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

Optimization Personnel Training Genetic Algorithms Hierarchical Systems Performance Evaluation Enterprise Human Resource Recruitment Deep Learning Fuzzy Evaluation Genetic Algorithm Talent Management Decision Support Systems 14 14 12 12 12 10 10 9 9 9 8 7 5.67% 5.67% 4.86% 4.86% 4.86% 4.05% 4.05% 3.64% 3.64% 3.64% 3.24% 2.83% Decision Theory 7 2.83% Fuzzy Systems 7 2.83% Human Resource Allocation 7 2.83% Innovation 7 2.83% Machine Learning 7 2.83% Membership Functions 7 2.83% Neural Network 7 2.83% Project Management 7 2.83% Analytical Hierarchy Process 6 2.43% Big Data 6 2.43% Source: Scopus Database; 1993 to 2020 and Authors Compilation Figure 3 represents country-based bibliometric coupling, indicating that the countries presented therein cite similar literature in their publications. Higher bibliometric coupling indicates that the studies deal with related subject matter (Martyn, 1964). The figure shows that strongest bibliometric coupling exists between China and the United States, indicating that the studies originated from China and the United States have common citations more frequently. Table 5 is representative of the main keywords related to AI in HRM, which indicate the functionalities and technologies associated with AI in the HRM field that is currently being referenced in research. These keywords related to functionalities are – "decision making", "resource allocation", "optimisation", "personnel training", "performance evaluation", "talent management". The corresponding keywords related to AI technologies are – "neural networks", "genetic algorithms", "deep learning", "fuzzy support systems". Research on HRM Functionality/AI Technique Research Sources Demand forecasting, Allocation of resources, Prediction tasks, Neural Networks applications, Analytic Hierarchy Process, Fuzzy Mathematics Aviso et al.(2018);Andalib et al.(2020);Apornak etal.(2021);Coelho et al.(2019);Chang (2010); Ivanov et al. (2020); Kieling et al.(2019); Khanizad & Montazer (2018); Kwak & Jung, (2003); Markevich & Sidorenko (2019); Xu et al.(2019) Prioritization of resource demands, Resource optimization Daojin (2010); Guenole & Feinzig (2019); Hsu et al. (2019); Lin & Gen (2008) Reverse candidate profiling, Ideal Fit for role, Balanced Job descriptions, AI Algorithms Gikopoulos(2019); Guenole & Feinzig (2019); Rogers (2018); Leem (1996) a) Term Co-Occurrence Analysis In order to identify the research focus, a co- occurrence analysis was conducted of all keywords, including author keywords and index keywords, using VOS viewer software (Van Eck and Waltman, 2010). As reflected in Figure 5, areas of maximum focus in the research related to AI in HRM literature are talent acquisition for recruitment and selection, resource allocation, and personnel training. The analysis also reflects the related AI technologies that support these functionalities: machine learning, data mining, big data, deep learning, neural network, and fuzzy logic. Co-occurrence network (Figure 5), based on title and abstract fields, indicates that the significant overlapping areas in AI in HRM research are resource allocation, training & development, talent acquisition, 36 Global Journal of Management and Business Research Volume XXIII Issue V Version I Year 2023 ( ) A Research on Artificial Intelligence in Human Resource Management: Trends and Prospects © 2023 Global Journals Table 6: Literature Related to Cluster 1: AI in Resource Allocation