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Consumers report that contextual and strategic actions lift AI adoption in hospitality, and adoption is linked to better technology management, sustainability and cost efficiency. The evidence rests on expert interviews and a survey of 499 AI‑aware consumers, so findings reflect perceptions rather than firm-level causal effects.

Conceptualization of causes and implications of AI adoption behavior in hospitality and tourism: sequential Qual-Quan mixed methods research

Mohammad Alimohammadirokni, Ali Iskender, Nasrin Rasouli · Fetched July 13, 2026 · Journal of Hospitality and Tourism Insights

semantic_scholar correlational low evidence 7/10 relevance Summary only summary available; pdf_status=not_found DOI Source

A mixed-methods study (8 expert interviews; survey of 499 AI-aware consumers) finds that contextual, causal, and strategic conditions predict AI adoption in hospitality and tourism, and higher adoption is associated with perceived improvements in technology management, sustainability, and cost efficiency.

This study develops and validates a multilevel framework explaining how contextual, causal, intervening, and strategic conditions shape AI adoption in hospitality and tourism. Integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), the Diffusion of Innovation (DOI), and the Resource-Based View (RBV), it examines how consumer acceptance relates to organizational outcomes such as sustainability and operational efficiency. A sequential mixed-methods design (Qual → Quan) was employed. Eight expert interviews, analyzed using grounded-theory coding, identified key AI-adoption conditions, which informed a survey of 499 AI-aware consumers. Contextual and causal conditions positively influence AI adoption, while strategic actions – such as training, empowerment, and data analytics – further strengthen adoption. Intervening factors, including cybersecurity concerns and financial barriers, also show positive associations, indicating that consumers perceive these risks as manageable trade-offs. AI adoption significantly enhances organizational outcomes, including technology management, sustainability, and cost efficiency. The study integrates UTAUT, DOI, and RBV into a holistic framework linking micro-level acceptance drivers to macro-level strategic outcomes, extending traditional technology acceptance models toward a process-oriented understanding of AI adoption in hospitality and tourism. Managers are advised to enhance personalization, simplify user experiences, invest in employee training, strengthen cybersecurity, and leverage data analytics to improve performance, reduce costs, and support sustainable operations. This study is among the first to empirically integrate UTAUT, DOI, and RBV into a unified AI-adoption framework in hospitality and tourism, demonstrating how adoption extends beyond consumer intentions to generate strategic organizational outcomes.

Summary

Main Finding

AI adoption in hospitality and tourism is shaped by multilevel conditions — contextual, causal, intervening, and strategic — and when adopted, AI substantially improves organizational outcomes (technology management, sustainability, and cost efficiency). Strategic actions (training, empowerment, data analytics) amplify adoption, while consumers view intervening risks (cybersecurity, financial barriers) as manageable trade-offs. The study integrates UTAUT, DOI, and RBV into a single, process-oriented framework linking individual acceptance to firm-level strategic outcomes.

Key Points

Conceptual contribution: Combines UTAUT (acceptance), DOI (diffusion), and RBV (resources/capabilities) into a unified, multilevel AI-adoption framework.
Empirical result: Contextual and causal conditions positively influence consumer AI adoption; strategic managerial actions further strengthen this effect.
Intervening factors (cybersecurity concerns, costs) correlate positively with adoption — interpreted as consumers perceiving risks as controllable trade-offs rather than absolute barriers.
Organizational benefits: Adoption is associated with improved technology management, greater sustainability practices, and lower operational costs.
Practical recommendations for managers: enhance personalization, simplify user experience, invest in employee training/empowerment, strengthen cybersecurity, and deploy data-analytics capabilities.

Data & Methods

Design: Sequential mixed-methods (Qual → Quan).
Qualitative phase: Eight expert interviews, analyzed via grounded-theory coding to extract key adoption conditions.
Quantitative phase: Survey of 499 AI-aware consumers informed by the qualitative findings.
Analytical focus: Tests of relationships between multilevel conditions (contextual, causal, intervening, strategic), consumer acceptance of AI, and firm-level outcomes (technology management, sustainability, cost efficiency).

Implications for AI Economics

Productivity and cost dynamics: Empirical links between adoption and cost efficiency suggest AI investments can generate measurable operational productivity gains in services industries, supporting models where digital capital substitutes for or complements labor.
Role of complementary investments: The amplifying effect of strategic actions (training, empowerment, analytics) highlights the importance of complementary human-capital and organizational-capability investments to realize returns on AI capital — consistent with general equilibrium models of skilled-biased technical change.
Adoption barriers and transaction costs: Finding that consumers treat cybersecurity and financial barriers as manageable implies these factors act more like bounded frictions than absolute constraints; policy or firm-level risk-reduction strategies can therefore have high marginal returns by lowering perceived adoption frictions.
Diffusion and externalities: Integration of DOI suggests potential spillovers across firms and markets; faster diffusion could yield aggregate welfare gains (consumer surplus, environmental co-benefits via sustainability) but also distributional effects (labor reallocation).
Measurement and policy guidance: The study supports using combined micro (acceptance) and macro (firm outcomes) indicators to evaluate AI deployment impacts. For policy, incentivizing complementary investments (training, cybersecurity standards, analytics access) may increase effective adoption and social returns.
Research directions: Quantify magnitude of cost and productivity gains, model dynamic complementarities between AI capital and human capital, and assess market-level diffusion/externalities to inform macroeconomic AI adoption forecasts and policy interventions.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on cross-sectional survey data of consumer perceptions and a small set of expert interviews; associations are reported but no causal identification strategy (randomization, natural experiment, longitudinal causal design) is used, and outcomes are perceptual rather than objective firm-level performance measures. Methods Rigormedium — The study uses a defensible sequential mixed-methods design (qualitative grounded-theory interviews to inform a reasonably sized survey, n=499) and integrates multiple theoretical lenses, but it relies on a small expert interview sample (n=8), likely non-random/self-selected respondents, cross-sectional self-report measures susceptible to common-method bias, and lacks objective organizational outcome data. SampleEight expert interviews (hospitality/tourism AI experts) analyzed with grounded-theory coding informed survey instrument development; survey of 499 AI-aware consumers in the hospitality and tourism context (cross-sectional, self-reported measures), with no reported longitudinal or firm-level performance data. Themesadoption org_design productivity GeneralizabilityIndustry-specific to hospitality and tourism, Based on consumer perceptions rather than firm-level/employee data, Likely geographic/context-specific (location not specified) limiting cross-country transferability, Self-selected 'AI-aware' respondents introduce selection bias, Cross-sectional design prevents temporal or causal generalization, Small qualitative sample (n=8) limits breadth of expert perspectives

Claims (9)

Claim	Direction	Outcome	Confidence & Evidence	Details
This study develops and validates a multilevel framework explaining how contextual, causal, intervening, and strategic conditions shape AI adoption in hospitality and tourism. Adoption Rate	positive	AI adoption	Reading fidelity high Study strength medium	n=499 0.3
Contextual and causal conditions positively influence AI adoption. Adoption Rate	positive	AI adoption	Reading fidelity high Study strength medium	n=499 0.3
Strategic actions — such as training, empowerment, and data analytics — further strengthen AI adoption. Adoption Rate	positive	AI adoption	Reading fidelity high Study strength medium	n=499 0.3
Intervening factors, including cybersecurity concerns and financial barriers, also show positive associations with adoption, indicating consumers perceive these risks as manageable trade-offs. Adoption Rate	positive	AI adoption	Reading fidelity high Study strength medium	n=499 0.3
AI adoption significantly enhances organizational outcomes, including technology management, sustainability, and cost efficiency (operational efficiency). Organizational Efficiency	positive	technology management, sustainability, cost efficiency	Reading fidelity high Study strength medium	n=499 0.3
The study integrates UTAUT, DOI, and RBV into a holistic framework linking micro-level acceptance drivers to macro-level strategic outcomes, extending traditional technology acceptance models toward a process-oriented understanding of AI adoption in hospitality and tourism. Other	positive	theoretical integration / explanatory scope	Reading fidelity high Study strength medium	n=499 0.3
Managers should enhance personalization, simplify user experiences, invest in employee training, strengthen cybersecurity, and leverage data analytics to improve performance, reduce costs, and support sustainable operations. Organizational Efficiency	positive	organizational performance (performance, cost reduction, sustainability)	Reading fidelity high Study strength speculative	n=499 0.05
This study is among the first to empirically integrate UTAUT, DOI, and RBV into a unified AI-adoption framework in hospitality and tourism, demonstrating how adoption extends beyond consumer intentions to generate strategic organizational outcomes. Other	positive	novelty of empirical integration / linkage to organizational outcomes	Reading fidelity high Study strength speculative	n=499 0.05
The study used a sequential mixed-methods design (Qual → Quan) consisting of eight expert interviews analyzed with grounded-theory coding and a follow-up survey of 499 AI-aware consumers. Other	null_result	study design / methodological procedure	Reading fidelity high Study strength high	n=499 0.5