Rethinking feminized service bots: user responses to abstract and gender-ambiguous chatbot avatars in a large-scale interaction study

Author:

Aumüller AnnaORCID,Winklbauer AndreasORCID,Schreibmaier Beatrice,Batinic Bernad,Mara MartinaORCID

Abstract

AbstractCompanies increasingly rely on chatbots to enable efficient and engaging communication with customers. Previous research has highlighted a trend towards female-gendered designs of customer service chatbots, adding to concerns about the reinforcement of outdated gender stereotypes in human-computer interactions. Against this background, the present study explores design alternatives to traditionally gendered chatbot avatars. In an online experiment, N = 1064 participants interacted with a bank service chatbot, where one half saw a gender-ambiguous anthropomorphic face as the chatbot’s default avatar, and the other half an abstract non-anthropomorphic icon. Contrary to earlier studies, which linked anthropomorphism to higher user acceptance, our manipulation of avatars did not significantly alter intentions to use the chatbot. After the interaction, participants could select their preferred avatar image from a set of six, including non-anthropomorphic icons (speech bubbles) and anthropomorphic faces (female, male, gender-ambiguous). While many adhered to their initially viewed image, a clear majority opted for abstract non-anthropomorphic icons. This overall preference was consistent across all user genders, although men were more likely than women to favor a traditionally female-looking avatar. Notably, less than a quarter of participants recognized the gender-ambiguous avatar as such. In accordance with traditional gender binaries, most identified it as either male or female. Those who perceived it as female reported higher intentions to use the chatbot. As a practical implication, our findings advocate for the adoption of more abstract and gender-neutral chatbot designs, as they not only help to avoid problematic stereotypes but also seem to align with customer preferences for non-gendered chatbot interactions.

Funder

Johannes Kepler University Linz

Publisher

Springer Science and Business Media LLC

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