AI at Work: Drivers of Employee Satisfaction in E-Commerce

Abstract

Artificial intelligence is increasingly integrated into e-commerce operations, influencing employee work processes and evaluations of their roles. The research examines the relationships between performance expectancy, effort expectancy, job autonomy, perceived intelligence of AI systems, and employee job satisfaction. A quantitative cross-sectional survey was conducted among professionals working with AI-enabled tools in e-commerce organizations. Data were analyzed using regression techniques to assess the proposed relationships. The results indicate that performance expectancy and job autonomy have strong positive effects on job satisfaction, highlighting the importance of efficiency improvements and decision control in AI-supported work. Effort expectancy shows a weaker but significant relationship, with greater relevance for less experienced users. Perceived intelligence is also positively associated with job satisfaction, reflecting the role of trust in system accuracy and contextual relevance. Differences across roles and experience levels indicate that employee evaluations vary based on task characteristics and familiarity with AI systems. The findings contribute to existing research by combining technology acceptance and job design perspectives in the context of AI-enabled work environments. Practical implications emphasize the need for systems that improve performance while maintaining employee control and supporting effective interaction with AI tools.