Gender identity and influence in human-machine communication:A mixed-methods exploration

Abstract

The advancement of conversational technologies stimulates new research agenda on the patterns, norms, and social impacts of human-machine communication (HMC) as a novel process. Conversational agents (CAs), a prevalent example of machines that communicate with users directly, are usually depicted as females in assisting roles. This study intends to explore empirical evidence of how “gendered” technologies might influence HMC and potentially reinforce gender stereotyping in human-human communication. We applied a mixed-methods approach to explore users' gender-related responses and evaluations in the interaction with CAs. First, we observed unrestricted interactions between 36 human participants and Amazon Alexa in a laboratory and qualitatively analyzed the transcripts to detect gendered communication cues. We then conducted a 2 × 3 (participant gender: female vs. male; CA gender: female vs. male vs. neutral) online experiment where 250 participants interacted with a customized chatbot created by the researcher. Results showed participants’ different emotions/tones, engagement, (non)accommodation, as well as credibility, attraction, and likeability evaluations between human-CA gender pairs.