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

Profile matching,Automation of resume/job description, Optical character recognition (OCR), Advanced application tracking systems, Inclusive AI algorithms for unbiased screening of profiles Barboza (2019); Cohen (2019); Esch & Black (2019); Gikopoulos (2019); Guenole & Feinzig (2019); Kaplan & Haenlein (2019); Meister (2018b); Nunn (2019); Rogers (2018); Sivathanu & Pillai (2018); Strohmeier & Piazza (2015) Talent selection techniques, Neural network, Fuzzy Systems, Data mining techniques,Gaming techniques for selection, AI chatbots – interpretation/validation of candidate response Bersin (2017); Huang et al.(2001,2004); Ranjan et al. (2008); Sivathanu & Pillai (2018); Ernest & Young (2018); Meister (2018b); Mentzelopoulos & Economou (2020); Johnson et al. (2020); Jimenez et al.(2018); Qin et al. (2020); Ye et al.(2019) Interview methods with Unconscious bias reduced -AI tools "listen"/prompt question, Robotic Process Automation (RPAs) Cohen (2019); Gikopoulos (2019); Guenole & Feinzig 2019); HRPA (2017) Onboarding, Natural language processing, for chatbot- agnostic technology and text-based conversational interface of Chatbots as an online buddy, customization, and automation, and core business functions, AI algorithms to map team fit,learning needs on Day 1 Barboza (2019); Gikopoulos (2019); Upadhyay & Khandelwal (2019) Table 8: Literature Related to Cluster 3: AI in Training & Development Research on HRM Functionality Research Source AI tagging of learning content through metadata, AI- enabled tutoring systems, intelligent agents embedded Barboza (2019); Guenole & Feinzig (2019); Meister (2019a); Niehueser & Boak (2020); Schweyer (2018); Qiong et al. (2018) Individual Development Plan, Succession Planning Smart data matching and AI-enabled Individual profile analysis to identify the right talent for key roles Barboza (2019); Bersin (2017); Nunn (2019) Career mobility through digital coaching Virtual assistant-datafor personalized career counselling. Bersin (2017); Ernest & Young (2018); HRPA (2017); IBM (2019);Kiron & Spindel (2019) Skill gap analysis, Gaming techniques for Deep Learning, Current analytics, and predictive analytics of skills required Barboza (2019);Guo & Li (2020); Nunn, (2019a); Mentzelopoulos & Economou (2020); The effectiveness of HR function is greatly enhanced by the adoption of AI and the associated technologies. The third research question (RQ3) addresses the areas/domains that require additional and focused research in the future related to AI in HRM. There has been a steady pace of development of applications related to AI technologies for the HRM function, which have been covered with adequate research. The analysis shows that a significant portion of the research was published during the last decade, with a sharp increase since 2018. In the country-wise publication output, China is leading with more than 42% of documents published in the area of AI in HRM. As pointed out in (Demchak, 2019; Li et al., 2021), China is abundant with the two critical assets needed in the AI era, i.e., data and engineering talent, which could be the reason behind its prominence in this field. The keyword and term co-occurrence analysis were conducted to explore the prevailing themes in the research related to AI in HRM. Three clusters emerged from the term co- occurrence analysis based on title and abstract is represented in Figure 5. A brief discussion on these clusters is presented in the following sub sections. a) Cluster 1 - Resource Allocation The logical allocation of human resources plays an important role for the organization's development and Research on Artificial Intelligence in Human Resource Management: Trends and Prospects 39 Global Journal of Management and Business Research Volume XXIII Issue V Version I Year 2023 ( ) A © 2023 Global Journals IV. D iscussion and F uture R esearch D irections