- Title
- How hr analytics can leverage big data to minimise employees' exploitation and promote their welfare for sustainable competitive advantage
- Creator
- Biswas, Kumar; Bhardwaj, Sneh; Zaman, Sawlat
- Date
- 2023
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/193587
- Identifier
- vital:18193
- Identifier
- ISBN:9781800888555 (ISBN); 9781800888548 (ISBN)
- Abstract
- Big Data leveraged Human Resource Management Analytics (HRA) can enable HR professionals to make objective decisions in effectively managing key HR functions such as recruitment, training, development, compensation. Despite the abundance of benefits of using HRA to manage people in the organization more objectively than ever before, scholars and practitioners have been raising concerns over the potential misuse of HRA to discriminate against a particular group of people who may not be aware of disparate HR practices. As part of this bigger picture, this chapter investigates how HRA can leverage big data to minimize employee exploitation and promote employee welfare to sustain competitive advantage. This chapter provides comprehensive articulation of the key concepts related to HR analytics, Big Data and delineates how big-data-driven HR analytics can be (mis)used for people management. Our chapter draws on critical challenges HR professionals experience in adopting big data leveraged HR analytics. Finally, this chapter concludes with a set of proactive and reactive measures that are to be adhered to minimize HRA-related biases to uphold the philosophy of equity and diversity and sustain the organization's branding as an employer of choice. © Editors and Contributors Severally 2023. All rights reserved.
- Publisher
- Edward Elgar Publishing Ltd.
- Relation
- Handbook of Big Data Research Methods Chapter 12 p. 179-194
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ Editors and Contributors Severally 2023
- Subject
- Big data-driven HR; HR analytics; HR analytics biases
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