Information exploitation of human resource data with persistent homology

• Published in: Journal of business research 2024-02, Vol.172, p.114410, Article 114410
• Main Authors: Chong, Woon Kian, Chang, Chiachi, Rudkin, Simon
• Format: Article
• Language: English
• Subjects: Performance evaluation; Persistent homology; Recruitment; Training
• ISSN: 0148-2963; 1873-7978
• DOI: 10.1016/j.jbusres.2023.114410

•Vast Human Resource Management (HRM) data sets is beckoning fresh perspectives.•Persistent homology(PH) exploits the potential of topography of data to HRM.•PH unearths true patterns free from preconceptions of relationships.•The searching method for clusters is easily transferrable to HRM practices.•Offers new direction for future research to further theoretical development. 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.