Global Journal of Management and Business Research, A: Administration and Management, Volume 23 Issue 5

hiring, which may lead to discriminative impacts on minorities/diversity. Democratizing data, transparency, and providing data and insights to employees is another aspect that needs further exploration (Hirsch, 2019). Practical implication of the findings is related to the skill development of HR practitioners. There is lack of research regarding a key aspect, which is related to the expectations of new skills and competencies that HR professionals need to be proficient in, to adopt and apply AI and leverage its benefits. The skills of present and future HR practitioners will need to be developed to manage today's AI applications and future advancements. HR practitioners need to learn how to use AI-enabled analytical tools. They also need to be able to interpret and take action basis the analysis, thus developing numerical analysis and reasoning skills will also be required (Davenport, 2019). HR professionals need to have the competence to utilize technology to provide insights that support business, which necessitates the skill development of HR professionals (Wang and Lin, 2020). There are hardly any studies conducted on this key aspect of AI in HRM, which is the new skills and competencies that HR professionals need to be proficient in adopting and applying AI applications in HRM and leveraging all the benefits. Recent studies indicate that COVID-19 may accelerate the adoption of AI in HRM (Hamouche, 2021; Vahdat, 2021; Khalifa, 2022). There is also research gap related to actual impact studies providing insights on the adoption of AI and related technologies on the transformation of HR, which can be potentially leveraged for future growth and advancement of the HR function. The adoption of AI in HRM has resulted in the effectiveness of HR processes, service delivery, and enhanced employee experience. It is imperative to study and interpret the further trends and opportunities of AI as applied to HRM. This work provides an exhaustive study of the emergence and accelerated growth of research on AI in HRM. We have evaluated the current status of research in the domain of AI in HRM and demonstrated the research gaps. In general, studies directly addressing AI in HRM in the abstract, title, or keywords has been continuously growing since 2010. The growth trajectory of literature indicates that it has more than doubled in size over the past decade. Analysis related to various aspects of research, be it types of documents and volume of documents, conceptual coherence, and citation impact, reveals that the most prevalent research areas are talent acquisition, resource allocation, and training and development in applying AI in HRM. Other predominant areas of research highlighted are neural networks, fuzzy logic, and evaluation models. Various future research implications are also discussed. Though this study has limitations in that it has considered only the publications indexed by Scopus, the comparatively small amount of research articles directly addressing the field suggest that further research is needed, focusing on areas of systematic theory development as well as conceptual and empirical studies. AI in HRM, being a rapidly developing area, there is substantial literature and research in the form of white papers and industry reports, which may lead to a lack of requisite bibliographic control. R eferences R éférences R eferencias 1. Albert, E. T. (2019). AI in talent acquisition: a review of AI-applications used in recruitment and selection. Strategic HR Review , 18 (5), 215-221. 2. Andalib, T. W., Halim, H. A., & Islam, M. K. (2020, February). Coding mechanism and soft systems technique applied to integrate the fuzzy based Decision Support System with HRM factors in the SMEs of Bangladesh. IOP Conference Series: Materials Science and Engineering. 769 (1), 012042. IOP Publishing. 3. Apornak, A., Raissi, S., Keramati, A., & Khalili- Damghani, K. (2021).Human resources optimization in hospital emergency using the genetic algorithm approach. International Journal of Healthcare Management , 14 (4), 1441-1448. 4. Aviso, K. B., Mayol, A. P., Promentilla, M. A. B., Santos, J. R., Tan, R. R., Ubando, A. T., & Yu, K. D. S. (2018). Allocating human resources in organiza- tions operating under crisis conditions: A fuzzy input-output optimization modeling framework. Resources, Conservation and Recycling , 128 , 250- 258. 5. Bersin, J. (2017). 4 powerful AI for HR examples that show promise, minefields Retrieved June 2, 2020, from https://www.techtarget.com/searchhrsoftware/ tip/4-powerful-AI-for-HR-examples-that-show- promise-minefields. 6. Bizer, C., Heese, R., Mochol, M., Oldakowski, R., Tolksdorf, R., & Eckstein, R. (2005). The impact of semantic web technologies on job recruitment processes . In Wirtschaftsinformatik, Physica, Heidelberg.1367-1381. 7. Bogen, M. (2019). All the ways hiring algorithms can introduce bias. Harvard Business Review , 6 , 2019. 8. Bondarouk, T., & Brewster, C. (2016). Conceptualizing the future of HRM and technology research. The International Journal of Human Resource Management, 27 (21), 2652-2671. 9. Bondarouk, T., Parry, E., & Furtmueller, E. (2017). Electronic HRM: four decades of research on adoption and consequences. International Journal of Human Resource Management, 28 (1), 98-131. 10. Bondarouk, T. V. (2014). Orchestrating electronic HRM . Enschede: Twente University Press. Research on Artificial Intelligence in Human Resource Management: Trends and Prospects 42 Global Journal of Management and Business Research Volume XXIII Issue V Version I Year 2023 ( ) A © 2023 Global Journals V. C onclusion