ECIS 2020 Research Papers
Abstract
Recent years have seen increased interest in the application of chatbots for conversational commerce. However, many chatbots are falling short of their expectations because customers are reluctant to disclose personal information to them (e.g., product interest, email address). Drawing on social response theory and similarity-attraction theory, we investigated (1) how a chatbot’s language style influences users’ perceived similarity in dominance (i.e., an important facet of personality) between them and the chatbot and (2) how these perceptions influence their self-disclosure behavior. We conducted an online experiment (N=205) with two chatbots with different language styles (dominant vs. submissive). Our results show that users attribute a dominant personality to a chatbot that uses strong language with frequent assertions, commands, and self-confident statements. Moreover, we find that the interplay of the user’s own dominance and the chatbot’s perceived dominance creates perceptions of similarity. These perceptions of similarity increase users’ degree of self-disclosure via an increased likelihood of accepting the chatbot’s advice. Our study reveals that language style is an important design feature of chatbots and highlights the need to account for the interplay of design features and user characteristics. Furthermore, it also advances our understanding of the impact of design on self-disclosure behavior.
Recommended Citation
Gnewuch, Ulrich; Yu, Meng; and Maedche, Alexander, "The Effect of Perceived Similarity in Dominance on Customer Self-Disclosure to Chatbots in Conversational Commerce" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
https://aisel.aisnet.org/ecis2020_rp/53
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