Purpose This study aims to investigate how managerial practices influence artificial intelligence (AI) post-implementation success in Iran’s tourism and hospitality sector, with transactional and transformational leadership styles as moderators, to optimize AI adoption outcomes. Design/methodology/approach Grounded in Resource-Based View (RBV) and Upper Echelons Theory, data were collected from 218 Iranian tourism and hospitality firms using a survey. Hypotheses were tested via Partial Least Squares Structural Equation Modeling to analyze complex relationships. Findings Training and education, business process re-engineering (BPR) and system integration significantly enhance AI post-implementation success, which positively impacts financial performance. Transformational leadership positively moderates project management’s effect, whereas transactional leadership negatively moderates BPR’s impact. Project management shows no direct effect, highlighting contextual challenges. Practical implications Managers should prioritize training, BPR and system integration to maximize AI benefits. Transformational leadership fosters innovation in AI projects, whereas transactional leadership may hinder creative processes like BPR. Policymakers can support AI adoption through skill development and workforce stability programs. Originality/value This study uniquely integrates RBV and Upper Echelons Theory to examine AI post-implementation success, an underexplored phase, in Iran’s tourism sector. It identifies effective practices and leadership moderation role, offering a novel framework for AI optimization.
Khoshkam et al. (Tue,) studied this question.
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