Journal of Human Resource Management

Journal of Human Resource Management

Evolution and Intellectual-conceptual Foundations of Research in the Field of Human Resources Analytics

Document Type : Original Article

Authors
1 Associate Prof., Department of Management, Institute for Humanities and Cultural Studies, Tehran, Iran.
2 . Ph.D., Department of Business Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran.
Abstract
Background & Purpose: Human Resources Analytics (HRA) was considered to replace decision-making based on intuition and experience with data-driven decisions, thereby connecting human resources functions with business outcomes through quantitative analysis. As this area is still in its infancy, it requires extensive examination from both theoretical and practical perspectives. To contribute to the literature on this topic, this study seeks to review the evolution of research and extract the intellectual-conceptual foundations of human resources analytics.
Methodology: In relation to the article's purpose, relevant research published between 1991 and 2022 was extracted from the Web of Science database and analyzed using VOS Viewer software. Descriptive, co-authorship, co-citation, and keyword co-occurrence analyses were performed on the collected data. Specifically, co-citation analysis was used to extract the intellectual-conceptual foundations of the subject.
Findings: Research findings revealed that in terms of quantity, the highest number of articles were published on this topic in 2017. The highest number of citations was recorded in 2022. Ulrich and Dave are the most cited researchers, Human Resource Management was the most cited journal, and the University of Southern California was the most cited university. Co-citation analysis, which expresses intellectual-conceptual foundations in the field, presented the literature on human resources analytics in the form of four clusters. The first cluster showed the "nature and necessity of human resources analytics", the second cluster focused on the discussion of "the link between human resources analytics and technology", the third cluster was formed around the topic of "human resources analytics and data science", and finally the fourth cluster was called “the dark strain of human resource analytics.
Conclusion: The topic of human resource analytics has a research history of about two decades in the world, although the process of published research in this field has been very fast. The structural dispersion of the research of the four clusters counted in this research shows that a so-called "bird's eye view" is required in human resources analytics. Furthermore, the most important weakness of research or even projects carried out in the field of human resources analytics is the limitation of work to people with human resources expertise or technical expertise, which deserves cooperation between these two types of expertise in the form of interdisciplinary collaboration.
Keywords

Arora, S. D. & Chakraborty, A. (2021). Intellectual structure of consumer complaining behavior (CCB) research: A bibliometric analysis. Journal of business research, 122, 60-74
 Avrahami, D., Pessach, D., Singer, G. & Chalutz Ben-Gal, H. (2022). A human resources analytics and machine-learning examination of turnover: implications for theory and practice. International Journal of Manpower, 43(6), 1405-1424.
Belizón, M. J. & Kieran, S. (2022). Human resources analytics: A legitimacy process. Human Resource Management Journal, 32(3), 603-630.
Berhil, S., Benlahmar, H. & Labani, N. (2020). A review paper on artificial intelligence at the service of human resources management. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 32- 40.
Chalutz Ben-Gal, H. (2019), An ROI-based review of HR analytics: practical implementation tools.Personnel Review, 48)6(, 1429-1448.
Chatterjee, S., Chaudhuri, R., Vrontis, D. & Siachou, E. (2021). Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach. International Journal of Manpower, 43(1), 52-74.
Credence (2019). Global HR analytics market to grow at A CAGR of 12.8% between 2019 and 2027 – credence research, available at: https://www.credenceresearch.com/press/global-hr-analyticsmarket.
Cuccurullo, C., Aria, M. & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics, 108, 595-611.
Delloite (2016). Global Human Capital Trends 2016, The new organization: Different by design, Deloitte Press, from: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/ HumanCapital/gx-dup-global-human-capital-trends-2016.pdf
Diez, F., Bussin, M., Lee, V. (2020). Fundamentals of HR Analytics A Manual on Becoming HR Analytical, Howard House: Emerald Publishing.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296.
Edwards, M. & Edwards, K. (2019). Predictive HR Analytics: Mastering the HR metric, (2th ed.). London: Kogan Page.
Fitz-enz, J. & John Mattox, II. (2014). Predictive analytics for human resources. New Jersey: John Wiley & Sons.
Garg, S., Sinha, S., Kar, A.K. & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71)5(,1590-1610.
Huang, L., Chen, K. & Zhou, M. (2020). Climate change and carbon sink: a bibliometric analysis. Environmental Science and Pollution Research, 27, 8740-8758.
Huda, A. & Ardi, N. (2021). Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification. International Journal of Interactive Mobile Technology, 15(8), 172-181.
Kakulapati, V., Chaitanya, K. K., Chaitanya, K. V. G. & Akshay, P. (2020). Predictive analytics of HR-A machine learning approach. Journal of Statistics and Management Systems, 23(6), 959-969.
Kholidah, H., Hijriah, H. Y., Mawardi, I., Huda, N., Herianingrum, S. & Alkausar, B. (2022). A Bibliometric mapping of peer-to-peer lending research based on economic and business perspective. Heliyon, 8(11).
Kumar, S., Sahoo, S., Lim, W. M., Kraus, S. & Bamel, U. (2022). Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview. Technological Forecasting and Social Change, 178, 121599.
Leonardi, P. & Contractor, N. (2018). Better people analytics. Harvard Business Review, 70–81.
León-Gómez, A., Ruiz-Palomo, D., Fernández-Gámez, M. A. & García-Revilla, M. R. (2021). Sustainable tourism development and economic growth: Bibliometric review and analysis. Sustainability, 13(4), 2270.
Madhani, P. M. (2023). Human Resources Analytics: Leveraging Human Resources for Enhancing Business Performance. Compensation & Benefits Review, 55(1), 31-45.
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.
Mukherjee, D., Lim, W. M., Kumar, S. & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research, 148, 101-115.
Niñerola, A., Sánchez-Rebull, M. V. & Hernández-Lara, A. B. (2019). Tourism research on sustainability: A bibliometric analysis. Sustainability, 11(5), 1377.
Qamar, Y. & Samad, T.A. (2022), Human resource analytics: a review and bibliometric analysis. Personnel Review, 51(1), 251-283.
Qureshi, T. (2020), HR analytics, fad or fashion for organizational sustainability, Sustainable Development and Social Responsibility–Volume 1: Proceedings of the 2nd American University in the Emirates International Research Conference, AUEIRC’18 – Dubai, UAE 2018, Springer Nature,103-107.
Rasmussen, T. & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad.Organizational Dynamics, 44(3), 236-242.
Rejeb, A., Rejeb, K., Abdollahi, A. & Treiblmaier, H. (2022). The big picture on Instagram research: Insights from a bibliometric analysis. Telematics and Informatics, 101876.
Rosett, C.M. & Hagerty, A. (2021). Introducing HR Analytics with Machine Learning: Empowering Practitioners, Psychologists, and Organization, Switzerland: Springer
Royal, C. & O'Donnell, L. (2008). Emerging human capital analytics for investment processes. Journal of Intellectual Capital, 9(3), 367-379.
Rusdiana, A. S., Sukmana, R. & Laila, N. (2021). Waqf on education: a bibliometric review based on Scopus. Library Philosophy and Practice (e-journal).
Tursunbayeva, A., Di Lauro, S. & Pagliari, C. (2018). People analytics-A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224-247.
Ulrich, D., Kryscynski, D., Ulrich, M. & Brockbank, W. (2017). Competencies for HR professionals who deliver outcomes. Employment Relations Today, 37-44.
Van Eck, N. and L. Waltman (2010). Software survey: VOS viewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
Viglia, G., Kumar, S., Pandey, N. & Joshi, Y. (2022). Forty years of The Service Industries Journal: a bibliometric review. The Service Industries Journal, 42(1-2), 1-20.
Železnik, D., Blažun Vošner, H. & Kokol, P. (2017). A bibliometric analysis of the Journal of Advanced Nursing, 1976–2015. Journal of advanced nursing, 73(10), 2407-2419.   
Zupic, I. & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18
Volume 13, Issue 3
Summer 2023
Pages 1-25

  • Receive Date 23 July 2023
  • Revise Date 11 September 2023
  • Accept Date 04 October 2023