The role of STARA competencies in driving AI adoption performance in tourism and hospitality: A systematic-quantitative synthesis of dual mediation analysis of self-efficacy and Techno-Eustress
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Abstract
This study investigates the dual mediation roles of AI self-efficacy and techno-eustress in the relationship between leaders’ STARA (Smart Technology, AI, Robotics, Algorithms) competencies and AI adoption performance in tourism and hospitality. Employing a mixed-methods approach, the research integrates a systematic literature review of 28 peer-reviewed articles with quantitative data from 401 employees in Saudi five-star hotels and tourism firms. The systematic literature review synthesizes conceptualizations of STARA competencies and psychological mediators, while partial least squares structural equation modeling (PLS-SEM) tests hypotheses derived from social cognitive and technostress theories. Results reveal that leaders’ STARA competencies significantly enhance AI adoption performance both directly (β = 0.176) and indirectly via self-efficacy (β = 0.143) and techno-eustress (β = 0.195). The dual mediation model explains 39.3% of AI adoption variance, underscoring the interplay of technical leadership and psychological readiness. The results align with Sustainable Development Goals (SDGs) 8, 9, and 12, linking AI integration to decent work, innovation, sustainable practices, and future economics. The study advances digital leadership theory by integrating psychological mediators into technology adoption frameworks and offers actionable insights for cultivating AI-ready workforces through competency development and stress management.
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