Paper Type

Short

Paper Number

PACIS2025-1849

Description

While AI-driven High-Performance Work Systems (AI-HPWS) offer the promise of enhanced efficiency and decision-making, concerns remain about algorithmic bias and its implications for justice in the workplace. This study proposes a conceptual model to examine how AI-HPWS influence employees’ perceptions of algorithmic justice and related workplace outcomes. Drawing on justice theory, the study explores how distributive, procedural, informational, and interpersonal justice function within algorithmic HR systems and affect employee experiences. Importantly, it investigates the role of algorithmic justice in shaping employee workplace well-being—a topic that has received limited empirical attention. By addressing a critical gap in algorithmic HRM research, this study provides both theoretical and practical insights into the ethical deployment of AI in HRM and its impact on employees.

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Jul 6th, 12:00 AM

AI-Driven High-Performance Work Systems: Implications for Algorithmic Justice and Workplace Well-being

While AI-driven High-Performance Work Systems (AI-HPWS) offer the promise of enhanced efficiency and decision-making, concerns remain about algorithmic bias and its implications for justice in the workplace. This study proposes a conceptual model to examine how AI-HPWS influence employees’ perceptions of algorithmic justice and related workplace outcomes. Drawing on justice theory, the study explores how distributive, procedural, informational, and interpersonal justice function within algorithmic HR systems and affect employee experiences. Importantly, it investigates the role of algorithmic justice in shaping employee workplace well-being—a topic that has received limited empirical attention. By addressing a critical gap in algorithmic HRM research, this study provides both theoretical and practical insights into the ethical deployment of AI in HRM and its impact on employees.