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

related to limitations imposed by small data sets, the intricacy of HR phenomena, aspects of fairness, ethical, legal, and accounting aspects in the adoption of AI in HR. There are various challenges ranging from empirical to conceptual, which can be attributed to the adoption of AI in HRM (Kaur et al., 2021). Thus, AI has become progressively of great interest to scholars and practitioners.The impact of AI on HRM has necessitated the need for conducting an exhaustive study of the research landscape of this critical domain. It appears from the literature that the research on applications of AI in HRM is receiving a greater focus and many related relevant areas remain unexplored. The current study has attempted to provide insights to the following research questions (RQs): RQ1: What are the research trends of publications in terms of source types, citations, document types etc. related to AI in HRM? RQ2: What are the key themes of research in the domain of AI in HRM? RQ3: Which are the areas/domains that require additional and focused examination in the future related to AI in HRM? This study was conducted using bibliometrics analysis to provide reference, insights, and inputs for future research and to share in-depth insights into aspects of AI-HRM research trends with the Scopus database as the base. This paper is arranged into five sections. The following section briefs the methods, data and the search strategy used in this study. Then we present the analysis and findings of the study. After that we provide a detailed discussion on major research themes identified and the future research directions followed by concluding remarks. a) Method Bibliometric analysis has been widely recognized as one of the significant methods to determine and forecast the research trends of specific topics (Zupic and Cater, 2015). Bibliometric analysis- based studies include methods for understanding the global trends in research within a particular field using a database of scholarly literature and presents findings and discussions on the evolution and intellectual structure of knowledge base in that field. In this study, we conducted a bibliometric analysis using citation analysis, co-citation analysis, keyword co-occurrence analysis, and clustering (Ellegaard and Wallin, 2015; Linnenluecke et al., 2020; Donthu et al., 2021). The following section presents the data source and search strategy applied in this study. b) Data Source Scopus is a detailed database with adequate inbuilt search filters (Oliveira et al., 2019). Thus, we have earmarked Scopus as the data repository to pull out relevant results related to the research. All related studies published and indexed in Scopus till the end of the year 2020 has been reviewed to document and analyse key trends since the emergence of research related to AI in HRM. c) Search Strategy Systematic methods were deployed in the research by adopting the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to make the article selection process to be objective and systematic (Priyashantha et al., 2021). As represented in the flow diagram (Figure 1), we have followed a search protocol and inclusion/exclusion criteria through the four steps of identification, screening, eligibility and inclusion for arriving at the list of articles to be analysed as mentioned in Meline (2006) and Pahlevan-Sharif (2019). The study integrated an extensive range of document types, indexed by Scopus, including books, chapters/ sections of books, journal articles, and conference papers published by 2020 on the topic. As there was no specific start date being referenced for the Scopus search in the literature, thus it facilitated the search engine to identify the earliest studies related to this topic. After the initial screening of documents from a relevant perspective, the final database reflected a total of 247 documents, resultant of query results by using keywords "Human Resource" OR "HRM" OR "Talent" OR "Personnel Management" OR "HRD" AND "AI" OR "Artificial Intelligence" OR "Machine Learning" OR "Deep Learning" OR "Neural Network" OR "Fuzzy" OR "ANN" OR "Genetic Algorithm" OR "Predict". Additionally, all available metadata related to the title, abstract, keyword, and related research studies were downloaded and analysed. Additional data editing was conducted for specific fields of the search, which included instances wherein synonyms were merged (e.g., 'Human Resource Management, 'Human Resources Management' and 'HRM'). The quantitative review aimed to examine the present status of literature related to AI in HRM and to recognize, identify, and trace clusters and integrated research. Research on Artificial Intelligence in Human Resource Management: Trends and Prospects 32 Global Journal of Management and Business Research Volume XXIII Issue V Version I Year 2023 ( ) A © 2023 Global Journals II. M ethodology