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The rapid advancement of artificial-intelligence (AI) technology and its increasing prevalence in multiple sectors mark a significant milestone in the digital revolution (West & Allen, 2018; Wu et al., 2024). With its sophisticated capabilities in cognition, learning, and pattern recognition, AI has begun to instigate transformative shifts in traditional operational paradigms, thereby redefining the modus operandi of diverse sectors (Bozdag, 2023; Xu et al., 2024). The realm of human-resource management (HRM) has not remained unaffected by this technological disruption (Azizi et al., 2021). In fact, the intersection of AI and HRM has emerged as a prominent area of interest in terms of both practical application and academic inquiry (Cayrat & Boxall, 2022). The unique attributes of AI, such as predictive analytics, machine learning, and natural language processing, offer unprecedented opportunities to enhance and optimize HRM practices. These AI-driven innovations hold the potential to revolutionize numerous facets of HRM such as workforce planning, learning and development, performance management, and talent acquisition, among others (Chowdhury et al., 2023; Liu et al., 2024). In the realm of workforce planning, for instance, AI can provide sophisticated predictive models to anticipate talent needs and aid in proactive planning. Similarly, within learning and development, AI-powered platforms can deliver personalized, adaptive learning experiences, thereby boosting the efficacy of training programs (Maghsudi et al., 2021). In performance management and talent acquisition, too, AI is poised to introduce improved accuracy and efficiency, thereby transforming conventional approaches (Johnson et al., 2021).
The existing literature, while rich in discussions on the technical dimensions and pragmatic applications of AI (Nurkin, 2023; Raska & Bitzinger, 2023; Yao et al., 2015), often overlooks the significant role of AI in data-driven decision-making, a recognized determinant of organizational success (Mikalef et al., 2019; Provost & Fawcett, 2013; Rialti et al., 2019). Current studies offer insight into AI's potential in refining decision-making processes (Akter et al., 2022; Babu et al., 2021; Cybulski & Scheepers, 2021) but primarily concentrate on its broad organizational impacts, bypassing a focused exploration of its effects within HRM practices. Consequently, the first objective of this study is to investigate the influence of AI integration into HRM on data-driven decision-making.
Moreover, while the existing body of work acknowledges the potential of AI to optimize HRM processes (Bozdag, 2023; Cayrat & Boxall, 2022; Chowdhury et al., 2023; Larson & DeChurch, 2020), it stops short of fully illuminating how AI catalyzes data-driven decision-making within HRM. Despite some recognition of the transformative potential in HRM (Chams & García-Blandón, 2019; Škudienė et al., 2020), the literature lacks empirical evidence that supports these claims. The complexities surrounding AI-driven HRM practices, such as fostering a data-driven decision-making culture and potential challenges therein, remain underexplored.