Developing a Human Resource Analytics (HRA) competency framework for enhancing Return on Investment (ROI): An empirical investigation
Keywords:
HRA competency, capability, motivation, opportunity, Return on investmentAbstract
The study of the relationship between existing HRA competency and Return on investment (ROI) is a relevant theme. Though the role of employee-related elements of HRA (such as capabilities, motivation and opportunity; CMO) in influencing financial outcomes is relevant, there is no empirical evidence analysing the influence of these variables in the HRA competency-ROI relationship. Using a sample of HR professionals (n = 230) in private organizations in Bangalore, India, this paper tested the hypothesized model using SEM. The present paper examined the mediating effects of capability, motivation and opportunity on the relationship between the existing HRA competency and ROI. Likewise, this study tested the differential effect of capability, motivation and opportunity on ROI. The findings of the study identified a positive and significant relationship between existing HRA competency, employee motivation and ROI. Besides, ‘opportunity’ was identified as a significant mediator of the link between existing HRA competency and ROI. Concerning the differential effect of the individual employee-related variables, the present study revealed that ‘opportunity’ was more strongly related to ROI than ‘motivation’. As one of the first, this paper presents a framework that explains how HRA competency influences ROI through CMO.
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References
Acito, F., & Khatri, V. (2014). Business analytics: Why now and what next? Business Horizons, 57(5), 565-570.
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.
Appelbaum, E., Bailey, T., Berg, P., Kalleberg, A. L., & Bailey, T. A. (2000). Manufacturing advantage: Why high-performance work systems pay off. Cornell University Press, Ithaca, NY.
Aral, S., Brynjolfsson, E., & Wu, L. (2010). Three-Way Complementarities: Performance Pay, HR Analytics and Information Technology. Management Science, 58(5), 913-931.
Aryee, S., Walumbwa, F. O., Seidu, E. Y., & Otaye, L. E. (2012). Impact of high- performance work systems on individual-and branch-level performance: test of a multilevel model of intermediate linkages. Journal of applied psychology, 97(2), 287.
Beltrán-Martín, I., & Bou-Llusar, J. C. (2018). Examining the intermediate role of employee abilities, motivation and opportunities to participate in the relationship between HR bundles and employee performance. BRQ Business Research Quarterly, 21(2), 99-110.
Ben-Gal, H. C. (2019). An ROI-based review of HR analytics: practical implementation tools. Personnel Review. DOI 10.1108/PR-11-2017-0362
Blumberg, M., & Pringle, C. D. (1982). The missing opportunity in organizational research: Some implications for a theory of work performance. Academy of management Review, 7(4), 560-569.
Boston Consulting Group. (2014). Creating People Advantage 2014-2015. The Boston Consulting Group, Inc., Boston, MA.
Boudreau, J. W., & Ramstad, P. M. (2005). Talentship, talent segmentation, and sustainability: A new HR decision science paradigm for a new strategy definition. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in alliance with the Society of Human Resources Management, 44(2), 129-136.
Boudreau, J. W., & Ramstad, P. M. (2006). Talentship and human resource measurement and analysis: From ROI to strategic organizational change. University of Southern California, Los Angeles, CA.
Boudreau, J., & Cascio, W. (2017). Human capital analytics: why are we not there?. Journal of Organizational Effectiveness: People and Performance, 4(2), 119- 126.
Bukhari, H., Andreatta, P., Goldiez, B., & Rabelo, L. (2017). A framework for determining the return on investment of simulation-based training in health care. Inquiry: The Journal of Health Care Organization, Provision, and Financing, 54, 0046958016687176.
Cabello-Medina, C., López-Cabrales, Á., & Valle-Cabrera, R. (2011). Leveraging the innovative performance of human capital through HRM and social capital in Spanish firms. The International Journal of Human Resource Management, 22(04), 807-828.
Chong, D., & Shi, H. (2015). Big data analytics: a literature review. Journal of Management Analytics, 2(3), 175-201.
Chuang, C. H., & Liao, H. U. I. (2010). Strategic human resource management in service context: Taking care of business by taking care of employees and customers. Personnel psychology, 63(1), 153-196.
CIPD. (2013). Talent Analytics and Big Data – The Challenge for HR. Chartered Institute for Personnel and Development, London.
Deloitte (2017). 2017 Deloitte Global Human Capital Trends report: Rewriting the rules for the digital age.
Edwards, M.R., & Edwards, K. (2019). Predictive HR Analytics: Mastering the HR Metric. 2nd ed., Kogan Page Ltd, London.
Ehrnrooth, M., & Björkman, I. (2012). An integrative HRM process theorization: Beyond signalling effects and mutual gains. Journal of Management Studies, 49(6), 1109-1135.
Falletta, S. V., & Combs, W. L. (2020). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work- Applied Management.
Fecheyr-Lippens, B., Schaninger, B., & Tanner, K. (2015). Power to the new people analytics. McKinsey Quarterly, 51(1), 61-63.
Fink, A. A. (2010). New trends in human capital research and analytics. People and Strategy, 33(2), 14.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Human Resource Management, 35(2), 137- 144.
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial-organizational psychology. Industrial and Organizational Psychology, 8(4), 491-508.
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141.
Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue. Human Resource Management, 57(3), 679-684.
Jiang, K., Lepak, D. P., Hu, J., & Baer, J. C. (2012). How does human resource management influence organizational outcomes? A meta-analytic investigation of mediating mechanisms. Academy of management Journal, 55(6), 1264-1294.
Jiang, K., Takeuchi, R., Lepak, D.P. (2013). Where do we go from here? New perspectives on the black box in strategic human resource management research. Journal of Management Studies, 50, 1448-1480.
Kaufman, B. E. (2014). The historical development of American HRM broadly viewed. Human Resource Management Review, 24(3), 196-218.
Kim, S. (2004). Individual-level factors and organizational performance in government organizations. Journal of Public Administration Research and Theory, 15(2), 245-261.
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford publications, New York.
Knies, E., & Leisink, P. (2014). Linking people management and extra‐ role behaviour: results of a longitudinal study. Human Resource Management Journal, 24(1), 57-76.
Kryscynski, D., Reeves, C., Stice‐ Lusvardi, R., Ulrich, M., & Russell, G. (2018). Analytical abilities and the performance of HR professionals. Human Resource Management, 57(3), 715-738.
Levenson, A. (2011). Using targeted analytics to improve talent decisions. People and Strategy, 34(2), 34.
Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685-700.
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.
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.
Minbaeva, D. B. (2017). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713.
Naula, S. (2015). HR analytics: its use, techniques and impact. International Journal of Research in Commerce & Management, (8), 47-52.
Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2), 687-698.
Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: developing a framework. Journal of Organizational Effectiveness: People and Performance, 7(2), 203-219.
Philips, J.J. (2012). Return On Investment in Training and Performance Improvement Programs. Routledge.
Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242.
Rauf, A., Gulzar, S., & Baig, J. (2016). Measuring the effectiveness of HR metrics on return on investment-an empirical study on Pakistani organizations.
Strohmeier, S. (2015). Analysen der Human Resource Intelligence und Analytics. In Human Resource Intelligence und Analytics (pp. 3-47). Springer Gabler, Wiesbaden.
van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness: People and Performance, 4(2), 127-132.
Wiratchai, N. (1999). LISREL model: Statistics for research (3rd d ed.). Chulalongkorn University Press, Bangkok, Thailand.
Xiu, L., Liang, X., Chen, Z., & Xu, W. (2017). Strategic flexibility, innovative HR practices, and firm performance. Personnel Review.
Zeidan, S., & Itani, N. (2020). HR Analytics and Organizational Effectiveness. International Journal on Emerging Technologies, 11(2), 683-688.
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