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H5: Organizational Adaptive Capacity positively mediates the relationship between
SME AI Readiness and Regenerative Tourism practices.
H6a: Governance and Policy Support positively moderates the relationship between
Organizational Adaptive Capacity and Destination Resilience; such that the relationship is
stronger when policy support is high.
H6b: Governance and Policy Support positively moderates the relationship between
Organizational Adaptive Capacity and Regenerative Tourism practices; such that the
relationship is stronger when policy support is high.
3. Research methodology
3.1. Research design
This study adopts a quantitative, cross-sectional research design to empirically test
the proposed conceptual model and hypotheses. A quantitative approach is deemed
highly appropriate for this research as it allows for the statistical validation of the causal
relationships between SME AI readiness, organizational adaptive capacity, destination
resilience, and regenerative tourism practices across a large sample of tourism
enterprises.
3.2. Sampling and data collection
The target population for this study comprises managers, owners, and IT directors
of small and medium-sized enterprises (SMEs) operating within the tourism and
hospitality sector (e.g., boutique hotels, homestays, local travel agencies, and
independent restaurants) in Ho Chi Minh City, the largest economic and tourism center in
Southern Vietnam. A purposive sampling technique will be employed, selecting
participants who are directly in charge of tourism businesses and have initiated the
adoption of at least one form of AI technology. To ensure high reliability and sufficient
statistical power for Partial Least Squares Structural Equation Modeling (PLS-SEM), the
study targets a final valid sample size of N = 250 respondents. This sample size well
exceeds the "10-times rule" commonly recommended for PLS-SEM (Hair et al., 2014).
Furthermore, given that the maximum number of arrows pointing at a latent variable in
this model is 3, a sample size of 250 significantly surpasses the minimum threshold
(approx. 59 cases) required to achieve a 5% significance level and an 80% statistical power
for detecting meaningful effects
3.3. Measurement instruments (Scale development)
The survey instrument will consist of measurement scales adapted from established
literature, contextualized for the AI and tourism domain. All items will be measured using
a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).
SME AI Readiness (AIR): Adapted from technology readiness and smart tourism
literature, measured as a second-order construct with three dimensions:
- Predictive analytics: "Our firm uses AI-driven tools to forecast tourist demand and
optimize pricing."
- Service personalization: "We deploy AI applications (e.g., chatbots, virtual
assistants) to provide customized recommendations to guests."
- AI-Oriented human capital: "Our employees are willing and trained to collaborate
with AI systems in their daily tasks."
Organizational adaptive capacity (OAC): Adapted from organizational resilience
literature, measuring the firm’s agility:
- "Our firm can rapidly adjust service delivery processes in response to sudden
market disruptions."

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