How AI enhances employee service innovation in retail: Social exchange theory perspectives and the impact of AI adaptability

Abstract

As Artificial Intelligence (AI) technologies continue to transform service industries, particularly in retail, their influence extends beyond efficiency to fostering employee creativity and innovation. However, the impact of AI service quality on employees' innovative behavior remains underexplored in existing literature. Applying Social Exchange Theory (SET), this study investigates how employees' perceptions of AI service quality—specifically system-related attributes (reliability and transparency) and interaction-related attributes (responsiveness and empathy)—influence their innovative service behavior in retail settings. Additionally, the moderating role of AI adaptability, a key feature that allows AI systems to adjust their behavior based on user needs, is examined for its potential to enhance service quality. Data from 290 retail employees were analyzed using structural equation modeling, revealing that reliability and empathy significantly enhance employees’ innovative service behavior, which in turn positively impacts employee-job fit and job satisfaction. Moreover, AI adaptability amplifies the impact of empathy while reducing the influence of system-related attributes. This study highlights an employee-centric perspective on AI service quality within the context of human-AI interactions, demonstrating how AI systems foster creativity and innovation in the workplace. By showcasing the potential of AI in workplace dynamics and offering actionable insights through the integration of adaptive AI systems, this research contributes to advancing practical understanding in the field.