TURNING DATA INTO VALUABLE INSIGHTS: THE CASE STUDY IN AVIATION SECTOR COMPANY
AbstractSince the early 2000s, there is increasing pressure on Human Resource Departments to show their impact on organizational performance. This pressure is related to the shift from industrial based economies to knowledge based economies and positioning people as potential sources of competitive advantage, and to the rise of Evidence-Based Management (EBM), which requires making decisions based on data and analysis. New technologies have enabled HR departments to start a transition from HR metrics to HR Analytics, thus transforming from the traditional administrative HR function to a more strategic HR function that can express qualitative matters and its impact on organizational performance with numbers. This case study describes the implementation of HR analytics in an aviation sector company. Quantitative data gathered from an annual staff engagement survey are analyzed using a Structural Equation Modelling technique with Smart PLS software. The results show that the analysis offers insights which are much more valuable than traditional diagnosis of the level of employee engagement. Thus, management can trace an employee’s journey within the organization and be able to predict their behavior in relationship to the time spent in the organization. Moreover, the changing needs of employees are seen form the analysis and Evidence-Based Management can be implemented.
Bassi, L. (2011). Raging debates in HR analytics. People and Strategy, 34(2), 14-18.
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, 44(2), 129-136.
Evans, W. R., & Davis, W. D. (2005). High-performance work systems and organizational performance: The mediating role of internal social structure. Journal of Management, 31(5), 758-775.
Falletta, S. (2014). In search of HR intelligence: evidence-based HR analytics practices in high performing companies. People and Strategy, 36(4), 28-37.
Fink, A. A. (2010). New trends in human capital research and analytics. People and Strategy, 33(2), 14-21.
Fitz-enz, J. (1984). How to measure human resource management. New York, NY: McGraw-Hill.
Fitz-enz, J. (2010). HR Analytics: Predicting the Economic Value of Your Company’s Human Capital Investments. New York, NY: AMACOM.
Fitz-Enz, J., & John Mattox, I. I. (2014). Predictive analytics for human resources. Hoboken, NJ: John Wiley & Sons.
Gardner, N., McGranahan, D., & Wolf, W. (2011). Question for your HR chief: Are we using our 'people data' to create value? McKinsey Quarterly, 2, 117-121.
Hair, J.F., Ringle, C.M., Sarsdet, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-151.
Huselid, M. (1995). The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38(3), 635-672.
Lawler III, E. E., Levenson, A. R., & Boudreau, J. W. (2004). HR metrics and analytics: Use and impact. People and Strategy, 27(4), 27-35.
Mishra, S. N., Lama, D.R., & Pal, Y. (2016). Human Resource Predictive Analytics (HRPA) for HR Management in Organizations. International Journal of Scientific & Technology Research, 5(5), 33-35.
Mondore, S., Douthitt, S., & Carson, M. (2011). Maximizing the impact and effectiveness of HR analytics to drive business outcomes. People and Strategy, 34(2), 20-27.
Paauwe, J., Guest, D., & Wright, P. (2012). HRM & Performance - Achievements & Challenges. Chichester: John Wiley & Sons Ltd.
Pease, G., Beresford, B., & Walker, L. (2014). Developing human capital: Using analytics to plan and optimize your learning and development investments. Hoboken, NJ: John Wiley & Sons.
Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242.
Russel, C., & Bennet, N. (2015). Big data and talent management: Using hard data to make the soft stuff easy. Business Horizons, 58(3), 237-242.
Rynes, S. L., Giluk, T. L., & Brown, K. G. (2007). The very separate worlds of academic practitioner periodicals in human resource management: Implications for Evidence-Based Management. The Academy of Management Journal, 50(5), 987-1008.
Suhr, D. (2006). The basics of structural equation modelling. Presented: Irvine, CA, SAS User Group of the Western Region of the United States (WUSS).
Van den Heuvel, S., & Bondarouk, T. (2016, February). The Rise (and Fall) of HR Analytics. Proceedings from 2nd HR Division International Conference (HRIC), Sidney, Australia.
Verschuren, D.E. & Doorewaard, H. (2010). Designing a research project (2nd edition). The Hague: Eleven International Publishing
Zainal, Z. (2007). Case study as a research method. Jurnal Kemanustaan, 9, Retrieved from http://psyking.net/htmlobj-3837/case_study_as_a_research_method.pdf
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