Artificial Intelligence Adoption, Perceived Value, Employee Job Satisfaction, and Performance
A Structural Equation Modeling Study in Yemen
DOI:
https://doi.org/10.64059/eiu.v3i1.92Keywords:
Employee Job Satisfaction, Employee Performance, AI Impact, Structural Equation Modeling, Yemeni EnterprisesAbstract
Given the ongoing political conflict and economic uncertainty in Yemen, Human Resources Management (HRM) has emerged as a crucial research topic for achieving better organizational performance, particularly with the increasing adoption of Artificial Intelligence (AI) technology in the workplace. This study aims to examine whether variables, such as employee perception, expectations, perceived value, and AI adoption influence employee job satisfaction and performance. Through a questionnaire survey yielding 201 valid responses, this study employs the Structural Equation Modeling (SEM) approach to analyze data using Smart-PLS software. Key findings can be summarized as follows: (1) AI adoption facilitates perceived value to be the strongest direct factor affecting employee job satisfaction; (2) Employee job satisfaction contributes to employee performance through employee loyalty; and (3) Employee expectation and perception are the enablers of AI’s impact and job satisfaction, creating a reinforcing cycle. These findings provide insights for policymakers and organizations to recognize the role of investing in AI-enabled HRM systems and their capacity to foster workforce stability and employee performance in uncertain situations.
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