Publication
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions
| Summary: | Artificial Intelligence (AI) gained centrality in society and organizations, with an ongoing unparalleled potential for change in work settings and in how workers relate to employers. Human Resource Management (HRM) is not an exception as there are many proposals, and implementations, of AI-based apps that replace or aid HRM processes. Still, change does not come without doubts and without general endorsement, change is doomed to failure or at the minimum, to suboptimal effectiveness. This thesis is designed to test to which extent individuals endorse automated Human Resource Management (a-HRM) based on normative dimensions, namely accountability, fairness, legitimacy, explainability, and reversibility. Based on a sample of 253 employees, findings using PLS-SEM models showed that legitimacy is the key variable explaining HRM functional domains AI endorsement, which overall are contributive to general a-HRM endorsement. Findings are discussed in light of theory and of the conclusions inferred towards its future albeit overall findings suggest constructs are not yet clear enough to allow for inferences made on solid ground. |
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| Main Authors: | Coutinho, Maria do Carmo Pinto Leite Pereira |
| Subject: | Inteligência artificial -- Artificial intelligence Human resource management Endorsement Normative dimensions Gestão de recursos humanos Aceitação Dimensões normativas |
| Year: | 2023 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | ISCTE |
| Language: | English |
| Origin: | Repositório ISCTE |
| Summary: | Artificial Intelligence (AI) gained centrality in society and organizations, with an ongoing unparalleled potential for change in work settings and in how workers relate to employers. Human Resource Management (HRM) is not an exception as there are many proposals, and implementations, of AI-based apps that replace or aid HRM processes. Still, change does not come without doubts and without general endorsement, change is doomed to failure or at the minimum, to suboptimal effectiveness. This thesis is designed to test to which extent individuals endorse automated Human Resource Management (a-HRM) based on normative dimensions, namely accountability, fairness, legitimacy, explainability, and reversibility. Based on a sample of 253 employees, findings using PLS-SEM models showed that legitimacy is the key variable explaining HRM functional domains AI endorsement, which overall are contributive to general a-HRM endorsement. Findings are discussed in light of theory and of the conclusions inferred towards its future albeit overall findings suggest constructs are not yet clear enough to allow for inferences made on solid ground. |
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