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Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future

In: Machine Learning for Data Science Handbook

Author

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  • Hila Chalutz-Ben Gal

    (Afeka Tel Aviv Academic College of Engineering, School of Industrial Engineering and Management)

Abstract

The purpose of this chapter is to provide a return on investment (ROI)-based review of human resources-based organizational data mining (HRODM). Organizational data mining (ODM) is defined as leveraging data mining (DM) tools and technologies to enhance organizational decision-making process by transforming data into valuable and actionable knowledge in order to gain a strategic competitive advantage (Nemati, Barko, J Comput Inf Syst 42(4):21–28, 2002; Ind Manag Data Syst 103(4):282–292, 2003). The objectives of this chapter are twofold: First, to offer an integrative analysis of the literature on the topic of HRODM to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic. Second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HRODM by highlighting specific directions as to where the expected ROI may be found. This chapter includes a four-step review and analysis methodology. The chapter provides theoretical and practical information for scholars and professionals aiming to study and adopt HRODM. The ROI-based approach to HRODM presented in this chapter provides a robust tool to compare and contrast different dilemmas and associated values that can be derived from conducting the various types of HRODM projects. A framework is presented that aggregates the findings and clarifies how various HRODM tools influence ROI and how these relationships can be explained. Two examples are presented to demonstrate HRODM implementation.

Suggested Citation

  • Hila Chalutz-Ben Gal, 2023. "Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 833-866, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_36
    DOI: 10.1007/978-3-031-24628-9_36
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