Impact of operational human resource management and artificial intelligence on retaining talented employees in the ministry of interior at Abu Dhabi-UAE

Over the past two decades, the study of human resource management and its impact on both organizations and individuals has garnered significant attention within academic research, particularly within the realm of management practices. The main objective of this research endeavour is to examine the impact of various operational functions of
Human Resource Management (HRM), including employee recruitment, training and development, compensation, employee integration, maintenance, and motivation, as well as the adoption of artificial intelligence, on employee retention within the Ministry of
Interior, Abu Dhabi. The proposed theoretical framework of this study encompasses an analysis of five independent variables, namely employee recruitment, training and development, compensation, employee integration, retention, and motivation, which are recognized as crucial operational functions of HRM. Additionally, this study incorporates one dependent variable, namely employee retention, and one moderating variable, which pertains to the adoption of technology and artificial intelligence. In total, the research presents 11 hypotheses, each focusing on a specific relationship within the conceptual framework. To accomplish the study's objective, a cross-sectional survey design was employed, targeting civil servants affiliated with the
Ministry of Interior in Abu Dhabi. The survey sample encompassed 366 employees, representing a fraction of the total workforce of 7,500 individuals employed at the ministry. Out of the 471 questionnaires distributed, the final analysis was based on the feedback received from 371 employees. The findings revealed that the combined influence of the five independent variables accounted for a substantial 74.4% of the variance observed in the employee retention. Based on the obtained results, all independent variables exhibit statistically significant
positive effects. The precedence of the five relationships, based on the path coefficient values, is as follows: compensation holds the highest value (0.195), followed by employee recruitment (0.191), maintenance and motivation (0.165), employee integration (0.163), and training and development (0.116). Regarding the moderating effect of the adoption of artificial intelligence, it was observed that there are two relationships deemed unacceptable for both employee recruitment and maintenance and motivation. Their impact ranking is as follows: training and development takes the first place (0.094), followed by employee integration (0.085), and compensation (0.070) holds the third position. These research findings hold valuable implications for public sector
management, highlighting the significance of recruitment beyond salary considerations. From a theoretical perspective, the study provides robust evidence supporting the applicability of the resource-based view theory in the public sector, while emphasizing the need to incorporate variables related to technology and AI adoption in management studies. Future research endeavours could replicate this study in diverse contexts to compare results and introduce additional variables, thereby enhancing the predictive capacity of the conceptual framework.