Clotilde Coron, Université Paris-Saclay

Data is increasingly recognized as a crucial resource for organizational innovation, with extensive and organized data exploitation considered a key driver of the ongoing fourth industrial revolution. HRM is not exempt from this trend, and the methodological challenges of leveraging increasingly diverse and massive datasets present significant stakes for both practitioners and academic researchers.

Recent reflections on applicable methods for extracting value from data often emphasize the disruptive potential of big data, presenting it as the foundation for a renewal of empiricism based on intensive exploration of data masses through exploratory methods, aimed at generating new knowledge through purely inductive logic. However, inductive, data-driven approaches remain rare in quantitative HRM studies, despite their capacity to transcend the classical dichotomy between qualitative/inductive and quantitative/deductive approaches in management sciences. Data-driven methodologies also help address criticisms leveled at hypothetico-deductive approaches, such as their strong standardization and difficulty in generating truly innovative theories.

This special issue aims to provide an updated overview of the challenges and opportunities of data-driven HRM, as well as inductive quantitative methods applicable in empirical research. Expected contributions may address these themes from methodological perspectives (such as presenting an innovative method with illustration in HRM), empirical perspectives (conducting a quantitative study on a subject by adopting an inductive approach), or conceptual perspectives (analyzing the stakes for HRM of data-driven management). Proposals presenting emerging or underutilized methods in francophone quantitative HRM studies will be particularly appreciated, including supervised learning, data mining, textual statistics, and longitudinal approaches.

Potential topics

• Data-driven approaches in HRM
• Inductive quantitative research methods
• Big data exploitation and analysis
• Supervised learning applications in HRM
• Data mining techniques
• Textual statistics
• Longitudinal approaches
• Innovative quantitative methods in HRM research
• Theory building from data
• Emerging methodologies in francophone HRM studies

Timeline

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• March 25, 2023: Submission deadline for articles

This special issue seeks to explore how Large Language Models (LLMs) are transforming methodological approaches in technology management research. We invite submissions that demonstrate innovative applications of LLMs as research tools, methodological enhancers, and theoretical instruments for advancing our understanding of technology management, innovation, and organizational change.

Contributions should address both the opportunities and challenges of incorporating LLMs into research practice, including questions of validity, reliability, ethical considerations, and the development of new theoretical frameworks that account for LLM capabilities and limitations in research contexts.

Potential topics

• Applications of LLMs in technology management research
• LLMs for data analysis and pattern recognition in technology studies
• Novel methodological approaches enabled by LLMs
• Theoretical frameworks for understanding LLM capabilities and limitations in research contexts
• LLMs for literature review and synthesis
• LLM-assisted qualitative and quantitative research methods
• Validation and reliability of LLM-based research findings
• Ethical considerations in using LLMs for academic research
• LLMs for forecasting technological trends
• Integration of LLMs with traditional research methodologies

Timeline

• January 1, 2027: Submission deadline

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This special issue asks a fundamental question about artificial intelligence and social science: do we want it to produce faster, cheaper versions of what we already do, or do we want fundamentally new science? Returning to Organization Science's founding mission—Daft and Lewin's 1990 call to break out of the "normal science straitjacket" and March's "exploration of new possibilities"—we want to shift our focus to how AI is changing the production of science and how it can expand our knowledge, rather than merely increasing the number of papers through efficiency and reduced labor.

In this call for science, we seek contributions that reimagine what a social science research contribution is in an AI-enabled world, encouraging wild ideas and radical innovation over obvious incremental improvement. We are not looking for conventional full-length papers with AI-related content, nor "AI slop"—we want the innovative applications themselves.

The issue follows a three-stage process—a research proposal and prototype, a collaborative development phase with an in-person workshop, and finalization—culminating in short Science/Nature-style articles and shorter "letters," all treated as true peer-reviewed contributions. We welcome submissions from scholars across the social sciences and adjacent fields, so long as they address organizational or managerial implications, broadly interpreted.

Potential topics

• AI-enabled research loops under human direction
• Reusable research infrastructure
• New forms of measurement
• AI-enabled qualitative and theory-building work
• Synthetic social systems
• New approaches to established research designs
• Critical or boundary-setting work on the limits of AI-enabled science
• Reimagining social science research contributions in an AI-enabled world

Timeline

• October 1, 2026: Submissions Open
• November 1, 2026: Submissions Close