We study how organisations design and implement effective HR strategies that create value for both employees and the organisation. Our work examines the added value of HR investments by integrating data from multiple sources collected within organisations over time, enabling data-driven HR decision-making. We also explore how HR practices operate as part of broader HR ecosystems, where multiple actors, technologies, and contextual factors interact to shape organisational outcomes.

Selected publications:

Biron, M., Boon, C., Farndale, E., and Bamberger, P. (2024). Human Resource Strategy: Formulation, Implementation, Impact. Routledge.

Boon, C., Jiang, K., & Eckardt, R. (2024). The Role of Time in Strategic Human Resource Management Research: A Review and Research Agenda. Journal of Management, 51(1), 172-211.

Diefenhardt, F., Rapp, M. L., Bader, V., & Mayrhofer, W. (2025). ‘In God We Trust. All Others Must Bring Data’: Unpacking the Influence of Human Resource Analytics on the Strategic Recognition of Human Resource Management. Human Resource Management Journal, 35(3), 597–612.

Rapp, M. L., Hassan, N., Trullen, J., & Valverde, M. (2025). A bird’s-eye view of the relationships between economic complexity, time, and the importance of HRM actors. The International Journal of Human Resource Management, 36(12), 2149–2184.

Our research in this area focuses on data-driven talent decisions across the employee lifecycle, including diversity and equity in hiring, proactive behaviours at work, turnover and retention, and global mobility. We combine individual and organisational data to understand when and why employees join, grow, or move within and across organisations.

Selected publications:

Bader, B., Bucher, J., & Sarabi, A. (2024). Female expatriates on the move? Gender diversity management in global mobility. Human Resource Management Journal, 34(3), 753–780. 

Sarabi, A., & Lehmann, N. (2024). Who Shortlists? Evidence on Gender Disparities in Hiring Outcomes. Administrative Science Quarterly, 0(0). 

Yuan, S., Kroon, B., & Kramer, A. (2024). Building prediction models with grouped data: A case study on the prediction of turnover intention. Human Resource Management Journal, 34(1), 20-38.

Zhang, Z., Yao, X., Yuan, S., Deng, Y., & Guo, C. (2021). Big five personality influences trajectories of information seeking behavior. Personality and Individual Differences, 173, 110631.

With the advancement of technology and artificial intelligence (AI), we study how algorithmic management shapes employee experiences, including how individuals interpret algorithmic control signals and differ in their adoption of people analytics tools. We also investigate the broader opportunities and challenges brought by Generative AI for HRM, and how it can be used to enhance key HR processes in the future.

Selected publications:

Bunzel, C., Boon, C., den Hartog, D. N., & Verburg, R. (2025). Mixed signals? A receiver-centric perspective on the interpretation of algorithmic control signals. The International Journal of Human Resource Management, 36(11), 1896–1928.

Bentvelzen, M., Boon, C., & Den Hartog, D. N. (2024). A person-centered approach to individual people analytics adoption. Journal of Organizational Effectiveness: People and Performance, 12(5), 60-82.

Nyberg, A. J., Schleicher, D. J., Bell, B. S., Boon, C., Cappelli, P., Collings, D. G., Dalle Molle, J. E., Feuerriegel, S., Gerhart, B., Jeong, Y., Korsgaard, M. A., Minbaeva, D., Ployhart, R. E., Tambe, P., Weller, I., Wright, P. M., & Yakubovich, V. (2025). A Brave New World of Human Resources Research: Navigating Perils and Identifying Grand Challenges of the GenAI Revolution. Journal of Management, 51(6), 2677-2718.

Yuan, S., Xing, L. (Lucy), & Zhao, D. (2025). A multi-stage HR-in-the-loop approach to enhance fairness perceptions of AI selection systems. The International Journal of Human Resource Management, 36(14), 2623–2658.

Our work covers a wide range of data types and advanced analytical techniques that help organisations extract meaningful insights from complex datasets. We also explore how emerging tools such as large language models, explainable AI, and prompt engineering techniques can enhance data collection and analysis in people analytics projects.

Selected publications:

Boon, C., Durak, E., & Birbil, Ş. İ. (2025). Towards a better understanding of misfit through explainable AI techniques. In Billsberry, J., Talbot, D.L. (eds) Employee Misfit: Theories, Perspectives, and New Directions (pp. 223-245). Springer, Singapore.

Yuan, S., De Roover, K., Dufner, M., Denissen, J. J. A., & Van Deun, K. (2021). Revealing Subgroups That Differ in Common and Distinctive Variation in Multi-Block Data: Clusterwise Sparse Simultaneous Component Analysis. Social Science Computer Review, 39(5), 802-820. 

Yuan, S., De Roover, K., & Van Deun, K. (2023). Simultaneous clustering and variable selection: A novel algorithm and model selection procedure Behavior Research Methods, 55(5), 2157-2174.