Artificial Normality: How Conversational Agents’ Perceived Humanness Inhibits Error Attribution and Preserves Satisfaction

We theorize that designing conversational agents (CAs) to appear more humanlike will make minor errors appear more normal because to err is human. When errors appear more normal, users are less likely to strive to identify their cause (a process called attribution) and thus are less likely to respond negatively. We conducted two experiments to test our theoretical model, and the results generally support our theorizing: greater perceived humanness preserves the perception of situational normality when an error occurs, thereby reducing error attribution and mitigating the negative effects of errors on service satisfaction. Our research contributes to the theory by identifying a theoretical mechanism that underlies users’ responses to errors (a reduction in situational normality triggers error attribution). It also has important implications for practice by showing that designing CAs to be more humanlike is important for CAs more likely to make errors (e.g. CAs controlled by large language models) and less important for other CAs (e.g. those controlled by robust rule-based scripts).