Conversational repair strategies to cope with errors and breakdowns in customer service chatbot conversations

This study aimed to investigate (1) what errors and conversational repair strategies appear during conversations with a real-life customer service chatbot and (2) how people perceive these errors and repair strategies in terms of user satisfaction, brand attitude, and trust. This study involved a corpus study of real-life conversations (N=100) with a customer service chatbot to investigate which errors and repairs occurred to inform a follow-up online experiment (N=150) on the perception of these errors and repairs. The experiment employed a 3 (error; excess of information, unsolvable question, lack of information) by 3 (repair strategy; repeat, options, defer) mixed subject design with the type of error as between-subjects factor and repair strategy as within-subjects factor. The results revealed that the repair strategy defer most positively impacted perceptions of trust and brand attitude, followed by the strategy options, and lastly repeat. In contrast, no significant main effects of error type nor interaction effects were found on user satisfaction, trust, and brand attitude. However, the open-ended questions revealed that there might be a connection between the nature of the customer request and the repair strategy.