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Hypotheses Path Beta (β) R 2 Conclusion
Moderating effects
H6a GPS * OAC DRE 0.209 - Supported
H6b GPS * OAC RTP 0.170 - Supported
Source: Authors' calculation via SmartPLS 3
4.4. Discussion
The findings of this study offer robust empirical validation for the proposed
conceptual model, significantly advancing the literature on smart tourism by explicitly
linking micro-level technological adoption to macro-level destination recovery. By
addressing the gaps identified in previous literature, this study thoroughly embeds its
empirical results within the Resource-Based View (RBV) and socio-ecological resilience
theory.
First, linking AI readiness to the resource-based view (RBV): The PLS-SEM results
demonstrate a remarkably strong direct effect of SME AI Readiness on Organizational
Adaptive Capacity (H1, β=0.703). This finding provides powerful empirical support for the
RBV theory (Barney & Clark, 2007) in the digital era. Unlike prior studies that
predominantly view technology as a supplementary operational tool for cost reduction,
our findings explicitly establish AI readiness - encompassing predictive analytics, service
personalization, and AI-oriented human capital -as a rare, valuable, and inimitable
strategic resource. Consistent with Huang et al. (2021), this study proves that when SMEs
actively cultivate AI readiness, they do not merely automate tasks; they fundamentally
transform their organizational agility and responsiveness to market volatility.
Second, linking the mediating role of adaptive capacity to socio-ecological resilience
theory: The most critical theoretical contribution of this study lies in confirming the
perfect mediating role of adaptive capacity between AI readiness and destination-level
outcomes (H4, H5). According to socio-ecological resilience theory, resilience is not
merely the ability to "bounce back" to a pre-crisis state, but rather the capacity to
proactively adapt, reorganize, and transform (Bec et al., 2016; Prayag, 2020). Our
empirical results perfectly align with this theoretical lens: AI technology in isolation does
not rescue a destination or regenerate an ecosystem. Instead, AI serves as the micro-
foundation that triggers organizational agility. It is this adaptive capacity that empowers
grassroots SMEs to absorb external shocks, diversify their services during crises, and
actively engage in regenerative practices (Dredge, 2022). This finding bridges the critical
micro-macro gap, demonstrating that destination resilience is an emergent property of
local SME adaptive capacities.
Third, linking the moderating role of governance to smart tourism ecosystems: The
results confirm that Governance and Policy Support (GPS) significantly and positively
moderates the pathways from adaptive capacity to both destination resilience and
regenerative tourism (H6a, H6b). This explicitly aligns with the smart tourism destination
framework (Gretzel et al., 2015), which posits that technological ecosystems cannot
thrive in a vacuum. Even with high adaptive capacity, SMEs require a supportive
institutional environment - such as financial incentives, shared digital infrastructure, and
clear data privacy regulations (e.g., GDPR) - to scale up their micro-level efforts. Our
findings reinforce the argument by C.Hall et al (2021) that risk governance and
institutional support act as mandatory catalysts, amplifying the translation of
technological advantages into comprehensive socio-ecological resilience.

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