Reclaiming humanness in AI-mediated workplaces: decision control, well-being, and the role of algorithmic accountability

Purpose – By integrating social cognitive theory and the job demands-resources model, this study investigates how employees experience artificial intelligence (AI) in the workplace. Design/methodology/approach – Two experimental survey studies were conducted with employees in the United Arab Emirates. Study 1 (N = 520) tested the effects of perceived usefulness on self-efficacy, competence, and decision control. Study 2 (N = 480) extended the model to employee well-being, testing mediation via decision control and moderation by algorithmic accountability. Measurement validity was established through confirmatory factor analysis, and hypotheses were tested using MANCOVA, regression, and PROCESS mediation/moderation models. Findings – Perceived AI usefulness enhanced self-efficacy, competence, and decision control. Both self-efficacy and competence mediated the usefulness-control relationship, with self-efficacy exerting the stronger effect. Algorithmic accountability expanded the positive impact of control on well-being. Originality/value – The study offers a novel theoretical integration and experimental evidence showing how workplace AI meaningfully shapes employees’ capability, control, and well-being.