Leadership Style, Perceived Fairness, Perceived Competence, and Employee Trust as Sequential Drivers of Acceptance of AI-Driven Decisions: Evidence from Higher Education in Lebanon
The rapid integration of artificial intelligence (AI) into organizational decision-making has generated an urgent need to understand the human conditions that enable or inhibit employee acceptance of AI-driven decisions. Drawing on Mayer, Davis, and Schoorman's (1995) Integrative Trust Model, Social Exchange Theory (SET), and the Technology Acceptance Model (TAM), this study proposes and empirically tests a novel sequential dual-mediation model in which Leadership Style (Transformational vs. Transactional) influences employee Acceptance of AI-Driven Decisions through two parallel first-stage mediators — Perceived Fairness and Perceived Competence — and one sequential second-stage mediator — Employee Trust. Data were collected from 400 academic faculty and administrative staff across higher education institutions in Lebanon using a cross-sectional survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 was employed for analysis. The measurement model demonstrated strong reliability (Cronbach's α: 0.843–0.901) and validity (AVE: 0.571–0.634; HTMT < 0.85). Structural results indicate that transformational leadership exerts significantly stronger positive effects on perceived fairness (β = 0.423, p < 0.001) and perceived competence (β = 0.391, p < 0.001) than transactional leadership. Both mediators significantly predicted employee trust, which in turn significantly enhanced acceptance of AI-driven decisions (β = 0.447, p < 0.001). Full sequential mediation was confirmed for all leadership pathways. The model explained 54.3% of the variance in employee trust and 49.8% of the variance in AI-driven decision acceptance. Findings advance theory by providing the first empirical validation of a sequential dual-mediation model connecting leadership style to AI acceptance, and offer practical guidance for higher education administrators navigating AI adoption.