Investigating the effects of sex-typing and sex-role stereotypes on user experience in the context of fintech services

Abstract

Front-desk financial service jobs are increasingly being replaced by artificial intelligence conversational agents, or chatbots. However, this presents the challenge of building customer trust and maintaining customer satisfaction, which is particularly important in financial services. Research under the Computers as Social Actors paradigm has shown that people apply social rules to human-computer interactions (e.g., such as sex-role stereotyping, similarity-attraction, consistency-attraction), which can help us understand how customers evaluate chatbots. The present study addresses this issue by conducting critical tests of social rules that apply to human-agent interaction in a financial advice chatbot, focusing particularly on social rules pertaining to sex and gender. Study 1 investigates if users apply a sex-role stereotype to a fintech chatbot, or if they apply the similarity-attraction rule such that they prefer fintech chatbot agents that are represented by the same sex category as themselves. Results of Study 1 show that users generally have better evaluations of the female agent as compared to a male agent. Study 2 extends the findings in Study 1 by additionally manipulating the conversation style of the fintech agent to express either masculine traits or feminine traits. Results of Study 2 further reinforces those of Study 1 by showing that users generally prefer a feminine conversation style. Theoretical and practical implications are discussed.