No abstract

With the advancement of technology, automation has rapidly embedded itself in many parts of daily function. As a result, it is imperative to understand the processes in which individuals use and develop trust with automation. Specifically, how this relationship is formed and how it evolves over time. Previous research has theorized that the frequency and the positivity of interaction with automated systems is a prominent influencer in the creation of trust. In the present study, we examined subjective trust ratings when participants performed a pattern recognition task with an autonomous agent which varied in positivity and number of interactions. Results showed that positivity amongst other parameters were significant predictors of subjective trust when analyzed using linear mixed models.

Computer agents are frequently anthropomorphized, giving them appearances and responses similar to humans. Research has demonstrated that users tend to apply social norms and expectations to such computer agents, and that people interact with computer agents in a similar fashion as they would another human. Perceived expertise has been shown to affect trust in human-human relationships, but the literature investigating how this influences trust in computer agents is limited. The current study investigated the effect of computer agent perceived level of expertise and recommendation reliability on subjective (rated) and objective (compliance) trust during a pattern recognition task. Reliability of agent recommendations had a strong effect on both subjective and objective trust. Expert agents started with higher subjective trust, but showed less trust repair. Agent expertise had little impact on objective trust resiliency or repair.

No abstract