Tony Fang, Memorial University of Newfoundland

Jennifer A. Harrison, EM Normandie

Fei Song, Toronto Metropolitan University

Mingwei Liu, Rutgers University

Generative AI is being rapidly integrated into workplace systems and HR processes, fundamentally reshaping how work is performed and productivity should be understood. The collaboration between humans and AI systems in task completion creates new challenges for measuring and attributing productivity outcomes, as traditional assumptions about performance metrics and human effort become outdated in AI-augmented contexts.

This special issue seeks research that reconsiders how productivity is conceptualized, measured, and compared when work involves human-AI collaboration. The call invites studies examining the distinction between AI adoption and actual use, the distribution of performance effects across different worker groups, implications for career development and employability, and the organizational and HR factors that influence AI-augmented productivity. Methodological contributions exploring innovative approaches to studying these phenomena are also welcomed.

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 AI 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

Philippe Aghion, Collège de France

Martin Andersson, Blekinge Institute of Technology

David B. Audretsch, Indiana University

Maksim Belitski, University of Reading

Pontus Braunerhjelm, KTH Royal Institute of Technology

This special issue seeks to advance Schumpeterian research that integrates entrepreneurial activities across multiple levels of analysis. It focuses on connecting individual entrepreneurs and firms with regional innovation ecosystems and industrial dynamics, while also considering broader institutional and policy frameworks.

The issue welcomes research on the full lifecycle of ventures, including formation, growth, contraction, and failure, with particular emphasis on how these dynamics drive productivity improvements. It also invites studies that enrich the Aghion-Howitt growth model with contemporary entrepreneurship research insights.

Appel publié par Small Business Economics. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• New venture formation and post-entry dynamics
• Scaling, displacement, and exit in entrepreneurial firms
• Reallocation dynamics and productivity growth
• The Aghion-Howitt framework and entrepreneurship research
• Multi-level analysis connecting micro (entrepreneurs and firms), meso (regional ecosystems and industrial structure), and macro (institutional configurations and policy regimes) levels
• Innovation and its transformation into productivity growth within the Schumpeterian tradition

Timeline

• November 1, 2026: Submission deadline

Tony Fang, Memorial University

Jennifer A. Harrison, EM Normandie Business School

Artificial intelligence is reshaping organizations by reconfiguring task structures, skill requirements, and managerial processes. Rather than eliminating jobs, AI adoption fosters new occupational roles and redefines expertise through changed patterns of human-AI collaboration. These developments promise to enhance productivity, though realizing these gains depends critically on the human skills that enable effective technology use.

The concept of skills varies significantly across disciplines, from labour economics viewing skills as human capital to organizational psychology emphasizing cognitive capabilities and sociology examining how skills are socially constructed. This fragmented understanding limits integrated knowledge about how AI shapes skills and productivity. The call seeks to bridge these perspectives by examining how skills are identified, defined, and valued in relation to AI, and how they translate into productivity gains across contexts.

Appel publié par Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• AI adoption and implementation in organizations, including how skills are identified, defined, measured, valued, and linked to productivity
• Human and AI collaboration, work transformation, and the identification and use of skills in changing task contexts
• Defining and developing skills in workplaces using AI (e.g., reskilling, upskilling, lifelong learning) and linking to productivity
• Changes in occupations, expertise, and professional roles, and how skill requirements are defined and evolving with AI
• Organizational and institutional factors shaping how skills are identified, defined, and managed in relation to AI and productivity (e.g., leadership, HR practices, policy contexts)
• Career sustainability, mobility, and inequality in labour markets shaped by AI, with a focus on how skills are defined, accessed, and valued

Timeline

• December 15, 2026: Submission Deadline

This special issue focuses on approaches to enhance productivity within health care systems. It seeks submissions that address strategies and innovations aimed at improving the efficiency and effectiveness of health care delivery.

Appel publié par Health Policy. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• December 31, 2026: Submission deadline