Date of Award

December 2021

Document Type


Degree Name

Master of Science (MS)


Human Factors Psychology

Committee Member

Rich Pak


Technologies such as voiced automation can aid older adults aging in place by assisting with basic home and health tasks in daily routines. However, currently available voice assistants have a common design - they are vastly represented as young and female. Prior work has shown that humans apply stereotypes to human-computer interactions similarly to human-human interactions. When these stereotypes are activated, users may lose trust or confidence in the device or stop using it all together. The purpose of this study was to investigate if users can detect age and gender cues of voiced automation and to understand the extent to which gender, age, and reliability elicit stereotypic responses which were assessed using history-based trust. A series of health-related voice automation scenarios presented users with voice assistants varying in gender, age, and reliability. Results showed differences in age and gender perceptions across participant age groups but no differences for overall trust. A three-way interaction showed that when voiced automation reliability was low, participants rated the young female voice assistant as significantly more trustworthy than all other voice assistants. This work contributes to our understanding of how anthropomorphic characteristics like age and gender in emerging technologies can elicit varied trust responses from younger and older adults.



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