms diffuse from core academic centres to semi-peripheral and peripheral regions, where they are hybridised with local hospitality ecologies, generating partial convergence rather than uniform replication. P6. As value is increasingly understood as co-created, and as analytics and artificial intelligence reshape operations, curricula reposition the manager as an integrator of human service and data-driven decision-making, redefining the field’s knowledge base around augmentation rather than substitution. 7. Limitations and future research Three limitations qualify the analysis and point toward future work. First, the study is conceptual and interpretive; its propositions organise existing knowledge but have not been tested, and they invite empirical examination. Bibliometric and content-analytic studies could assess the degree and drivers of curricular convergence implied by P3; tracer and labour-market studies could disentangle the signaling and human-capital effects in P2; and comparative case studies could examine the hybridisation described in P5. Second, the literature drawn upon is weighted toward English-language scholarship and core-region journals, which may under-represent work from regions where hospitality education is growing fastest; incorporating Global South perspectives would test whether the framework holds where the core–periphery dynamic is most consequential. Third, several recent sources reflect the pandemic period and may over-index on crisis and online themes; longitudinal work is needed to determine which of these shifts are durable. Beyond addressing these limitations, future research could examine how artificial intelligence is reshaping the field’s knowledge base and whether the augmentation orientation in P6 survives sustained automation, and how micro-credentials and lifelong-learning arrangements interact with the traditional degree. 8. Conclusion Within a century, hotel management moved from tacit craft to academic field. This article has argued that the transition is best understood as the joint outcome of several social mechanisms: a professional project that codified knowledge to claim legitimacy, credential signaling that sustained a degree market, institutional isomorphism that made programmes converge, the conversion of educational capital into status, and the diffusion of models across an unequal world system, with service-dominant logic and digital transformation now redefining the knowledge base. The contribution is an integrative framework that connects the field’s history to these mechanisms and a set of propositions through which the framework can be tested. Education did not displace experience; it framed, scaled, and legitimised it, producing a manager who is at once practitioner and analyst. Recognising the social processes behind this transformation can help educators design curricula that hold analytical rigour and operational empathy together, and can give the field a clearer account of its own intellectual maturity. References Altinay, L. and Taheri, B. (2019), “Emerging themes and theories in the sharing economy: a critical note for hospitality and tourism”, International Journal of Contemporary Hospitality Management, Vol. 31 No. 1, pp. 180–193. https://doi.org/10.1108/IJCHM-02-2018-0171 Amin, I., Yousaf, A., Walia, S. and Bashir, M. (2022), “What shapes e-learning effectiveness among tourism education students? An empirical assessment during COVID-19”, Journal of Hospitality, Leisure, Sport & Tourism Education, Vol. 30, 100337. https://doi.org/10.1016/j.jhlste.2021.100337 Arun, T.M., Kaur, P., Bresciani, S. and Dhir, A. (2021), “What drives the adoption and consumption of green hotel products and services? A systematic literature review of past achievement and future promises”, Business Strategy and the Environment, Vol. 30 No. 5, pp. 2637–2655. https://doi.org/10.1002/bse.2768 Busulwa, R., Pickering, M. and Mao, I. (2022), “Digital transformation and hospitality management competencies: toward an integrative framework”, International Journal of Hospitality Management, Vol. 102, 103132. https://doi.org/10.1016/j.ijhm.2021.103132 Demirciftci, T., Cetin, G. and Bilgihan, A. (2017), “Coping with RM challenges in hospitality education”, Journal of Revenue and Pricing Management, Vol. 16 No. 5, pp. 499–512. https://doi.org/10.1057/s41272-017-0086-0 Denizci Guillet, B. (2020), “An evolutionary analysis of revenue management research in hospitality and tourism: is there a paradigm shift?”, International Journal of Contemporary Hospitality Management, Vol. 32 No. 2, pp. 560–587. https://doi.org/10.1108/IJCHM-06-2019-0515 Denizci Guillet, B. and Chu, A.M.C. (2021), “Managing hotel revenue amid the COVID-19 crisis”, International Journal of Contemporary Hospitality Management, Vol. 33 No. 2, pp. 604–627. https://doi.org/10.1108/IJCHM-06-2020-0623 DiMaggio, P.J. and Powell, W.W. (1983), “The iron cage revisited: institutional isomorphism and collective rationality in organizational fields”, American Sociological Review, Vol. 48 No. 2, pp. 147–160. https://doi.org/10.2307/2095101 Doborjeh, Z., Hemmington, N., Doborjeh, M. and Kasabov, N. (2022), “Artificial intelligence: a systematic review of methods and applications in hospitality and tourism”, International Journal of Contemporary Hospitality Management, Vol. 34 No. 3, pp. 1154–1176. https://doi.org/10.1108/IJCHM-06-2021-0767 Gaur, L., Afaq, A., Singh, G. and Dwivedi, Y.K. (2021), “Role of artificial intelligence and robotics to foster the touchless travel during a pandemic: a review and research agenda”, International Journal of Contemporary Hospitality Management, Vol. 33 No. 11, pp. 4079–4098. https://doi.org/10.1108/IJCHM-11-2020-1246 Goel, P., Kaushik, N., Sivathanu, B., Pillai, R. and Vikas, J. (2022), “Consumers’ adoption of artificial intelligence and robotics in hospitality and tourism sector: literature review and future research agenda”, Tourism Review, Vol. 77 No. 4, pp. 1081–1096. https://doi.org/10.1108/TR-03-2021-0138 Gupta, V., Roy, H. and Sahu, G. (2022), “How the tourism & hospitality lecturers coped with the transition to online teaching due to COVID-19: an assessment of stressors, negative sentiments and coping strategies”, Journal of Hospitality, Leisure, Sport & Tourism Education, Vol. 30, 100341. https://doi.org/10.1016/j.jhlste.2021.100341 Gursoy, D. and Chi, C.G. (2020), “Effects of COVID-19 pandemic on hospitality industry: review of the current situations and a research agenda”, Journal of Hospitality Marketing & Management, Vol. 29 No. 5, pp. 527–529. https://doi.org/10.1080/19368623.2020.1788231 Ivanov, S., Gretzel, U., Berezina, K., Sigala, M. and Webster, C. (2019), “Progress on robotics in hospitality and tourism: a review of the literature”, Journal of Hospitality and Tourism Technology, Vol. 10 No. 4, pp. 489–521. https://doi.org/10.1108/JHTT-08-2018-0087 Kimes, S.E. (2011), “The future of hotel revenue management”, Journal of Revenue and Pricing Management, Vol. 10 No. 1, pp. 62–72. https://doi.org/10.1057/rpm.2010.47 Spence, M. (1973), “Job market signaling”, The Quarterly Journal of Economics, Vol. 87 No. 3, pp. 355–374. https://doi.org/10.2307/1882010 Vargo, S.L. and Lusch, R.F. (2004), “Evolving to a new dominant logic for marketing”, Journal of Marketing, Vol. 68 No. 1, pp. 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036 Wilensky, H.L. (1964), “The professionalization of everyone?”, American Journal of Sociology, Vol. 70 No. 2, pp. 137–158. https://doi.org/10.1086/223790 Zopiatis, A., Papadopoulos, C. and Theofanous, Y. (2021), “A systematic review of literature on hospitality internships”, Journal of Hospitality, Leisure, Sport & Tourism Education, Vol. 28, 100309. https://doi.org/10.1016/j.jhlste.2021.100309 #HotelManagement #HospitalityEducation #HospitalityHigherEducation #Professionalization #InstitutionalIsomorphism #ServiceDominantLogic #RevenueManagement #SustainableHospitality #ExperientialLearning #Bourdieu #WorldSystemsTheory #Credentialism #TourismManagement #HospitalityResearch #AIinHospitality #DigitalTransformation #CurriculumDesign #AcademicResearch #SociologyOfProfessions #HigherEducation