This study investigates the impact of artificial intelligence (AI) voice assistants (VAs)' conversational styles on consumer attitudes. This study addresses the following research questions: 1. How do AI conversation styles (abstract vs concrete) influence consumer attitudes in hedonic and utilitarian service contexts? 2. What role does regulatory focus (promotion vs prevention) play in moderating the relationship between AI conversation styles and consumer attitudes? 3. How can AI developers optimize conversation styles to enhance user satisfaction and engagement based on service context and regulatory focus?.
Four experiments were conducted to explore the effect of AI VAs' conversational styles on consumer attitudes.
The results indicate a significant interaction between conversational styles, usage context and regulatory focus. In hedonic contexts, an abstract conversational style enhances consumers' positive attitudes, whereas in utilitarian contexts, a concrete conversational style has a more pronounced effect. Additionally, consumers with a promotion focus tend to evaluate abstract conversational styles more favorably, while those with a prevention focus exhibit a preference for concrete conversational styles. These findings offer valuable insights for designing AI VAs, suggesting that optimizing conversational style based on usage context and consumers' psychological states can enhance user satisfaction.
This research offers a comprehensive understanding of when and how AI conversation styles should be tailored to optimize consumer satisfaction and engagement. From a theoretical perspective, this study contributes to the literature by integrating service context and regulatory focus into the discourse on AI conversation styles. It provides a nuanced framework for designing AI VAs that resonate with users’ emotional and functional needs across different contexts. Managerially, the findings offer actionable insights for AI developers and marketers to enhance user experience by customizing conversation styles according to specific usage scenarios and user psychological orientations.
