Effects of humanlikeness and conversational breakdown on trust in chatbots for customer service

Abstract

Trust in chatbots can be shaped by various factors such as humanlikeness in terms of visual appearance and conversational content, and conversational performance in terms of the chatbot’s ability to avoid conversational breakdown. The literature is inconclusive concerning the effect of humanlikeness and conversational performance on trust, especially their interaction effect. To examine the relations among these variables, we conducted a 2x3 (humanlikeness x conversational performance) factorial experiment with 251 participants, who were asked to perform three tasks with a chatbot for an online bank under one of the six conditions. Participants completed a questionnaire measuring trust and commented on trust factors. Results of between-group analysis showed that for the task with seeded breakdowns there were significant differences in trust across the six groups with the lowest ratings for the two groups experiencing breakdowns without repairs and that humanlikeness did not impact the extent to which the trust level changed. Results of within-group analysis showed significant differences in trust across the tasks but non-significant inter-task correlations on trust for the two groups. These observations challenge the effect of humanlikeness on trust while supporting the notion of trust resilience as the participants did not spill the impaired trust over the subsequent task. Thematic analysis showed that inter-group contrasts could be found for the theme ‘underlying functionality’ and ‘affective responses.’ Implications for research, practice and future work were drawn.