Unpacking the AI Black Box in HRM: A Serial Mediation Model of Algorithmic Fairness, Trust, and AI Readiness with AI Anxiety as a Functional Predictor

Abstract

This study unpacks the “black box” phenomenon of artificial intelligence (AI) in Human Resource Management (HRM) by examining the dual structural mechanisms of algorithmic fairness and AI anxiety in shaping employees’ adaptive performance within the Ability-Motivation-Opportunity (AMO) framework. Using a quantitative explanatory design, primary data were collected from 285 full-time professionals in the Indonesian e-commerce industry through purposive sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings reveal a significant full serial mediation mechanism in which algorithmic fairness influences adaptive performance entirely through trust in AI and AI readiness. Although fairness strengthens trust in AI, trust alone does not directly enhance adaptive performance; instead, it functions as a cognitive precursor that fosters AI readiness, which subsequently drives adaptive performance. Contrary to conventional assumptions that technological anxiety weakens performance, AI anxiety has a positive direct effect on adaptive performance, suggesting a proactive defensive coping mechanism among employees facing technological disruption and job insecurity. These findings imply that organizations should move beyond merely auditing algorithmic fairness and focus on developing employees’ cognitive readiness for AI adoption through transparent systems and targeted literacy programs that transform anxiety into proactive digital competence. This study contributes to algorithmic management literature by showing that AI readiness, rather than emotional trust alone, is the key functional link in the AI-HRM interface. However, the study is limited by its cross-sectional design, high R-squared values, and focus on a single industry