Conversational Breakdown in a Customer Service Chatbot: Impact of Task Order and Criticality on User Trust and Emotion
While chatbots are increasingly used for customer service, there is a knowledge gap concerning the impact of Conversational Breakdown in such chatbot interactions. In a 2 \({\times}\) 4 factorial design online experiment, we studied how Conversational Breakdown impacts user emotion and trust in a chatbot for customer service, given variations in task criticality and breakdown task order. Here, 257 participants were randomly assigned to complete high- or low-criticality tasks with a prototype chatbot for customer service, experiencing Conversational Breakdown for the first, second, third or none of their tasks. The task set was decided from a 63-participant pre-study. We found significant impact of Conversational Breakdown, including a marked order effect on overall trust, as well as a bounce-back effect on task-specific trust and emotion after subsequent successful task completion. We found no post-interaction effect of Task Criticality. Based on our findings, we discuss theoretical and practical implications and suggest future research.
Authors
- Effie Lai‐Chong Law (ORCID: https://orcid.org/0000-0002-0873-0150)
- Asbjørn Følstad (ORCID: https://orcid.org/0000-0003-2763-0996)
- Nena van As (ORCID: https://orcid.org/0000-0002-8826-1509)
Institutions
- SINTEF (NO)
- Durham University (GB)
- Jaeder (Norway) (NO)
Publication Details
- Journal
- ACM Transactions on Computer-Human Interaction
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1145/3690383
- Citations
- 9
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 3.13
Funders
- Norges Forskningsråd