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.

Automated summary

Drawing upon the communication privacy management theory, this research investigates the impact of self-disclosure on customers' intention to compliment and complain via AI-enabled platforms. The findings suggest that low self-disclosure AI technology encourages customers to express their emotions, and the role of privacy concerns as a mediating variable was identified. Additionally, perceived emotional value and performance expectancy were found to influence customers' intention to compliment. The moderating effect of reward timing was also examined.