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Information exploitation of human resource data with persistent homology

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  • Chong, Woon Kian
  • Chang, Chiachi

Abstract

In an era of big data, corporations have access to an abundance of employee details. While few inferences about employee performance can be made from these data, discarding them may be potentially detrimental to a business. Likewise, employee applications contain substantial amounts of information that cannot necessarily be used to indicate the potential performance of employees should they be appointed. “Persistent homology” considers the topography of data, identifying clusters of behavior that may be associated with performance levels, as well as “holes” in the data cloud that may be filled with suitable job applicants. Therefore, this study presents a theoretical application of persistent homology to human resource management, which considers the topography of data to identify clusters of behavior associated with performance levels and fill gaps with suitable job applicants. Our study demonstrates the potential of persistent homology that offers a breakthrough contribution to the wider research agenda.

Suggested Citation

  • Chong, Woon Kian & Chang, Chiachi, 2024. "Information exploitation of human resource data with persistent homology," Journal of Business Research, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:jbrese:v:172:y:2024:i:c:s0148296323007695
    DOI: 10.1016/j.jbusres.2023.114410
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    References listed on IDEAS

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    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. L.R. Maglen, 1990. "Challenging the Human Capital Orthodoxy: The Education‐Productivity Link Re‐examined," The Economic Record, The Economic Society of Australia, vol. 66(4), pages 281-294, December.
    4. Zhang, Xiaomeng & Zhou, Jing, 2014. "Empowering leadership, uncertainty avoidance, trust, and employee creativity: Interaction effects and a mediating mechanism," Organizational Behavior and Human Decision Processes, Elsevier, vol. 124(2), pages 150-164.
    5. Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.
    6. Lu, Chia-Mei & Chen, Shyh-Jer & Huang, Pei-Chi & Chien, Jui-Ching, 2015. "Effect of diversity on human resource management and organizational performance," Journal of Business Research, Elsevier, vol. 68(4), pages 857-861.
    7. Y Dabaghian & F Mémoli & L Frank & G Carlsson, 2012. "A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-14, August.
    8. Sohrabi, Babak & Khalilijafarabad, Ahmad, 2018. "Systematic method for finding emergence research areas as data quality," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 280-287.
    9. Kim, Jaemin & Dibrell, Clay & Kraft, Ellen & Marshall, David, 2021. "Data analytics and performance: The moderating role of intuition-based HR management in major league baseball," Journal of Business Research, Elsevier, vol. 122(C), pages 204-216.
    10. Caputo, Francesco & Mazzoleni, Alberto & Pellicelli, Anna Claudia & Muller, Jens, 2020. "Over the mask of innovation management in the world of Big Data," Journal of Business Research, Elsevier, vol. 119(C), pages 330-338.
    11. El-Kassar, Abdul-Nasser & Singh, Sanjay Kumar, 2019. "Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 483-498.
    12. Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    13. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    14. C. J. Carstens & K. J. Horadam, 2013. "Persistent Homology of Collaboration Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, June.
    15. Maglen, L R, 1990. "Challenging the Human Capital Orthodoxy: The Education-Productivity Link Re-examined," The Economic Record, The Economic Society of Australia, vol. 66(195), pages 281-294, December.
    16. Braganza, Ashley & Brooks, Laurence & Nepelski, Daniel & Ali, Maged & Moro, Russ, 2017. "Resource management in big data initiatives: Processes and dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 328-337.
    17. Escolar, Emerson G. & Hiraoka, Yasuaki & Igami, Mitsuru & Ozcan, Yasin, 2023. "Mapping firms’ locations in technological space: A topological analysis of patent statistics," Research Policy, Elsevier, vol. 52(8).
    Full references (including those not matched with items on IDEAS)

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