Algorithmic Nudging and Employee Well-being: A Mixed-Methods Study on the Double-Edged Sword of AI-Driven Management in the Hybrid Work Era

Abstract

Algorithmic nudging through artificial intelligence-driven management has emerged as a transformative force in contemporary hybrid workplaces, offering unprecedented opportunities for personalized performance optimization while simultaneously raising critical concerns about employee autonomy and psychological well-being. This mixed-methods study examined 87 white-collar professionals from Indonesian technology, financial services, and consulting firms to elucidate the complex relationship between algorithmic nudging, job burnout, perceived threat, and workforce well-being. Drawing upon self-determination theory and conservation of resources theory, the study integrated in-depth qualitative interviews (n=32) with quantitative burnout assessments employing the Maslach Burnout Inventory. Results revealed a curvilinear relationship whereby moderate algorithmic nudging implementations demonstrated positive effects on competence satisfaction and task clarity, whereas intensive surveillance and real-time algorithmic interventions paradoxically increased emotional exhaustion and cynicism by undermining autonomy and relatedness. Person-job fit emerged as a critical moderator, with individuals in roles aligned with algorithmic management exhibiting 34% lower burnout compared to misaligned counterparts. The study identified three primary mechanisms through which algorithmic nudging influences well-being: resource depletion (through psychological pressure), autonomy suppression (through constrained decision-making), and relatedness erosion (through surveillance-induced isolation). Contextual factors including organizational transparency, employee agency in system design, and hybrid work flexibility substantially buffered negative effects. These findings suggest that algorithmic nudging represents a double-edged sword requiring calibrated implementation, genuine employee participation in system governance, and human-centric safeguards to maximize productivity gains while protecting psychological well-being in the hybrid work erav