The management of the reward system is a critical aspect of Human Resource Management (HRM), yet contradictions often arise between policy design and implementation. This study aims to explore how Artificial Intelligence (AI) techniques can be applied to ensure fair and transparent reward distribution, thereby enhancing employee motivation and job satisfaction. The AI reward system continuously monitors employee performance and allocates rewards in real time when targets are achieved or expectations are exceeded, reducing delays associated with annual appraisals. A quantitative research design was employed, using probability sampling with a stratified sampling technique to collect data using the sample size of 377, Rao-soft software was used to compute the sample size of 377 respondents, as per the sample size formula with a 95% confidence level. The sample size of 377 respondents was therefore considered sufficient for population sizes up to 20,000 under these parameters. The findings indicate that AI-driven reward systems improve fairness, transparency, and efficiency compared to traditional manual appraisal methods. They also address misinterpretation issues and enable personalized, timely recognition, which contributes positively to employee motivation and satisfaction. This study contributes to existing literature by highlighting employees’ perceptions of AI-based reward systems and their influence on job satisfaction. The implications suggest that organizations should integrate AI-driven reward mechanisms to deliver unbiased, efficient, and individualized recognition, supporting both motivation and satisfaction for long-term organizational development and competitive advantage.
Batool et al. (Mon,) studied this question.