Human Resources Analytics: A Systematization of Research Topics and Research Agenda
DOI:
https://doi.org/10.3126/irjmmc.v7i1.93193Keywords:
human resource analytics, people analytics, strategic human resource management, data-driven decision making, workforce analytics, organizational performanceAbstract
The growing availability of workforce-related data and advances in analytical technologies have significantly transformed the field of Human Resource Management (HRM), giving rise to Human Resources Analytics (HR Analytics) as a strategic domain. HR Analytics refers to the systematic use of data, statistical analysis, and predictive models to improve human resource decision-making and organizational performance. Despite increasing academic and practitioner interest, the HR analytics literature remains fragmented, with diverse definitions, methodologies, and application areas. This study aims to systematize existing research on HR analytics by identifying key themes, methodological approaches, and emerging research gaps, while proposing a comprehensive future research agenda. Using a structured review of peer-reviewed journal articles indexed in Scopus, this study categorizes HR analytics research into core thematic areas, including talent acquisition, employee performance management, retention and turnover, learning and development, workforce planning, and employee well-being. The review highlights that early research predominantly focused on descriptive and diagnostic analytics, while recent studies increasingly emphasize predictive and prescriptive analytics to support strategic HR decisions. Advanced analytical techniques such as machine learning, artificial intelligence, and big data analytics are gaining prominence, enabling organizations to forecast employee behaviour and optimize human capital investments. Methodologically, the review reveals a dominance of quantitative approaches, particularly regression analysis, structural equation modelling, and data mining techniques, often using large organizational datasets. However, qualitative and mixed-methods studies remain limited, suggesting a need for deeper contextual understanding of how HR analytics is implemented and interpreted by decision-makers. Furthermore, most studies adopt a firm-level perspective, with limited attention to ethical concerns, data privacy, employee perceptions, and the potential unintended consequences of algorithm-driven HR decisions. The findings also indicate that while HR analytics is frequently linked to improved organizational performance and competitive advantage, empirical evidence remains inconsistent across industries and regions. This inconsistency underscores the importance of contextual factors such as organizational culture, analytical capability, leadership support, and HR professionals’ data literacy. The review identifies a significant gap between academic research and practical implementation, as many organizations struggle to translate analytical insights into actionable HR strategies. Based on the systematization of existing literature, this study proposes a future research agenda that emphasizes longitudinal research designs, cross-cultural studies, ethical and legal considerations, and the integration of employee-centric perspectives. Additionally, future studies should explore the role of HR analytics in promoting sustainable HRM, diversity and inclusion, and employee well-being. By providing a structured overview of the HR analytics research landscape, this study contributes to theory development and offers practical guidance for scholars and practitioners seeking to leverage analytics for strategic human resource management.
Downloads
References
1. Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090
2. Belizon, M. J., & Kieran, S. (2022). Human resources analytics: A legitimacy process. Human Resource Management Journal, 32(3), 603–630. https://doi.org/10.1111/1748-8583.12417
3. Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review (October).
4. Fernández, V., & Gallardo-Gallardo, E. (2021). Tackling the HR digitalization challenge: Key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal, 31(1), 162–187. https://doi.org/10.1108/CR-12-2019-0163
5. Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. https://doi.org/10.1080/0960085X.2021.1927213
6. Hugosson, M., & Espegren, Y. (2023). HR analytics-as-practice: A systematic literature review. Journal of Organizational Effectiveness: People and Performance, 12(5), 83–111. https://doi.org/10.1108/JOEPP-11-2022-0345
7. Khan, S. A., & Tang, J. (2016). The paradox of human resource analytics: Being mindful of employees. Journal of General Management, 42(2), 57–66.
8. King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487–495. https://doi.org/10.1177/1534484316675818
9. Kryscynski, D., Coff, R., & Jones, C. (2018). Something like “Analytical abilities” in HR (workforce analytics) and strategic value. Human Resource Management, 57(3), 715–738.
10. Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160, 377–392. https://doi.org/10.1007/s10551-019-04204-w
11. Leonardi, P., & Contractor, N. S. (2018). Better people analytics: Measure who they know, not just who they are. Harvard Business Review (November–December).
12. Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685–700. https://doi.org/10.1002/hrm.21850
13. Levenson, A., & Fink, A. (2017). Human capital analytics: Too much data and analysis, not enough models and business insights. Journal of Organizational Effectiveness: People and Performance, 4(2), 145–156. https://doi.org/10.1108/JOEPP-03-2017-0029
14. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
15. Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), Article 100795. https://doi.org/10.1016/j.hrmr.2020.100795
16. McCartney, S., & Fu, N. (2022). Bridging the gap: Why, how and when HR analytics can impact organizational performance. Management Decision, 60(13), 25–47. https://doi.org/10.1108/MD-12-2020-1581
17. Minbaeva, D. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701–713. https://doi.org/10.1002/hrm.21848
18. Qamar, Y., & Samad, T. A. (2022). Human resource analytics: A review and bibliometric analysis. Personnel Review, 51(1), 251–283. https://doi.org/10.1108/PR-04-2020-0247
19. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242. https://doi.org/10.1016/j.orgdyn.2015.05.008
20. Tursunbayeva, A., Pagliari, C., Di Lauro, S., & Antonelli, G. (2018). The ethics of people analytics: Risks, opportunities and recommendations (people analytics overview stream). International Journal of Information Management, 43, 224–247. https://doi.org/10.10
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Research Journal of MMC (IRJMMC)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
