The field of Human Resource Management (HRM) has experienced a significant transformation with the emergence of big data and algorithms. Major technology companies have introduced software and platforms for analyzing various HRM practices, such as hiring, compensation, employee engagement, and turnover management, utilizing algorithmic approaches. However, scholarly research has taken a cautious stance, questioning the strategic value and causal inference basis of these tools, while also raising concerns about bias, discrimination, and ethical issues in the applications of algorithms. Despite these concerns, algorithmic management has gained prominence in large organizations, shaping workforce management practices. This thesis aims to address the gap between the rapidly changing market of HRM algorithms and the lack of theoretical understanding.
The thesis begins by conducting a comprehensive review of HRM algorithms in HRM practice and scholarship, clarifying their definition, exploring their unique features, and identifying specific topics and research questions in the field. It aims to bridge the gap between academia and practice to enhance the understanding and utilization of algorithms in HRM. I then explore the legal, causal, and moral issues associated with HR algorithms, comparing fairness criteria and advocating for the use of causal modeling to evaluate algorithmic fairness. The multifaceted nature of fairness is illustrated and practical strategies for enhancing justice perceptions and incorporating fairness into HR algorithms are proposed. Finally, the thesis adopts an artifact-centric approach to examine the ethical implications of HRM algorithms. It explores competing views on moral responsibility, introduces the concept of "ethical affordances," and analyzes the distribution of moral responsibility based on different types of ethical affordances. The paper provides a framework for analyzing and assigning moral responsibility to stakeholders involved in the design, use, and regulation of HRM algorithms.
Together, these papers contribute to the understanding of algorithms in HRM by addressing the research-practice gap, exploring fairness and accountability issues, and investigating the ethical implications. They offer theoretical insights, practical recommendations, and future research directions for both researchers and practitioners. / Thesis / Doctor of Philosophy (PhD) / This thesis explores the use of advanced algorithms in Human Resource Management (HRM) and how they affect decision-making in organizations. With the rise of big data and powerful algorithms, companies can analyze various HR practices like hiring, compensation, and employee engagement. However, there are concerns about biases and ethical issues in algorithmic decision-making. This research examines the benefits and challenges of HRM algorithms and suggests ways to ensure fairness and ethical considerations in their design and application. By bridging the gap between theory and practice, this thesis provides insights into the responsible use of algorithms in HRM. The findings of this research can help organizations make better decisions while maintaining fairness and upholding ethical standards in HR practices.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29708 |
| Date | January 2024 |
| Creators | Cheng, Minghui |
| Contributors | Hackett, Rick, Business |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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