Customers' intention to compliment and complain via AI-enabled platforms: A self-disclosure perspective.

Building upon the communication privacy management theory, the research reveals the effect of self-disclosure on the identified mechanisms of perceived emotional value, performance expectancy, and privacy concerns, which in turn, influence customers' intention to compliment and complain via AI-enabled platforms. Findings from two quasi-experiments with 439 valid responses from U.S. customers suggest that customers are more likely to express their feelings when low self-disclosure AI technology is presented. The results suggest a prominent role of privacy concerns in mediating the effect of self-disclosure on customers' intention to compliment and complain. The effects of self-disclosure also channel through perceived emotional value and performance expectancy when customers want to leave a compliment. The moderating effect of reward timing was examined. Similarities and differences between customers' intentions to compliment or complain using AI-enabled platforms are discussed to provide theoretical and practical implications. • Customers are more likely to use AI-enabled platforms with low self-disclosure. • Emotional value mediates the effect of self-disclosure on compliment intentions. • Performance expectancy is a significant mediator when it comes to compliment. • Privacy concerns hinder customers' intention to complain via AI-enabled platforms. • Delayed (vs. immediate) rewards encourage customers to compliment. [ABSTRACT FROM AUTHOR]