Domain driven data mining in human resource management: A review of current research

• Published in: Expert systems with applications Vol. 40; no. 7; pp. 2410 - 2420
• Main Authors: Strohmeier, Stefan, Piazza, Franca
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.06.2013; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Documents; Domain driven data mining; Electronic human resource management (e-HRM); Exact sciences and technology; Expert systems; Firm modelling; HR data mining; HRM; Human resource management; Legal; Literature review; Memory organisation. Data processing; Operational research and scientific management; Operational research. Management science; Recognition; Software; Literature review; HRM; Data mining; Electronic human resource management (e-HRM); HR data mining; Domain driven data mining; Data analysis; Data processing; Information extraction; Data driven modelling; Human resource management; Standards
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.10.059

► We review data mining in human resource management as a new and prospering field of research using a domain driven framework. ► Findings show a large number of contributions of recent date and mostly technical or methodical provenance (n=100). ► Contributions cover a broad variety of HR domain problems and data mining methods. ► Contributions, however, regularly do not consider certain specific domain requirements. ► We elaborate on these requirements to support future work on the topic. An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new research field. The current paper reviews this research on HR data mining to systematically uncover recent advancements and suggest areas for future work. Based on the approach of domain driven data mining, an initial framework with significant domain-specific requirements is elaborated. Relevant research contributions are identified and reviewed against the background of this framework. The review reveals that HRM constitutes a noteworthy new domain of data mining research that is dominated by method- and technology-oriented work. However, specific domain requirements, such as evaluating the domain success or complying with legal standards, are frequently not recognized or considered in current research. Therefore, the systematic consideration of domain-specific requirements is demonstrated here to have significant implications for future research on data mining in HRM.