AI-Driven Talent Acquisition Systems: Transforming Recruitment Strategies in the Digital Age

This empirical study focuses on the efficacy and efficiency of recruitment strategies in healthcare industry driven by organizational size, complexity as well as AI-based systems used for talent acquisition. These factors exert their influence through the mediating position that quality of candidate-job fit plays in this study. This research is conducted in the health sector in Dubai, United Arab Emirates with a valid sample of 309 employees from both public and private healthcare institutions. One of the main objectives is to measure how Al-driven, organizational size and complexity impacts efficacy and efficiency in recruitment strategies. In the case of health care, this study also considers how candidate-job fit quality mediates in such a relationship. In a quantitative way, there is an application of random sampling. The participants of this study were the employees in Dubai's health care industry, and a valid sample was formed from 309 respondents. To evaluate the hypotheses and model the interactions between the variables, the study uses partial least squares structural equation modelling, or PLS SEM. The research underscores the importance of organizational size and complexity and adds to the expanding body of knowledge on the use of AI in talent acquisition in the healthcare industry. The study intends to offer practical insights for healthcare organizations looking to improve their recruiting strategies through the implementation of AI-driven systems by investigating the mediating role of candidate-job match quality.