Digital inclusion, trust, and experience quality: a conceptual framework for smart tourism technology acceptance in fast-digitalising destinations, with illustration from Uzbekistan

1 Introduction

Digital transformation is usually studied as something that happens to a population inside a jurisdiction, and the resulting inequality is measured against the national baseline that produced it (Helsper, 2012; Van Deursen and Helsper, 2015). International travel breaks that frame. When entry to a country, payment inside it, and interpretation of its heritage run through connected systems, a person formed by one digital environment must operate inside another, under rules they cannot read and with instruments that may not work. Tourism is the largest everyday setting in which this occurs, and a revealing site for studying how digital infrastructures redistribute advantage across, and not only within, populations.

Research on tourists’ acceptance of these systems is extensive. It draws mainly on the Unified Theory of Acceptance and Use of Technology and its consumer extension (Venkatesh et al., 2012), on attribute models of smart tourism technologies (Huang et al., 2017; Yoo et al., 2017), and on studies linking perceived technology quality to satisfaction, memorable experience, and loyalty (Azis et al., 2020; Jeong and Shin, 2020; Um and Chung, 2021); a systematic review confirms its convergence on a small set of belief constructs supplemented by trust and satisfaction (Zhang et al., 2025).

The empirical base of that literature is narrow in one respect that matters for theory. This is our reading of the settings reported in the studies cited and in the corpus of Zhang et al. (2025), which is dominated by East Asian, European, and North American studies of respondents who had already used the technology; it is not the product of a systematic review conducted for this article. In those settings, connectivity is mature, the tourist and destination often share a regulatory and linguistic environment, and respondents have already crossed the thresholds that make technology usable. Under those conditions, it is reasonable to treat capability as a facilitating condition, trust as a single belief, and experience quality as the last term in a causal chain. We argue that those conditions do not hold where smart tourism is expanding fastest (Section 2.1).

Uzbekistan illustrates the mismatch. Internet penetration reached about 89% at the start of 2025 (DataReportal, 2025); the country entered the very high group of the E-Government Development Index in 2024 at rank 63 (United Nations Department of Economic and Social Affairs, 2024); the statistics agency records 8.2 million foreign citizens entering for tourism purposes in 2024 (Statistics Agency, 2025); and the tourism product is concentrated in four inscribed heritage cities (Section 7). Connectivity of that order sits alongside unevenly distributed competence and an inbound market arriving with foreign devices, payment instruments, and languages, and with no prior exposure to the platforms that mediate entry and registration. Acceptance here is a sequence of decisions made by people who differ in what they can do, in whom they rely on, and in what they notice when something fails.

This article is a conceptual analysis and reports no new data. It reopens three constructs that acceptance research has treated as settled and specifies how they relate when a destination is digitally advanced in infrastructure and uneven in use. Throughout, we distinguish findings established in the literature, propositions derived from the framework and marked as ours, and contextual observations about Uzbekistan that motivate the illustration but are not tested in the framework. The claim that two visitors may report identical intention and obtain very different outcomes is a proposition of the second kind (Proposition 8), not a prior finding, and Section 8 specifies the design under which it could be rejected.

Section 2 reconstructs the inherited assumptions and states how the framework differs from the models it builds on. Section 3 recasts digital inclusion, separating the two tiers that gate use from the tier that follows it. Section 4 separates trust into four referents linked by association-conditioned transfer and by calibration, drawing on institution-based trust theory (McKnight et al., 2002), trust transfer (Stewart, 2003), and expectation disconfirmation and attribution (Oliver, 1980; Weiner, 1985). Section 5 argues that experience quality is recursive and asymmetric. Section 6 assembles the framework, its bridging mechanisms, measurable boundary conditions, and the case against it. Sections 7–9 give the illustrative setting, the research agenda, and the discussion.

1.1 How literature was assembled

Because this is a conceptual analysis rather than a systematic review, the literature was assembled purposively around the three gaps the article addresses, in three steps that the reference list makes checkable. First, three published reviews were taken as anchors, one for each body of work: the systematic review of technology adoption determinants in tourism (Zhang et al., 2025), supplemented by the 10-year retrospective of the smart tourism concept (Koo et al., 2025a); the systematic review of the second- and third-level digital divide (Scheerder et al., 2017), supplemented by the corresponding fields and sequentiality arguments (Helsper, 2012; Van Deursen et al., 2017); and, for trust, the integrative typology of e-commerce trust (McKnight et al., 2002) with the trust transfer and initial trust studies it connects to (McKnight et al., 1998; Stewart, 2003) and the tourism application by Tussyadiah et al. (2020). Second, backward citation searching from these anchors supplied the foundational sources on which the framework's mechanisms rest (Baumeister et al., 2001; De Matos et al., 2007; Heckman, 1979; Oliver, 1980; Sen, 1999; Weiner, 1985), and forward citation searching supplied the empirical smart tourism studies of 2017–2025 reviewed in Section 2. Third, work on the Uzbek setting was drawn from the authors' own and colleagues' published research on Bukhara and on national digital and tourism policy (Dushanova et al., 2025; Farmanov et al., 2025; Jafarova et al., 2026; Radjabov et al., 2025) and from official statistical and regulatory sources. Sources were retained if they established a mechanism the framework uses or documented a boundary condition it must respect; no claim of exhaustiveness is made.

2 What acceptance research currently assumes

2.1 From technology attributes to interconnected ecosystems, and where DITAE departs

Conceptual work has moved from smart tourism as the data-centred integration of infrastructure, social sources, and institutional records (Gretzel et al., 2015) to interlinked ecosystems of firms, platforms, and public agencies (Buhalis et al., 2023), real-time co-creation (Buhalis and Sinarta, 2019), and, most recently, a revised model in which governance, sustainability, accessibility, and innovation are the organising pillars (Koo et al., 2025a). Adoption has moved through successive technology waves without changing the theoretical apparatus (Buhalis et al., 2024), with smaller enterprises' capacity a standing constraint (O’Connor, 2023) and the pandemic an accelerant (Sigala, 2020). Measurement has been more conservative. The dominant operationalisation treats smart tourism technologies as a second-order construct of informativeness, accessibility, interactivity, and personalisation, sometimes with security (Huang et al., 2017; Yoo et al., 2017), and reports consistent associations with satisfaction, memorable experience, and revisit intention across settings (Azis et al., 2020; Balakrishnan et al., 2023; Jeong and Shin, 2020; Pai et al., 2020, 2021; Um and Chung, 2021; Zhang et al., 2022), with technology readiness (Shin et al., 2021) and privacy and safety risk (Afolabi et al., 2021; Ngan et al., 2025; Omar et al., 2025) as moderators.

Attributes in this literature are measured as perceptions of a system respondents have already used. This is a limitation of the sampling frame, and it becomes a statistical selection problem only when the process determining who enters the frame is correlated with the outcome modelled; Section 3.1 argues that in rapidly digitalising destinations that condition is likely to hold and specifies the process. The attribute model therefore describes those who reached and attempted the system and is largely silent about those who did not, and about those who attempted and obtained nothing.

Table 1 places the DITAE framework against the four bodies of theory it draws on. The framework does not replace UTAUT2 or the attribute model; it changes the population over which they are estimated, the number of trust objects they admit, and the temporal position of experience quality.

Table 1. Existing models and the DITAE framework: what each explains, what each assumes, and what DITAE adds.

Source: Prepared by the authors on the basis of the literature reviewed in Sections 2–5.

2.2 Three inherited assumptions

The first assumption concerns capability. In UTAUT, facilitating conditions capture the resources and support a person believes are available and are one predictor among several (Venkatesh et al., 2012); tourism applications retain that position, adding demographic controls. The specification implies that capability shifts adoption at the margin. Digital inequality research suggests a stronger claim: capability is layered, and disadvantage at one layer propagates to the next (Scheerder et al., 2017; Van Deursen et al., 2017). The claim made here is narrower than the strong reading that capability determines whether any evaluation can occur. A visitor with low competence may complete a task with a companion, receptionist, or guide and form an opinion in doing so. What low competence gates is the unassisted evaluation by the person the acceptance model describes. Assisted completion is a channel substitution: the evaluation formed is partly of the assistance, the outcome is attributable to the helper as much as the system, and Section 3.3 records it as a distinct state.

The second assumption concerns trust. Tourism studies show trusting beliefs forming through cognitive appraisal and leading to adoption intention (Ali et al., 2023; Koo et al., 2025b; Tussyadiah et al., 2020), but they share a single trust object. Information systems research established long ago that trust is multidimensional and that beliefs about a vendor rest on institution-based trust in surrounding structures (McKnight et al., 2002). In a domestic encounter, simplification is defensible. In a cross-border encounter, the object multiplies: the traveller must judge an intermediary platform, a payment infrastructure, a local supplier, and a regulatory regime whose data protection standards are unknown (Hall and Ram, 2020). Section 4.1 explains why these collapse to four referents.

The third assumption concerns experience quality, almost always modelled as an effect of perceived technology quality (Azis et al., 2020; Jeong and Shin, 2020). Yet a trip is not a single transaction: a visitor failed by a ticketing application at 9:00 in the morning and carries that information into every later encounter of the day. Experience is an input to subsequent acceptance as well as an output of earlier acceptance, and the models in use do not express the loop. What distinguishes this feedback from post-use satisfaction is its timing and object: it is measured at the touchpoint, before the trip ends, and it revises beliefs about technologies and referents not yet used (Section 5.1).

Table 2 states these assumptions, the difficulty each creates, and the reframing developed below. Its final row records an idea developed in Section 6: context enters as a generative condition that specifies which referents exist and which fallbacks are available, and therefore which model structure is appropriate, rather than as a covariate that shifts coefficients.

Table 2. Assumptions in prevailing acceptance research and the reframing proposed in this article.

Source: Prepared by the authors on the basis of the literature reviewed in Section 2.

3 Digital inclusion as a constitutive antecedent

3.1 Three tiers: two gates and one outcome

Digital inequality scholarship distinguishes a first level of material access, a second level of skills and uses, and a third level of tangible benefits (Van Deursen and Helsper, 2015); research remains concentrated on sociodemographic predictors of skills, with outcomes least studied (Scheerder et al., 2017); the levels are compound and sequential (Van Deursen et al., 2017); and offline exclusion maps onto digital exclusion in matching domains (Helsper, 2012).

The third level is best understood through the capability approach, in which possession of a resource is distinct from the freedom to convert it into a valued outcome (Sen, 1999): a tourist holding a smartphone and a booking reference possesses the resource; whether it converts into entry to a monument is a separate question. Conversion is used in this sense and differs from two neighbouring concepts. It is not marketing conversion, a platform-side funnel transition such as a completed purchase; a purchase yielding an unusable ticket is a marketing conversion but not a conversion here. Nor is it usability or successful use, which concern task completion; conversion asks whether completion delivered what was needed. Digital benefit and outcome, the third-level divide terms, are cumulative gains from general use over months, whereas conversion here is task-specific and immediate.

A consequence of this definition is temporal, and it governs the architecture of the framework. Conversion is an achieved outcome of interaction with technology: a tourist cannot ordinarily have converted before initial use has occurred. The three tiers therefore do not all stand before acceptance. Two of them gate use and one of them follows it, and the terminology below is adjusted accordingly.

3.1.1 Tier 1, functional access

The material and environmental conditions under which a technology can be reached and read at all: a functioning handset, adequate data at the place and moment of need, battery and roaming provision, and an interface language the traveller can read. The term is wider than connectivity. The criterion for assigning an indicator to Tier 1 is that it is a condition of the environment or equipment, external to the person's skills, that determines whether the interface can be reached and read; an interface presented only in a script the traveller cannot read is such a condition, because no skill makes an unreadable screen usable. Access in tourism is intermittent, since the traveller crosses coverage zones, operators, and tariff regimes within a day.

3.1.2 Tier 2, enacted competence

Tier 2 (enacted competence) comprises the operational, informational, and transactional skills required to complete a task, exercised under the Tier 1 conditions that obtain. The criterion is that the indicator is a property of the person that determines whether a reachable, readable interface can be operated to completion. Operational competence covers navigation; informational competence covers judging whether retrieved content is credible; transactional competence covers completing an authenticated action such as a visa application or card payment, the most demanding because errors are costly and often irreversible. The qualifier enacted matters for the sequence: a competent person remains competent when the network fails, so Tier 1 conditions gate not competence itself but its exercise in the task at hand. Where an indicator could belong to either tier, assignment follows the locus of the obstacle: a foreign card declined by a domestic acquirer is a Tier 1 access failure, whereas a mis-keyed authentication code is a Tier 2 competence failure.

3.1.3 Tier 3, realised conversion

Tier 3 (realised conversion) comprises the realisation of benefit after use. A tourist who operates a system but does not obtain the intended result, whether a valid ticket, a comprehensible interpretation, or a refund, has not converted use into value. Conversion is the tier at which inclusion becomes visible in outcomes, and the one acceptance research rarely measures. Because it is post-use, it is not a gate on acceptance but the outcome against which acceptance beliefs are compared (Proposition 8). Conversion opportunity, the pre-use state in which Tiers 1 and 2 are satisfied, is a gate; realised conversion, the post-use state in which value was obtained, is an outcome.

Figure 1 conveys the point that gives the tier structure its analytical force, and it makes the selection process precise. Six states can be distinguished for any tourist–task pair: (i) exposure, in which the task is one the destination expects to be performed digitally; (ii) access, Tier 1 conditions are met; (iii) attempted use, unassisted; (iv) completed use, with or without assistance; (v) realised benefit; and (vi) inclusion in a research sample. Conventional surveys recruit from states (iii) to (v), exclude everyone held at (i) or (ii), and include people at (iii) and (iv) who never reached (v). The estimated coefficients therefore describe attempters and completers, and mix converters with non-converters unless the outcome is recorded. Bias arises whenever the unobserved determinants of reaching (iii) are correlated with the acceptance beliefs modelled, which is plausible because the competence that permits an unassisted attempt also shapes effort expectancy and perceived usefulness. Correcting it requires, at minimum, observation of tourist–task pairs held at (i) and (ii), which application-based recruitment cannot supply, together with variables that predict gate passage but not acceptance beliefs. Selection correction (Heckman, 1979) is one route and stratified estimation by tier another; the framework does not adjudicate between them (Section 8.2). The point is not that acceptance research is careless, but that capability has been treated as a covariate rather than as a selection mechanism.

3.2 Inclusion is situational, not only demographic

Digital vulnerability in tourism has usually been attributed to fixed characteristics, chiefly age (Chen et al., 2024; Mubarak and Suomi, 2022; Tuomi et al., 2023), disability (Cho and Kim, 2022), and rural location (Esteban-Navarro et al., 2020), with the unequal diffusion of smartphone use in travel pointing the same way (Shin and Baek, 2023).

A recent reconceptualisation argues that digital vulnerability in smart tourism is dynamic and context-dependent, and that international tourists face vulnerabilities of their own through interface complexity, cultural unfamiliarity, and a lack of support (Shi et al., 2025). This is the position adopted here. A software engineer of 30 who cannot read the Latin-script Uzbek labels of a kiosk, whose foreign card is declined, and whose roaming data is throttled is, at that moment, digitally excluded. Vulnerability is produced by the encounter rather than carried into it, as Bukhara fieldwork showing physical and digital accessibility to be separate problems also indicates (Dushanova et al., 2025).

If inclusion is situational, it must be measured at the touchpoint, and the unit of observation becomes the tourist–task pair; affordance theory supplies the vocabulary, since an affordance is a relation between the environment and the person rather than a property of either (Shi et al., 2025; Tomej and Xiang, 2020). Because several pairs come from the same tourist, and tourists are nested within sites, observations are not independent; any test using the tourist–task pair must treat tasks as occasions nested within tourists and estimate accordingly (Section 8.2).

3.3 Touchpoints and characteristic failure modes

Following the division of tourist communication into the periods before, during, and after the visit (Radjabov et al., 2025), Table 3 maps the tiers onto the stages of an international trip to a heritage destination and identifies the characteristic failure mode of each combination. Failures that look alike from the outside, since in each case the tourist stops using the system, have different causes and remedies. The final column separates indicators that measure the tier construct from indicators that are diagnostic only.

Table 3. Digital inclusion tiers mapped to journey stages, with characteristic failure modes and observable indicators.

Source: Prepared by the authors. Failure modes are derived from the tier definitions in Section 3.1 and from evidence on digital vulnerability reported by Shi et al. (2025), Tuomi et al. (2023), and Shin and Baek (2023). Measurement indicators are observed conditions or binary states, not reflective scale items (see Section 8.1).

Proposition 1 Functional access and enacted competence operate as sequential pre-use gates rather than as marginal predictors: access conditions the exercise of competence, and both condition whether an unassisted attempt at use occurs. Acceptance estimates obtained only from tourists who attempted use are therefore biased with respect to the visiting population whenever the determinants of passing the gates are correlated with acceptance beliefs; observing the gated population is a condition for estimating acceptance in this class of destination.

Proposition 2 Situational inclusion, measured at the tourist–task pair, has incremental explanatory value for attempted use, completed use, and continued use beyond dispositional digital capability measured at the level of the individual. The theoretical basis is that dispositional measures capture the person and not the person-environment relation in which the task is performed (Section 3.2); the proposition is stated as incremental value rather than as a comparison of explained variance, because the latter depends on measurement reliability and sample variance rather than on theory.

4 Trust as a layered, multi-referent judgement

4.1 Four referents

Trust research in tourism technology has established the basic mechanism: trusting beliefs about intelligent systems form through cognitive appraisal, are shaped by a propensity to trust and by negative attitudes towards technology, and lead to adoption intention (Tussyadiah et al., 2020; see also Ali et al., 2023; Ivanov and Webster, 2020; Koo et al., 2025b; Tussyadiah, 2020).

What remains underspecified is the object. Information systems research distinguishes trusting beliefs about a specific party from institution-based trust in the structures that make a transaction safe, and shows that they are separable (McKnight et al., 2002). Two points matter here. First, institution-based trust in that sense is trust in structural assurance and situational normality, without an identified actor. Trust in the state, as used below, has an identifiable object, the government and its agencies, whose competence, benevolence, and integrity can be judged as those of an actor. The two overlap, since a state that operates the entry platform is also the source of the assurance around it, but they are not equivalent; the referent defined below is the actor-object, and structural assurance is one belief held about it. Second, applying the actor’s distinction to a cross-border encounter yields four referents rather than two, because the intermediary and the peer community are separate actors with reputations of their own.

The set of four is derived from a single criterion: a referent is an actor to whom the visitor can attribute responsibility for the outcome of a technology-mediated task and about whom competence, benevolence, and integrity beliefs can be held (McKnight et al., 2002). The visitor's own device is excluded because it is the visitor's instrument, not an actor; device failure is a Tier 1 condition. The payment rail is not a separate referent because responsibility for it is attributed to whichever actor is perceived to operate it, usually the platform for a booking payment and the state or provider for a kiosk payment. Table 4 sets out the four referents that survive.

Table 4. The four trust referents: object, dimensions, level, and reason for retention.

Source: Prepared by the authors, applying the trusting-belief dimensions of McKnight et al. (2002).

Institutional trust is directed at the state and its agencies as an actor: the electronic visa regime, the registration requirement, the national tourism portal, and the authorities responsible for the data protection rules under which these operate. Its temporal precedence is conditional. It is formed before any commercial referent only for visitors who must pass through a state procedure before or upon arrival: in the illustrative setting, visa-required nationals using the electronic visa portal and, for all visitors, the registration of stay, usually performed by the accommodation provider (Section 7). For visa-exempt visitors in licensed accommodation, the first direct institutional encounter may occur later, or be mediated entirely by the provider.

Platform trust is directed at the intermediary that aggregates supply, holds the booking, and processes the payment; for most international visitors, this is a foreign platform, so the judgement is imported. Where the platform and the provider belong to the same organisation, the two referents collapse for that touchpoint; the distinction is then made by the object named in the item and by attributed responsibility, and where the visitor makes none, the referents are merged in the analysis rather than forced apart.

Provider trust is directed at the hotel, guide, museum, or transport operator that performs the service. It is the referent most closely studied in service research and the one most amenable to local management.

Peer trust is directed at other travellers through user-generated content and ratings and is the referent through which the other three are usually screened. Its object is diffuse, namely a population of contributors, and its beliefs concern the accuracy and disinterestedness of testimony rather than the performance of a service. It is retained because it is the referent to which visitors with low situational competence and no institutional familiarity most often turn (bridging mechanism B1).

4.2 Transfer, its enabling condition, and calibration

Two processes connect the referents (Figure 2). Transfer occurs when a tourist lacking direct experience with a technology borrows a trust judgement from a referent with which they have experience; online, trust moves between organisations perceived as interacting or similar, and institution-based trust is carried from a familiar channel to an unfamiliar one (Stewart, 2003). The relational condition in that finding is part of the mechanism. Trust transfers when the visitor perceives a salient association between the source and the target, such as ownership, endorsement, institutional affiliation, technological integration, similarity, or responsibility for the outcome. A visitor who has completed an electronic visa without difficulty approaches the national tourism application with a favourable prior only if the application is perceived as belonging to, or endorsed by, the same authority. Transfer is efficient, since it economises on appraisal, and fragile, since it is not grounded in the object to which it is applied.

Perceived association also settles direction. Transfer from referents to specific technology is treated as the dominant path in a first encounter, because the visitor has experience of the referents and not of the technology. Other directions are possible and testable: provider to platform when the hotel recommends the booking application; peers to institution when reviews praise the visa process; and technology to referent after use, which is calibration. The dominant direction at a touchpoint is predicted by which objects the visitor has experience of and where the strongest association lies; a design measuring referent trust before the touchpoint, technology-specific trust after it, and perceived association between them estimates the paths separately (Section 8).

Calibration is the return movement: experienced performance revises not only the belief about specific technology but also the belief about the referent from which trust was borrowed. Three established mechanisms supply its foundation, and calibration names their joint operation across the source–target link rather than a new mechanism. Expectation disconfirmation (Oliver, 1980) explains why performance below the level the transferred prior implied produces a downward revision that scales with the gap. Attribution (Weiner, 1985) explains why the revision reaches the referent: the same perceived association that enabled transfer routes the disconfirmation back to the actor held responsible. Reputational spillover (Stewart, 2003) explains why it extends to other technologies associated with the same referent. Calibration is thus distinct from trust repair, which concerns deliberate trustee action after violation, and from general belief updating, in being the revision of a borrowed judgement through the link by which it was borrowed. Whether a single failed payment at a state-operated kiosk degrades a visitor's assessment of the destination's public digital systems as a whole is a prediction of the framework (Proposition 4), not an established finding; the tourism literature documents negativity in service failure but has not, to our knowledge, tested spillover from a technology failure to an institutional referent.

The prediction that transferred trust is less resistant to disconfirmation than trust earned through direct experience rests on the initial trust literature, which characterises trust formed without direct experience as based on thin evidence and therefore unstable relative to knowledge-based trust (McKnight et al., 1998). A transferred belief has no experiential base of its own to absorb a disconfirming event, so the event carries the full weight of the revision. The inference is plausible but not yet tested in this setting, and Proposition 4 is accordingly worded as a prediction that a single design can reject.

For measurement, an item asking whether the respondent trusts the application confounds a borrowed judgement with an earned one; separating the referents and recording the perceived association shows which source carries the trust and is exposed when the technology fails.

4.3 Privacy calculus under jurisdictional unfamiliarity

Privacy concerns influence behavioural intention in smart destinations through perceived risk, control over information, and prior violation experience (Afolabi et al., 2021); trust in providers moderates threat and coping appraisals (Ngan et al., 2025); and tourism has been slow to confront continuous data capture (Hall and Ram, 2020).

The standard privacy calculus weighs perceived benefit against perceived risk. Jurisdictional unfamiliarity disturbs it in a specific way. The international visitor typically cannot evaluate the legal regime governing data collected during the trip, identify the responsible authority, or assess the availability of redress. The risk term is then estimated on two kinds of information: observable platform practice, such as consent screens and visible security cues, and generalised perceptions of the country and its institutions. The framework does not claim that visitors ignore the former. It claims that the weight on the latter rises with unfamiliarity, because platform cues can only be interpreted against a legal regime the visitor understands. Personalisation therefore meets an asymmetry: the benefit is evaluated on the merits of the output while the risk is evaluated partly on a prior that service design cannot readily reach. The evidence is indirect. That political dispositions shape responses to artificial intelligence supports the view that such priors enter the encounter (Van Esch et al., 2022), and the privacy studies above document perceived risk, but neither establishes the weighting under unfamiliarity. Proposition 5 is therefore a hypothesis, and Section 8 specifies an experimental design for it.

On the destination side, most sustainability indicator systems treat communication as a technical component rather than as a strategic instrument (Radjabov et al., 2025), so destinations lack indicators of whether their assurances reach the referent on which visitor trust rests.

Proposition 3 Where entry or registration is mediated by state digital platforms and the visitor perceives a salient association between the state and a downstream smart tourism technology, institutional trust exerts a larger indirect effect on pre-use acceptance of that technology than provider trust, operating through transfer. The relative magnitude is contingent on the perceived association: where the technology is perceived as privately owned and unconnected to the state, provider or platform trust is expected to dominate, and the proposition predicts no institutional advantage.

Proposition 4 Trust in a technology acquired by transfer, without direct experience, is less resistant to disconfirmation than trust acquired through direct experience. Hence, an observed failure of a transferred-trust technology produces a larger downward revision of the technology-specific belief and, through attribution along the perceived association, of the source referent. This is a prediction derived from the initial trust literature and from expectation disconfirmation; it has not been tested in tourism.

Proposition 5 Under jurisdictional unfamiliarity, visitors draw on both observable platform practice and generalised country perceptions in estimating privacy risk, and the weight on generalised perceptions increases with unfamiliarity, so that transparency measures taken by individual providers have a smaller effect on perceived risk for institutionally unfamiliar visitors than for familiar ones. This is a hypothesis requiring an experimental test.

5 Experience quality as a recursive construct

5.1 Definitional position and boundaries

Experience quality is used here to mean the traveller's evaluation of the process through which a technology-mediated task was delivered, formed at or shortly after the touchpoint. It is distinguished from satisfaction, an affective summary judgement, and from memorable tourism experience, a retrospective construct organised around hedonism, refreshment, local culture, meaningfulness, knowledge, involvement, and novelty (Kim et al., 2012). Because the construct is central and sits close to several established ones, its boundaries are fixed here and tabulated in Supplementary Table S1. The most important boundary is with realised conversion: conversion records whether value was obtained; experience quality records how the process of obtaining it was evaluated. A visitor can convert with poor experience quality (the ticket was issued after three failed attempts and a call to the hotel) and can fail to convert with acceptable experience quality (the process was smooth and the ticket was invalid at the gate). Against service quality (Parasuraman et al., 1988), the object is one technology-mediated task at a touchpoint rather than a service organisation, and the evaluation is formed within minutes; against customer experience (Lemon and Verhoef, 2016), it is the touchpoint-level evaluation from which the cumulative construct is built; against technology performance and task success, it is the person's evaluation of them rather than their objective record; against perceived usefulness and ease of use (Davis, 1989), it is an evaluation of one episode rather than a generalised belief that episodes revise; against destination experience, it is one technology-related contributor. The framework keeps these apart in the propositions, in Figure 3, and in the item pool.

Technology acts on the process directly and on memory only through the process: a misdirected wayfinding application degrades experience quality immediately. It may leave no trace in a memorable experience scale administered 3 months later. Empirical work links perceived technology attributes to experience and loyalty (Azis et al., 2020; Balakrishnan et al., 2023; Jeong and Shin, 2020; Wang et al., 2020) but does not establish the direction of dependence within a trip, because measurement is taken once and after the fact. The sequence proposed is: pre-use acceptance (intention) at or before the touchpoint; initial use; experience quality evaluated within minutes; revision of technology-specific trust and of the source referent; and continued use at the next touchpoint. Post-use satisfaction is measured after the last of these; within-trip feedback is measured between the third and the fifth.

5.2 Asymmetry, recovery, and the seamlessness conjecture

Three properties of technology-mediated experience deserve explicit theoretical status. Their evidential standing differs and is stated for each.

The first is asymmetry. Successful operation tends to be absorbed into the expected background of the visit and produces a modest positive increment in evaluated quality, whereas failure is salient, is attributed, and produces a larger negative increment. The general principle is established: negative events are processed more thoroughly, weigh more heavily, and resist disconfirmation more than positive ones of equivalent magnitude (Baumeister et al., 2001). Tourism-specific evidence that a failed digital interaction outweighs a successful one is, however, limited; we are not aware of a study comparing the two increments for smart tourism technologies, and the application is stated as a prediction (Proposition 6a). If it holds, the functional form linking performance to experience quality is not linear, and linear specifications will understate the cost of unreliability and overstate the return to added functionality.

The second is recovery. A meta-analysis found the service recovery effect positive for satisfaction but not for repurchase intention, word of mouth, or corporate image (De Matos et al., 2007). Recovery can repair the immediate evaluation but should not be expected to repair the downstream judgements that destinations care about, and cannot substitute for reliability. A credible non-digital fallback is recovery by channel substitution, and its attenuating effect (Proposition 6b) is a separate claim from asymmetry, resting on the recovery evidence rather than on negativity bias.

The third is what we call the seamlessness conjecture. The better a system performs, the less the traveller notices it and the less able the traveller is to report on it; instruments asking respondents to rate informativeness, interactivity, and personalisation depend on attention that high-performing systems may suppress. This could produce an artefact in which attribute scores fail to rise as objective quality rises, and is one plausible contributor to the modest effects commonly reported. It is presented as a conjecture, consistent with Baumeister et al. (2001) but untested, and not as a proposition; its methodological implication is to pair attribute ratings with logged performance.

Taken together, the three properties imply that experience quality responds mainly to breakdown and to its handling rather than to steady-state performance. That reorientation moves the analytical focus from feature inventories to failure design, which is where destinations with uneven digital inclusion are most exposed.

5.3 Mediation and authenticity in heritage settings

Heritage destinations add a complication. Digital interpretation increases informational access to a monument while inserting a screen between the visitor and the site; it has positive effects on visitor outcomes (Balakrishnan et al., 2023) and value for visitors whose access would otherwise be constrained (Dushanova et al., 2025), but its valuation is heterogeneous, restoring access to meaning for some and displacing an unmediated encounter for others. Interpretive preference is therefore a moderator of the initial-use-to-experience-quality link rather than a proposition, since its direction is not predicted by the framework. It can be measured as a short pre-visit attitudinal scale (illustrative items: I prefer to learn about a monument through a screen or audio guide as I look at it; I prefer to look at a monument without any device in my hand) and behaviourally, as the observed choice at a site offering both a digital layer and an unmediated route, recorded before experience quality is assessed; the behavioural indicator is preferred where available. Both appear in Table 5.

Table 5. Illustrative starting item pool, with measurement logic and the propositions each item serves.

Source: Prepared by the authors. Items are illustrative and require expert review, cognitive interviewing, and validation before use.

Proposition 6a The relationship between smart tourism technology performance and experience quality is asymmetric: the marginal negative effect of a failed interaction on experience quality exceeds in absolute value the marginal positive effect of a successful interaction of comparable magnitude. This applies the negativity asymmetry principle to a setting in which it has not been tested.

Proposition 6b A credible non-digital fallback available at the touchpoint attenuates the negative effect of a failed interaction on experience quality without fully offsetting it, and its attenuating effect on continued use and on the source referent is smaller than its effect on experience quality. This rests on the service recovery evidence and is separate from Proposition 6a.

Proposition 7 Experience quality is recursive within a trip. Evaluated quality at an early technology touchpoint predicts, at later touchpoints in the same trip and net of pre-trip acceptance beliefs: (a) trust in the same technology; (b) trust in the referent from which trust in that technology was transferred, in proportion to the perceived association; and (c) continued use of the same technology and of other technologies perceived as associated with the same referent. No effect is predicted on trust in technologies perceived as unassociated with the source referent, and this null prediction is part of the test.

6 The DITAE framework

Figure 3 assembles the preceding arguments. Digital inclusion enters as a pre-use gate rather than a covariate; trust as a layered construct whose referents are differentially available; acceptance disaggregated into pre-use acceptance (intention), initial use, and continued use, because only the last is post-experiential; experience quality enters after initial use, feeding back to trust and continued use within the trip; and realised conversion functions as a post-use outcome against which intention is compared (Proposition 8). Line style distinguishes four relationships: solid arrows are hypothesised causal paths, the double bar represents a selection relationship that is not a path coefficient, dashed lines temporal feedback, dotted lines indicate moderators drawn to the specific path on which each operates, and the dash-dot line represents a bridging mechanism.

Three features distinguish the framework from a conventional extension of an acceptance model. The relationship between inclusion and acceptance is not additive: the pre-use tiers define the population within which acceptance beliefs are meaningful, so the specification is a selection model or stratified estimation rather than an added path. Trust is a set rather than a scalar, with transfer running from referents to technologies conditional on association and calibration running back along the same link, which predicts that the borrowed-from referent absorbs the damage of a failure. Finally, the model is temporal: the journey stage is the axis along which recursion operates, so cross-sectional post-trip surveys, which collapse the sequence of touchpoints into one retrospective judgement, are not well placed to observe it.

Context enters as a generative condition, as Table 2 anticipated. Which referents exist at a touchpoint, whether the state is visibly involved, whether a fallback is staffed, and whether the interface is readable are properties of the institutional configuration, and they determine which paths in Figure 3 are live. In a mature domestic market with private-platform entry, the institutional referent is absent, and the gates admit nearly everyone, so the framework reduces to a conventional model. The framework models context not by adding a moderator but by specifying which of its own elements are present, which is why the boundary conditions in Section 6.2 are observable configuration variables rather than a country list.

Proposition 8 Among visitors who state a pre-encounter intention to use a smart tourism technology, stated intention converges across the inclusion tiers that the visitors subsequently occupy at the touchpoint, while realised conversion diverges by tier; pre-encounter intention is therefore an unreliable indicator of inclusive digital tourism outcomes. The proposition compares intention measured before the encounter, when the tier position is not yet enacted, with conversion measured after it, which resolves the apparent conflict with Proposition 1: the gating tiers do not prevent a visitor from stating an intention; they prevent the visitor from realising it.

6.1 Bridging mechanisms

Without connecting relationships, the three components would be an aggregation of three extensions. Three bridging mechanisms are therefore stated. They are not additional constructs but relationships between constructs already defined, each testable with the designs in Section 8.

6.1.1 B1, competence–trust substitution

The lower a visitor's enacted competence at a touchpoint, the greater the reliance on transferred and peer trust relative to direct appraisal, because appraisal requires the competence to interpret the system's cues; institutional unfamiliarity strengthens this reliance. The mechanism predicts an interaction: the effect of referent trust on pre-use acceptance is larger for visitors with lower enacted competence. In Figure 3, it is a moderator on the trust-to-acceptance path.

6.1.2 B2, transferred trust and failure

The damage that a failure does to the source referent is largest when initial use rested on transferred rather than experiential trust and no fallback was available, because the transferred belief has no experiential base to absorb the disconfirmation (Proposition 4) and the absent fallback removes the recovery that would localise attribution to the touchpoint (Proposition 6b). Fallback availability therefore attenuates the failure increment on experience quality and compensates for competence limitations by supplying assisted completion; the two effects are separately observable on experience quality and on completed use.

6.1.3 B3, direct tier effects

The inclusion tier affects experience quality and realised conversion directly, through the effort that a task requires and the probability that completion delivers value, not only through acceptance beliefs; a visitor at the margin of Tier 2 who completes a task after several retries has converted with low experience quality, which the acceptance path does not carry. Successful conversion is predicted to revise technology-specific trust directly, and the source referent only through the perceived association, so repeated success strengthens the referent slowly while a single failure weakens it quickly.

6.2 Boundary conditions

The framework is scoped to destinations in which three conditions hold jointly, each paired with a measurable criterion so that scope can be determined empirically. The thresholds are provisional, offered to be revised rather than derived from theory. Table 6 shows how each condition is met in the illustrative setting discussed in Section 7.

Table 6. Contextual conditions in Uzbekistan, their evidential status, and their conceptual leverage for the DITAE framework.

UN DESA, United Nations Department of Economic and Social Affairs. Source: Compiled by the authors from the cited sources.

6.2.1 Condition 1, connectivity high in aggregate but unevenly converted into competence

Criterion: individual internet use at or above 80% of the population, together with a gap between the telecommunications infrastructure and human capital components of the E-Government Development Index of at least 0.05 in favour of infrastructure, or an equivalent national indicator of skills lagging behind access; the second element identifies the configuration in which Tier 1 and Tier 2 effects are separable.

6.2.2 Condition 2, a substantial share of visitors is international and institutionally unfamiliar

Criterion: international arrivals at or above one quarter of arrivals at the sites studied, with the share from states not sharing the destination's language of administration or payment infrastructure recorded separately, since unfamiliarity is a matter of degree.

6.2.3 Condition 3, public digital platforms occupy the entry point

Criterion: At least one state-operated digital procedure (electronic visa, electronic registration of stay, or a mandatory national tourism application) applies to a documented share of international arrivals before or at arrival, with that share recorded and the procedure identified as performed by the visitor or by an intermediary.

Where all three conditions fail, as in a mature domestic market with private-platform entry, the framework collapses towards a conventional acceptance model, which is the appropriate outcome; where connectivity itself is low, the first tier dominates, and the trust and experience components carry little weight. The framework is a middle-range account for a definable class of destinations, not a general theory.

6.3 Considering the counter-case

A framework of this kind should state the strongest argument against itself. Three deserve attention.

The first is parsimony. Existing models are compact, validated, and portable, and adding tiers, referents, and a feedback path multiplies parameters and reduces comparability. The defence is that each addition is tied to a proposition that a single-construct model does not generate, and Section 8 specifies how each could be rejected; if the discriminant validity tests of Stage 1 fail, the parsimonious model should be preferred.

The second is that the phenomena may already be captured: facilitating conditions arguably absorb Tier 1 and part of Tier 2, institution-based trust is present in the literature that the framework cites, and habit carries some of the recursive load (Venkatesh et al., 2012). Absorption is not equivalence. Facilitating conditions are a perception reported by current users, whereas the tiers determine whether unassisted use is attempted, and the two coincide only when non-use is negligible; institution-based trust lacks the actor-object that calibration requires (Section 4.1); and habit accumulates across trips rather than within one.

The third is generalisability. If the framework applies only where public platforms mediate entry, its scope may be narrow enough to question its value as theory. The reply is that the class is large and growing, as visa digitalisation, biometric borders, and national tourism platforms spread across middle-income destinations, and that a framework precise about a defined class is preferable to one that is general and untestable.

Table 7 states the eight propositions and three bridging mechanisms with the theoretical anchor of each, the design that would test it, and the estimand that the design must recover. The estimator is deliberately left open (Section 8.2).

Table 7. Propositions and bridging mechanisms of the DITAE framework, with theoretical anchors, testing designs, and estimands.

Source: Prepared by the authors. Full statements appear in Sections 3–6; anchors are given in the corresponding text. No estimator is prescribed; see Section 8.2.

7 Uzbekistan as an illustrative setting

Conceptual work benefits from a setting that makes an otherwise hidden mechanism observable. Uzbekistan qualifies on four counts, each documented from statistical sources or published studies. Two cautions govern the material. The setting is used illustratively, and no claim is made that the framework has been tested there. In addition, the indicators are macro-level and contextual: they establish that the configuration of Section 6.2 is present, but they are not measures of the framework's constructs, which are individual and task-level and would have to be collected as Section 8 specifies.

The first count is the gap between infrastructure and use. Internet penetration stood at approximately 89% in January 2025 (DataReportal, 2025). The country entered the very high group of the E-Government Development Index in 2024, ranked 63rd with a score of 0.7999, with a telecommunications infrastructure subindex of 0.8769 and an online services subindex of 0.7648 against a human capital subindex of 0.7580 (United Nations Department of Economic and Social Affairs, 2024): the infrastructure component exceeds human capital by 0.1189 on the zero-to-one scale, or about 11.9 points out of 100. Two qualifications apply. The human capital component is built from adult literacy, gross enrolment, and expected and mean years of schooling; it measures general educational attainment, not digital competence, and still less the situational competence of international visitors that Tier 2 concerns. National internet penetration likewise measures residents' access, not a visitor's effective access at a touchpoint. The gap is therefore not a statistical signature of the framework's tiers, but a contextual indicator that infrastructure has advanced faster than the educational base from which digital skills are drawn, the configuration in which Section 6.2 expects Tier 1 and Tier 2 effects to be separable; whether they are is a question for Stage 2.

The second count is institutional mediation of entry and stay, which applies to different visitor categories. The electronic visa portal applies to nationals of states requiring a visa; as of 2025, citizens of about 90 states may enter visa-free for tourism and citizens of about 56 further states are eligible for the electronic visa (International Trade Administration, 2025), so for visa-required nationals the institutional referent is encountered before any commercial referent. Registration of temporary stay applies to all foreign citizens staying beyond three working days, with longer periods for some neighbouring nationals under bilateral agreements; it is performed automatically by licensed accommodation and independently by the visitor through the electronic registration system where accommodation is private (E-mehmon, 2025), so visa-exempt visitors in licensed accommodation may not encounter the state platform directly. The national digital strategy identifies tourism as a priority domain (Jafarova et al., 2026). The institutional referent is thus encountered early and directly by a documented subset of visitors and indirectly by the rest, which is what Proposition 3 requires: perceived state–technology association varies across visitors. The state platform is not an obligatory point of passage for all visitors.

The third count is the composition of demand. The national statistics agency records 8.2 million foreign citizens entering for tourism purposes in 2024, up from 6.6 million in 2023 (Statistics Agency, 2025). The series counts border crossings with tourism as a declared purpose, including short and same-day visits, not overnight leisure tourists in the sense of international tourism statistics, and its largest source markets are neighbouring states, principally Kyrgyzstan, Tajikistan, Kazakhstan, and the Russian Federation, whose visitors are less institutionally unfamiliar than those from further afield. The heterogeneity that the framework needs is nonetheless present: the inbound market spans visitors who share script, language, and payment infrastructure with the destination and visitors who share none, and Latin and Cyrillic scripts coexist in signage and interfaces, so within a single site on a single day the variance in situational inclusion is wide. For Condition 2 of Section 6.2, the share of arrivals from beyond the neighbouring states should be recorded, not assumed.

The fourth count is the heritage character of the product. Uzbekistan has four cultural properties on the World Heritage List corresponding to the historic cities on which the tourism product is concentrated: Itchan Kala in Khiva (inscribed 1990), the Historic Centre of Bukhara (1993), the Historic Centre of Shakhrisyabz (2000), and Samarkand, Crossroad of Cultures (2001) (UNESCO World Heritage Centre, 2025); transnational serial and natural properties are not part of the urban heritage product discussed here. Work on inclusive and accessible smart tourism in Bukhara has identified the tension between digital provision and physical and digital accessibility in a historic fabric (Dushanova et al., 2025), and work on strategic communication in the same setting finds destination communication evaluated through technical indicators rather than through its effect on visitors (Radjabov et al., 2025), leaving the institutional referent poorly instrumented where it carries most weight. Two studies of Bukhara bear on governance. A mixed-methods assessment of smart city readiness in the city reports gaps in interoperability, public participation, and data governance alongside emerging opportunities in transport and municipal services, and proposes digital governance as one of three pillars of a localised readiness model (Farmanov et al., 2025). Stakeholder evidence places the social dimension of digital integration well above the governance dimension, with governance identified by stakeholders as the priority for intervention (Jafarova et al., 2026). Both are perceptual or readiness-level assessments rather than performance statistics, and governance is accordingly described as the stakeholder-identified priority rather than a binding constraint. The pattern is consistent with Section 4, in that institutional trust is load bearing where institutional performance is perceived as weakest, but the consistency is illustrative and not a test.

Uzbekistan is not presented as representative of emerging destinations. Comparable configurations plausibly obtain elsewhere in Central Asia, the Caucasus, and South and Southeast Asia where rapid digital government reform has met tourism liberalisation; establishing the boundaries is the comparative task of Stage 4, using the criteria of Section 6.2.

8 Research agenda and measurement implications

The framework is designed to be falsifiable. This section specifies how it may be tested.

8.1 Measurement priorities and measurement logic

Three measurement problems must be solved before the propositions can be tested, and the measurement logic of each construct must be stated before discriminant validity can be assessed.

Situational inclusion has no established instrument for the tourist-task pair. Tiers 1 and 2 are observed conditions and binary states, not latent traits, so the indicators in Table 3 are formative or observational rather than reflective. Three things are kept apart in the item pool because they are not interchangeable: personal competence, a property of the visitor, measured by task-anchored self-report and observed unassisted completion; person–system fit, the relation between visitor and interface at the touchpoint, measured by affordance-type items; and observed task performance, recorded from logs and observation, which depends on competence, fit, and system state together. An indicator such as first-attempt authentication reflects competence, interface design, payment infrastructure, device compatibility, and system failure jointly and cannot on its own establish which tier failed; it is therefore diagnostic in Table 3. Internet skills instruments (Scheerder et al., 2017) are a starting point for the competence tier but require adaptation to transactional, cross-border tasks.

Referent-specific trust requires items written separately for institutional, platform, provider, and peer objects. The referents are latent and measured reflectively, following the multidimensional treatment for e-commerce trust (McKnight et al., 2002). Because the central claim is that the referent varies, the belief dimension is held constant across referents wherever the object allows: parallel competence, integrity, and benevolence items are written for each actor referent, so that referent differences are not confounded with dimension differences. Peer trust, whose object is diffuse, is measured on accuracy and disinterestedness of testimony, a limit on parallelism acknowledged. The battery must be tested for discriminant validity, and measurement invariance across domestic and international visitors is a precondition for the comparisons Propositions 3 and 5 require. Perceived source–target association is measured alongside, one item per referent–technology pair.

Experience quality and conversion are measured separately at the touchpoint: experience quality as a reflective process evaluation prompted within 30 min, realised conversion as an observed or self-reported binary outcome. Failure and recovery events are captured poorly by post-trip surveys, because unresolved failures are recalled selectively and resolved ones forgotten; experience sampling at touchpoints is the appropriate design, with single-day day reconstruction as a fallback.

Discriminant validity among the inclusion dimensions is established before structural testing by separating tier-specific measures from diagnostic performance indicators; testing whether the tier measures form the predicted sequential pattern (Tier 2 measures are informative only where Tier 1 is satisfied); and confirming that task items load on the intended tier rather than a general difficulty factor, using tasks of varying complexity so that complexity can be partialled out. Table 5 gives an illustrative item pool, a specification of what the constructs would look like in the field rather than a validated instrument, with each item tied to the proposition that it serves.

8.2 Designs, estimands, and sequence

The framework implies several distinct estimands, and no single estimator serves all of them: sequential selection (P1), binary and continuous outcomes (P2, P8), repeated observations within tourists nested within sites (P2, P7), temporal feedback (P7), indirect effects with measured moderators (P3, B1), event-contingent contrasts (P4, P6a, P6b, B2), experimental manipulation (P5), and, at Stage 4, cross-destination and quasi-experimental policy comparison. Table 7 therefore specifies the design and estimand for each claim and leaves the estimator to the empirical study, subject to two constraints that follow from the conceptual argument. Any estimator must respect the nesting of occasions within tourists and tourists within sites, which for the within-trip propositions points towards multilevel or within-person longitudinal specifications rather than a single cross-sectional structural model. Furthermore, because Proposition 1 defines the population over which acceptance effects are meaningful, the estimand for the acceptance paths is a stratified or selection-adjusted effect rather than a pooled path coefficient. Variance-based structural equation modelling may remain useful for the measurement work of Stage 1 and parts of Stage 2, but it is not proposed as the estimator for the framework as a whole.

Because Proposition 8 predicts convergence in intention alongside divergence in outcome, studies reporting only intention will find no effect where the framework predicts a substantial one; outcome measurement is the condition under which the central claim can be observed. Figure 4 sets out the sequence.

9 Discussion

9.1 Theoretical implications

Three consequences follow for how acceptance is theorised. First, treating functional access and enacted competence as pre-use gates changes the estimand from what predicts the decision among those who arrive to who is able to arrive at an unassisted decision, and treating conversion as a post-use outcome supplies the term against which acceptance beliefs are compared; this aligns tourism research with a distinction digital inequality scholarship settled some time ago (Scheerder et al., 2017; Van Deursen and Helsper, 2015). Second, disaggregating the trust object, conditioning transfer on association, and specifying calibration through disconfirmation and attribution locate where the risk of a smart tourism programme sits: where institutional trust is the borrowed source and the association is perceived, the reputational cost of a failed application is borne by the state, which reframes reliability as a governance matter, consistent with recent statements placing governance among the pillars of smart tourism (Gretzel et al., 2023; Koo et al., 2025a). Third, making experience quality recursive and asymmetric moves attention from feature accumulation to failure design, and offers the seamlessness conjecture of Section 5.2 as a candidate explanation for the modest effects of attribute-based models.

9.2 Implications for destination practice and policy

The positions below follow as implications of the framework and would become recommendations only if the propositions survive testing. Destinations might design for the failure path: a credible non-digital fallback at every mandatory touchpoint, staffed rather than signposted, is the intervention that the framework expects to protect both experience quality and the institutional referent, although recovery evidence suggests it would repair the immediate evaluation more reliably than the downstream one (De Matos et al., 2007). Reporting could shift from adoption counts to conversion rates, since task completion, assisted-completion share, and redress resolution correspond to realised inclusion and are already generated by platform back-ends. Trust signalling would be placed at the referent carrying the weight: where the state is perceived as operator, provider-level assurances are expected to have limited reach, which would require destination management organisations to treat communication as a strategic instrument with its own indicators (Radjabov et al., 2025). Interpretation would be offered rather than imposed (Dushanova et al., 2025). Furthermore, capability-building might be treated as tourism infrastructure: where the educational base lags telecommunications provision (United Nations Department of Economic and Social Affairs, 2024), front-line assistance at heritage sites would substitute for visitor competence, and the framework expects larger short-run gains from it than from added platform functionality. Each is a hypothesis about policy to be tested at Stage 4.

9.3 Contribution to research on digital impacts

Beyond tourism, part of the framework speaks to a general problem in the study of digital transformation. Governments and international organisations report progress through indices of infrastructure and service availability, which are informative about supply but not about the distribution of conversion, that is, who among intended users obtains the outcome that the service exists to deliver. The three-tier inclusion structure, with its separation of pre-use gates from post-use conversion, is the component proposed to generalise: the citizen at a welfare office stands in the same relation to its digital front-end as the visitor to the ticketing kiosk. The multi-referent trust structure is not proposed to generalise wholesale, because in a domestic public service the platform and the state usually coincide and the referents collapse; nor is within-trip recursion, because the corresponding dynamics in public service use unfold over months and are better represented by existing models of continued use.

9.4 Limitations

As a conceptual analysis, this article presents no data and offers no empirical test. Several propositions, particularly those concerning asymmetry (P6a), transferred-trust fragility (P4), privacy weighting (P5), and recursion (P7), are stated more precisely than the available evidence can adjudicate and are labelled as predictions or hypotheses where they appear. The three-tier structure is imported from research on general internet use and its applicability to short, high-stakes, cross-border tasks requires validation. The four-referent account may prove reducible; if the actor referents fail to discriminate empirically, scalar treatments would be vindicated, as Section 6.3 states in advance. The bridging mechanisms are a starting point, and the boundary criteria are provisional thresholds. The framework is scoped to the class of destinations defined in Section 6.2, and outside it only the inclusion component is expected to apply. The Uzbek material is illustrative and macro-level; none of it measures the framework's constructs or tests the argument. The literature was assembled purposively rather than systematically (Section 1.1).

9.5 Conclusion

Acceptance research has told us a great deal about which beliefs predict the intention to use a smart tourism technology, and much less about who reaches the point of forming such a belief without assistance, in whom the resulting confidence is placed, and what happens across a trip when the technology does not work. Those questions are secondary where connectivity is uniform, institutions are familiar, and systems rarely fail; they are primary where digital transformation and tourism growth have arrived within the same decade. The DITAE framework offers one way of asking them together, in a form specific enough to be tested and, if the evidence goes against it, to be rejected.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fhumd.2026.1961668/full#supplementary-material

Conceptual boundaries of experience quality relative to adjacent constructs (satisfaction, memorable tourism experience, realised conversion, service quality, customer experience, technology performance and task success, perceived usefulness and ease of use, and destination experience). Supplementary material at the source

Footnotes

Edited by: Vuk Mirčetić, Faculty of Applied Management, Economics and Finance, Serbia

Reviewed by: Yerlan Issakov, Abai University, Kazakhstan

Algooth Putranto, Universitas Dian Nusantara, Indonesia

Mohamed Ben Arbia, Universite de Sousse Institut Superieur de Gestion de Sousse, Tunisia