Tamera Schneider (Advisor), Gary Burns (Committee Member), Gene Alarcon (Committee Member)
Master of Science (MS)
Few studies have examined how propensity to trust in automation influences trust behaviors, those which indicate users are relying on automation. Of the published studies, there are inconsistencies in how propensity to trust automation is conceptualized and thus measured. Research on attitudes and intentions has discerned that reliability and validity of measures can be increased by using more direct and specific language, which reduces ambiguity and increases the ability to predict behavior. This study examined how traditional measures of propensity to trust automation could be adapted to predict whether automation is deemed as trustworthy (perceived trustworthiness) and whether people behave in a trusting manner when interacting with automation (behavioral trust). Participants (N = 55) completed three propensity to trust in automation surveys including Propensity to Trust in Technology, an adapted version, and the Complacency-Potential Rating Scale. The Propensity to Trust in Technology scale was adapted by replacing “technology” with “automated agent” as the referent. Participants played a modified investor/dictator game, where people teamed with a NAO robot. Betting behaviors were used to measure behavioral trust. This study demonstrated that compared to generally-worded measures, more specifically-worded measures of propensity to trust automation are more reliable and better predictors of perceived trustworthiness and behavioral trust. An adapted propensity to trust technology scale was the only significant predictor of both perceived trustworthiness of the automation and the trusting behaviors of participants. By decreasing the ambiguity of the referent in the adapted propensity to trust automation scale, the reliability and predictive validity was increased.
Department or Program
Department of Psychology
Year Degree Awarded
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