Tony Fang, Memorial University

Jennifer A. Harrison, EM Normandie

Fei Song, Toronto Metropolitan University

Mingwei Liu, Rutgers University

Generative AI is increasingly being integrated into organizational systems and work processes, fundamentally reshaping how tasks are executed and decisions are made. This raises important questions about how productivity should be understood and measured when humans and AI systems collaborate to produce outputs together. The challenge lies in attributing outcomes to human effort versus AI contributions when work is augmented by AI.

This special issue seeks research that reconsiders traditional productivity assumptions in the context of AI-augmented work. It invites submissions examining how productivity is conceptualized and measured, how human and AI contributions can be distinguished, and how GenAI affects skill development, career paths, and organizational outcomes. The issue welcomes methodological approaches that employ diverse data sources and longitudinal designs to study these emerging phenomena.

Appel publié par Personnel Review. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Conceptualizing productivity in AI work and careers
• Distinguishing between AI exposure, use, and augmentation
• Measuring human versus AI contributions to outputs and performance
• Changes in the distribution of performance
• Implications of GenAI for skill development, career trajectories, and employability
• HR practices, job design, and organizational context in shaping productivity
• Methodological approaches to studying AI and work

Timeline

• August 31, 2026: Opening date for manuscripts submissions
• January 31, 2027: Closing date for manuscripts submission