Global Journal of Management and Business Research, A: Administration and Management, Volume 23 Issue 2

A survey of the existing literature identifies the gaps that exist in the application of AI in HR recruitment systems. Liu et al. (2021) observe that every business must attract and recruit strong talent to develop their organization in today's competitive world. These authors investigated HR teams that consolidated performance scores and corresponding recruitment entry channels (campus, online, internal transfers, and referrals). The performance scores included three dimensions of an employee's performance: subjective awareness, job satisfaction, and job self-dedication. This data was then processed through AI regression analysis to determine the correlation coefficient's T-value and how predictable the entry channel was as an indicator of performance. Dijkkamp (2019) used an exploratory research method with a case study to investigate the organization AI recruiting, which supported the study by allowing access to company documents, presentations, communications, conversational interviews, and policy documents (Dijkkamp, 2019). The author collected different perspectives from the company’s HR professionals, including asking them about AI replacing their jobs. Through the case study, Dijkkamp was able to map changes to the HR teams once AI was Research Gaps in HR Applications of AI in Recruitment integrated into their workflow. Geetha and BanthuSree Reddy (2018) examined how AI influenced recruitment strategy and evaluated AI's value in an organization. Their study utilized a literature review, which found that HR professionals'view of AI replacing their jobs was not a concern. Rather, study findings suggest that AI eases HR's mundane activities and allows more time for effective work. Johansson and Herranen (2019) used a combination of conversational interviews with company employees of various designations and literature reviews to explore the application of AI in HRM and its impact on the traditional recruitment process. Firstly, these authors found that AI in HRM is very new and involves high one- time integration and HR training costs. Secondly, AI can cater to about half of the recruitment process, but the rest requires human-specific capabilities that are beyond current AI. Finally, AI integration in HRM gives companies access to a larger candidate pool, speeds up parts of the recruitment process, and handles the more mundane HR tasks. Bhalgat (2019) used a wide- scale literature survey that included topics such as information technology (IT) in recruitment, recruitment strategy and planning, and AI in recruitment and its risks. Bhalgat concluded that the use of AI-based applications in recruitment is an emerging field that is growing rapidly with technological innovations and data expansion. In particular, the growth of AI helps HRM optimize the recruitment process by eliminating repetitive tasks (like sourcing and screening) that otherwise fall to HR staff. In their study of an AI recruitment integration application, Son et al. (2019) note that AI-based interview tools improve thousands of applicants. The paper observes that AI has the potential to evaluate employee experiences, train employees in various job skills, and make decisions necessary for business management. Klucin (2020) states that only large companies try AI in recruitment, pointing out that using AI for recruitment is a new process still caught up in ongoing research, and there are feasibility concerns making it work with recruitment in actual practice. Nawaz (2020) studied facial recognition AI applications and their impact on recruitment, and found that the AI applications could accurately judge an applicant's body language during job interviews. Johnson et al. (2021t) argue that e- recruitment with AI helps talent acquisition in the tourism and hospitality industry. Arslan et al. (2021)observe that AI challenges for HRM users create closer connections between leaders and their departments. These authors described the close collaboration between AI-based tools and human workers in Industry 4.0, noting that the AIs’ interaction data accumulated over time. Leadership AI-based tools then used this accumulated data to recommend the best advice (regarding decision- making) for their team to reduce their challenges and improve their workflow. Wan Ibrahim and Hassan (2019) also examined the implications of AI in HRM in Industry interviewing recruitment specialists. Vedapradha et al. (2019) reviewed the adaptability of AI and how this impacts the performance of employees. Lahti (2020) observes that the rapid growth of technology always creates new business opportunities, which applies to AI recruitment. Hovland (2021) notes that the advent of AI innovated internal HRM processes and contributed to unbiased and fair recruitment and selection processes. This paper exemplifies the affordance–actualization theory in HRM. Mujtaba (2020) and Veluchamy et al. (2021) both statethat using AI in recruitment creates inherent bias leading to career issues. The paper discusses using tools to reduce bias and add fairness in order to create optimal career pathways for workers, which they state is necessary given the radical changes in AI integration. Rezzani (2021) suggests there are problems with user acceptance of AI in an industrial IT environment. The paper explains bias, ethical concerns, and the human/AI mix in decision-making (Rezzani, 2021). Abou Hamdan (2019) addresses the application of AI for screening resumes to improve HRM efficiency and reduce errors and bias. Cavaliereet al. (2021) 55 Global Journal of Management and Business Research Volume XXIII Issue II Version I Year 2023 ( ) A © 2023 Global Journals communication between the applicant and the recruiter; the authors scale this experience to the interviews of studied the recent increase of e-recruitment over traditional recruitment and its associated reasons, such 4.0 (where 16% of the workforce is retiring and more jobs are being automated). These authors evaluated the pros and cons of AI in HRM. Kekkonen (2020) applied AI to HRM analyses of highly skilled specialists and senior managers, finding that recruitment specialists followed an abductive approach *. The author analyzed the recruitment and selection process theory (or practice) by