AI Adoption in Decision-Making and Firm Competitiveness in Tourism: Integrating CTUAT with Resource-Based and Dynamic Capability Perspectives
Li Wenting, Wan Mohd Hirwani Wan Hussain, Jia xinlin, Meng Na, Syed Shah Alam · Journal of Quality Assurance in Hospitality & Tourism · 2026
Artificial Intelligence (AI) is reshaping decision-making in the travel and tourism industry, enhancing efficiency, customer engagement, and strategic planning. However, AI adoption remains fragmented, with limited research on firm-level enablers. This study examines AI adoption in decision-making among Malaysian tourism firms using the Comprehensive Theory of Use and Adoption of Technology (CTUAT), Resource-Based View (RBV), and Dynamic Capabilities Theory. A survey of 343 mid-to-top-level managers was analyzed using SmartPLS-SEM to assess the influence of strategic orientation, organizational resources, employee readiness, and innovativeness on AI adoption, alongside the mediating role of performance effectiveness and the moderating effect of technological turbulence on competitive advantage. Findings reveal that strategic orientation, organizational resources, and employee readiness significantly drive AI adoption, while top management support has no direct impact, challenging conventional technology adoption models. AI adoption enhances competitive advantage, but technological turbulence weakens this relationship, highlighting the need for adaptive AI strategies. The study advances firm-centric AI adoption frameworks, emphasizing organizational agility and digital transformation. Practically, firms must align AI investments with strategic goals, enhance workforce AI readiness, and adopt flexible governance frameworks. Policymakers should foster AI-friendly regulations and financial incentives to support adoption, particularly among SMEs.