Algorithmic dehumanization and safety behaviors in ride-hailing: the role of ego depletion and mindfulness

Within the framework of Conservation of Resources theory, this study explores the psycho-behavioural pathway linking algorithmic dehumanisation to the safety behaviours of ride-hailing drivers (RHDs). We position ego depletion as the mediating mechanism and mindfulness as a potential moderator. Data from a two-wave survey of 209 Pakistani RHDs, analysed with PROCESS Macro, reveal that algorithmic dehumanisation significantly erodes self-regulatory resources, resulting in ego depletion and, consequently, diminished safety compliance and participation. Although mindfulness buffers the experience of ego depletion, it does not directly restore safety performance. This finding underscores a critical gap: individual resilience strategies are insufficient to offset systemic risks embedded in algorithmic control. The study contributes to occupational safety and gig-economy literature by recasting algorithmic dehumanisation as a tangible job demand and risk factor. We conclude with imperative practical recommendations for ride-hailing platforms, including algorithmic transparency, contextual performance metrics, and integrative well-being interventions.