Unlike complaints with intense dissatisfaction that typically focus on specific failures, customer feedback stemming from milder dissatisfaction with constructive comments tends to be more beneficial for a company’s success. This study aims to address this gap by exploring how AI service robots might enhance feedback collection, compared to human approaches across various scenarios.
Conceptualized on the commitment-trust theory, this research delves into the relationship between motivation and willingness to share customer feedback catalyzed by three factors: feedback type (self-related vs non-self-related), agent type (robot vs human) and interaction frequency that refers to the sequence of interactions with the same or varied agents. Progressively, these three factors were tested in three studies accordingly using the scenario-based experiment approaches.
Through juxtaposing a series of three empirical studies, the results show that the interplay of the three examined factors leads to findings that challenge conventional assumptions; especially when customers share non-self-related feedback, a robot can generate higher customer trust and perceived utility than a human agent, and when feedback from customers encompasses both self-related and non-self-related aspects, customer trust mediates the moderating effect of interaction frequency on the influence of perceived utility.
This study provides nuanced insights into the optimal service agent type for collecting distinct feedback categories. Additionally, it elucidates how the sequence of agent interactions shapes divergent customer cognitive responses.
