This (AI)n't fair? Employee reactions to artificial intelligence (AI) in career development systems - Alina Köchling; Marius Claus Wehner; Sascha Alexander Ruhle (Review of Managerial Science, 2024)
Organizations increasingly implement AI for career development to enhance efficiency. However, there are concerns about employees' acceptance of AI and the literature on employee acceptance of AI is still in its infancy. To address this research gap, integrating justice theory, we investigate the effects of the deciding entity (human, human and AI, and AI) and the impact of the data source (internal data, external data), on employees' reactions. Using a scenario-based between-subject design, displaying a common situation in organizations ( N 0.167em=0.167em280) and an additional causal-chain-approach ( N 0.167em=0.167em157), we examined whether a decrease of human involvement in decision making diminishes employees' perceived fairness and satisfaction with the career development process and increases their perceived privacy intrusion. Although we also considered other data sources to moderate the proposed relationships, we found no support for interaction effects. Finally, fairness and privacy intrusion mediated the influence of the deciding entity and data source on turnover intention and employer attractiveness, while satisfaction with the process did not. By addressing how the employees react to AI in career development–showing the negative reactions, our study holds considerable relevance for research and practice.
Keywords: O15; C99; M10; 650; Artificial intelligence; Employees' reactions; Fairness; Organizational attractiveness; Privacy intrusion; Turnover intentions