The Effects of Self-Efficacy on Job Burnout and Organizational Commitment in an LLM-Based AI Adoption Environment

The rapid development of generative artificial intelligence(AI) and large language models(LLM) has significantly transformed organizational work environments and service operations. While the adoption of AI technologies improves productivity and operational efficiency, it may also create psychological challenges for employees due to job uncertainty and changes in work roles. In this context, this study aims to examine the effects of self-efficacy on job burnout and organizational commitment in an LLM-based AI adoption environment. A survey was conducted among employees working in organizations where AI-based systems or digital technologies have been implemented. A total of 230 valid responses were collected and analyzed using structural equation modeling(SEM) with SPSS and AMOS. The key constructs examined in this study include self-efficacy, job burnout(emotional exhaustion, depersonalization, and reduced personal accomplishment), and organizational commitment. The results indicate that self-efficacy has significant negative effects on all three dimensions of job burnout. In addition, self-efficacy positively influences organizational commitment. Furthermore, the three dimensions of job burnout -emotional exhaustion, depersonalization, and reduced personal accomplishment- have significant negative effects on organizational commitment. These findings highlight the critical role of self-efficacy as a psychological resource in AI-driven work environments and provide meaningful implications for effective human resource management strategies in the era of digital transformation.