Algorithmic Reliability at the Helm: Investigating the Relationship Between Experienced Algorithmic Reliability, Trust, and Work Engagement in the Gig Economy - Lu, Ying; Yang, Miles M.; Mitchell, Rebecca; Boyle, Brendan; Gu, Jun (Human Resource Management, 2026)
ABSTRACT In the gig economy, the role of artificial intelligence (AI) in managing human resource functions such as task allocation and performance management is increasingly significant. However, there is limited understanding of how the reliability of these functions, as experienced by workers, impacts their trust and engagement. Grounded in the transactional model of stress and coping, this study examines the influence of experienced algorithmic reliability on gig workers' trust in their platforms and their subsequent work engagement. We further explore how occupational stigma consciousness moderates this mediated relationship. Through a time‐lagged survey of 332 gig workers, our findings indicate that reliable algorithmic management experiences significantly enhance trust and subsequently work engagement. Moreover, this relationship is complicated by occupational stigma consciousness, which can diminish the positive effects of algorithmic reliability on trust and engagement. This study deepens our understanding of technology‐mediated work environments, emphasizing the critical role of workers' experiences with AI‐driven HRM functions in enhancing engagement and well‐being.