The impact of challenges posed by the adoption of artificial intelligence strategy for human resource managers
Artificial intelligence (AI) is revolutionizing industries across the globe and its transformative potential continues to make significant changes in human resource management (HRM) by optimizing recruitment, enhancing employee engagement, and streamlining HRM processes. The study evaluated the challenges faced while adopting AI in HRM to enhance firms’ data transformation processes. The study adopted a quantitative research design and surveys for data collection. A random sampling approach was used to select 169 companies in Jordan, with 200 employees chosen. A multiple regression model (MRM) was used to assess the impact of challenges on AI adoption. The study revealed that data falsification and biased decision-making are the most pervasive challenges, while firm long-term budgeting has been facilitated. The study concludes that adopting AI in HRM results in unfair decision-making and invalidated results. Further financial maintenance is also a factor sufficiently provided by the innovative AI techniques applied in the recruitment process (Jihad & Várallyai, 2021; Prikshat et al., 2023). The Jordanian context, which is culturally distinct from the rest of the globe, has not had its AI-related challenges well addressed. This research addresses a gap in the literature by describing the challenges faced by human resource (HR) managers in Jordan when attempting to integrate AI and by proposing a solution to these problems.
Authors
- Naser Khdour (ORCID: https://orcid.org/0000-0002-3044-8792)
- Roberta Fenech (ORCID: https://orcid.org/0000-0002-0001-4563)
- Priya Baguant (ORCID: https://orcid.org/0000-0002-7505-2601)
- Alessandra Theuma (ORCID: https://orcid.org/0000-0002-4273-6564)
Institutions
- Higher Colleges of Technology (AE)
Publication Details
- Journal
- Corporate and Business Strategy Review
- Published
- 2025-06-20
- DOI
- https://doi.org/10.22495/cbsrv6i3art3
- Citations
- 1
- Primary Topic
- Organizational and Employee Performance
- Type
- article
- Field-Weighted Citation Impact
- 2.48