A Systematic Literature Review on the Impact of AI Integration on Job Satisfaction: Guiding the Shenzhen Tech Industry on the Roles of AI Task Efficiency and Job Autonomy

The study explores the relationship between Artificial Intelligence (AI) integration and job satisfaction, with a focus on the roles of task efficiency and job autonomy. AI integration has accelerated in the technology industry of Shenzhen, which has made it increasingly important to understand its impact on employee outcomes. The systematic literature review follows PRISMA guidelines, which results in a final sample of 31 studies published between 2020 and 2026. The review synthesises existing literature using a thematic analysis to understand the mechanisms and patterns connecting AI integration to job satisfaction. The findings show that AI integration has a complex relationship with job satisfaction, and the relationship is not linear. AI integration can improve task efficiency by automating routine tasks and by allowing for more meaningful work, but the impact on job autonomy varies according to implementation. Task efficiency and job autonomy are important variables that affect job satisfaction. Positive outcomes happen when increased efficiency is accompanied by greater autonomy, but negative outcomes happen when there is misalignment between employees and the work environment. The review offers a novel contribution by integrating multiple theoretical perspectives to explain how AI integration affects job satisfaction through interrelated mechanisms.