Skip to main content

Human Resource Analytics in South Korea: Transforming the Organization and Industry

  • Chapter
  • First Online:
Human Resource Development in South Korea

Abstract

Many large and global companies in South Korea have started adopting Human Resource (HR) analytics in response to growing demands of making evidence-based decisions for the organization’s people-related issues. Organizational leaders as well as HR professionals see HR analytics as an emerging field that is growing in importance. However, frameworks to follow or empirical evidence for adopting HR analytics are scarce yet in the overall landscape of the HR analytics field. Based on the authors’ multiple HR analytics projects experiences from several different companies, this chapter captured the scope, benefits, challenges, lessons learned, and needed support issues. We also emphasized how successful execution and expansion of HR analytics require careful planning on the part of the analytics team for collaboration and buy-in support from business units, and how leaders will need to address the policy, governance, and culture with regard to using and sharing data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ardichvili, A., & Yoon, S. W. (2009). Designing integrative knowledge management systems: Theoretical considerations and practical applications. Advances in Developing Human Resources, 11(3), 307–319.

    Article  Google Scholar 

  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.

    Article  Google Scholar 

  • Bock, L. (2015). Work rules: Insights from inside Google that will transform how you live and lead. New York, NY: Twelve.

    Google Scholar 

  • Bodie, M. T., Cherry, M. A., McCormick, M. L., & Tang, J. (2017). The law and policy of people analytics. University of Colorado Law Review, 88, 961–1042.

    Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Thousand Oaks: Sage.

    Google Scholar 

  • Briner, R. B., Denyer, D., & Rousseau, D. M. (2009). Evidence-based management: Concept cleanup time? Academy of Management Perspectives, 23(4), 19–32.

    Article  Google Scholar 

  • Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.

    Google Scholar 

  • D’Onfro, J. (2014, November 20). Google wrote an equation for deciding which engineers should get promoted—Here’s why it failed. Retrieved from https://www.businessinsider.com/google-promotion-equation-2014-11.

  • Davenport, T. H. (1997). Ten principles of knowledge management and four case studies. Knowledge and Process Management, 4(3), 187–208.

    Google Scholar 

  • Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88(10), 52–58.

    Google Scholar 

  • Dearborn, J., & Swanson, D. (2017). The data driven leader: A powerful approach to delivering measurable business impact through people analytics. Hoboken: Wiley.

    Google Scholar 

  • Ducey, A. J., Guenole, N., Weiner, S. P., Herleman, H. A., Gibby, R. E., & Delany, T. (2015). I-Os in the vanguard of big data analytics and privacy. Industrial and Organizational Psychology, 8(4), 555–563.

    Article  Google Scholar 

  • English, D. E., Manton, E. J., & Walker, J. (2007). Human resource managers’ perception of selected communication competencies. Education, 127(3), 410–418.

    Google Scholar 

  • Glass, A., & Saggi, K. (1996). International technology transfer and the technology gap. Columbus, OH: Department of Economics, Ohio State University.

    Google Scholar 

  • Hambrick, D. C., Finkelstein, S., & Mooney, A. C. (2005). Executive job demands: New insights for explaining strategic decisions and leader behaviors. Academy of Management Review, 30(3), 472–491.

    Article  Google Scholar 

  • Harris, M. M., Van Hoye, G., & Lievens, F. (2003). Privacy and attitudes towards internet-based selection systems: A cross-cultural comparison. International Journal of Selection and Assessment, 11, 230–236.

    Article  Google Scholar 

  • Joiner, D. A. (2000). Guidelines and ethical considerations for assessment center operations: International task force on assessment center guidelines. Public Personnel Management, 29(3), 315–332.

    Google Scholar 

  • Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford: OUP USA.

    Google Scholar 

  • King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487–495.

    Article  Google Scholar 

  • Kwartler, T. (2017). Text mining in practice with R. Hoboken: Wiley.

    Google Scholar 

  • Levenson, A. R., Van der Stede, W. A., & Cohen, S. G. (2006). Measuring the relationship between managerial competencies and performance. Journal of Management, 32(3), 360–380.

    Google Scholar 

  • Lundblad, J. P. (2003). A review and critique of Rogers’ diffusion of innovation theory as it applies to organizations. Organization Development Journal, 21(4), 50–64.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Madariaga, R., Oller, R., & Martori, J. C. (2018). Discrete choice and survival models in employee turnover analysis. Employee Relations, 40(2), 381–395.

    Google Scholar 

  • Marsden, P. V., & Friedkin, N. E. (1993). Network studies of social influence. Sociological Methods & Research, 22(1), 127–151.

    Article  Google Scholar 

  • McLagan, P. A., & Bedrick, D. (1983). Models for excellence: The results of the ASTD training and development competency study. Training and Development Journal, 37(6), 10–20.

    Google Scholar 

  • Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Mishra, K. E., Spreitzer, G. M., & Mishra, A. K. (1998). Preserving employee morale during downsizing. MIT Sloan Management Review, 39(2), 83–95.

    Google Scholar 

  • Pfeffer, J., & Sutton, R. I. (2006). Evidence-based management. Harvard Business Review, 84(1), 62–74.

    Google Scholar 

  • Pilny, A., & Poole, M. S. (Eds.). (2017). Group processes: Data-driven computational approaches. Cham: Springer.

    Google Scholar 

  • Ployhart, R. E., Weekley, J. A., & Ramsey, J. (2009). The consequences of human resource stocks and flows: A longitudinal examination of unit service orientation and unit effectiveness. Academy of Management Journal, 52(5), 996–1015.

    Google Scholar 

  • Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol: “O’Reilly Media, Inc.”.

    Google Scholar 

  • Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236–242.

    Article  Google Scholar 

  • Ratanjee, V. (2019, December 9). How HR can optimize people analytics. Retrieved from https://www.gallup.com/workplace/259958/optimize-people-analytics.aspx.

  • Robins, G., Elliott, P., & Pattison, P. (2001). Network models for social selection processes. Social Networks, 23(1), 1–30.

    Article  Google Scholar 

  • Rothwell, W. J. (2011). Replacement planning: A starting point for succession planning and talent management. International Journal of Training and Development, 15(1), 87–99.

    Article  Google Scholar 

  • Rousseau, D. M. (2006). Is there such a thing as “evidence-based management”? Academy of Management Review, 31(2), 256–269.

    Article  Google Scholar 

  • Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674.

    Article  Google Scholar 

  • Schmiedel, T., MĂĽller, O., & vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941–968.

    Article  Google Scholar 

  • Sharma, A., & Srivastava, J. (2017). Group analysis using machine learning techniques. In A. Pilny & M. S. Poole (Eds.), Group processes: Data-driven computational approaches (pp. 145–180). Cham: Springer.

    Chapter  Google Scholar 

  • Thornton III, G. C., & Rupp, D. E. (2004). Simulations and assessment centers. In J. C. Thomas (Ed.), Comprehensive handbook of psychological assessment. Industrial and organizational assessment (Vol. 4, pp. 319–344). John Wiley & Sons, Inc.

    Google Scholar 

  • Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258.

    Article  Google Scholar 

  • Urdan, T. C. (2011). Statistics in plain english (3rd ed). New York, NY: Routledge.

    Google Scholar 

  • Veale, D. J., & Wachtel, J. M. (1996). Mentoring and coaching as part of a human resource development strategy: An example at Coca-Cola Foods. Leadership & Organization Development Journal, 17(3), 16–20.

    Article  Google Scholar 

  • VĂ©voda, J., VĂ©vodová, Ĺ ., BubenĂ­ková, Ĺ ., Kisvetrová, H., & Ivanová, K. (2016). Datamining techniques—Decision tree: New view on nurses’ intention to leave. Central European Journal of Nursing and Midwifery, 7(4), 518–526.

    Article  Google Scholar 

  • Waddill, D. D., & Marquardt, M. J. (2011). The e-HR advantage: The complete handbook for technology-enabled human resources. Boston: Nicholas Brealey.

    Google Scholar 

  • Wheelan, C. (2013). Naked statistics: Stripping the dread from the data. WW Norton & Company.

    Google Scholar 

  • Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. Sebastopol: O’Reilly Media, Inc.

    Google Scholar 

  • Wimer, S., & Nowack, K. M. (1998). 13 common mistakes using 360-degree feedback. Training and Development, 52, 69–82.

    Google Scholar 

  • Yoon, S. W. (2018). Innovative data analytic methods in human resource development: Recommendations for research design. Human design. Human Resource Development Quarterly, 29(4), 299–306.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yoon, S.W., Chae, C., Kim, S., Lee, J., Jo, Y. (2020). Human Resource Analytics in South Korea: Transforming the Organization and Industry. In: Lim, D.H., Yoon, S.W., Cho, D. (eds) Human Resource Development in South Korea. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-54066-1_9

Download citation

Publish with us

Policies and ethics