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Ph.D. Defense: Md Abdullah Al Fahim
November 23, 2022 @ 1:30 pm - 2:30 pm EST
Title: The Effect of Experienced Emotions on User’s Trust in Automation
Ph.D. Candidate: Md Abdullah Al Fahim
Major Advisor: Dr. Mohammad Maifi Hasan Khan
Associate Advisors: Dr. Derek Aguiar, Dr. Dong-Guk Shin
Date/Time: Wednesday, November 23rd, 2022, 01:30 pm
Location: WebEx
Meeting link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mddf9a7e141fd5ab0104e56b2a4a6ae39
Meeting number: 2627 062 2520
Password: VmwNkQNv338
Abstract
Technological advances are increasingly pushing humans to team up with and rely on automation to accomplish various complex tasks more efficiently. In such human-automation interaction scenarios, trust in automation is noted to be one of the most important factors that governs users’ dependence on such systems. Therefore, ensuring an appropriate level of trust and usage by avoiding misuse (i.e., over-trust) and disuse (i.e., under-trust) is of extreme importance. Without an appropriate level of trust, human-automation collaborations are likely to fail and lead to suboptimal performance.
Recent work in the human-automation trust literature acknowledges the influence of emotions on users’ trust in automation among other factors. However, what emotions are related to human-automation interaction, how these emotions are elicited during the interaction, and how these experienced emotions affect users’ trust in the automation and subsequent usage behavior is far from clear. Guided by prior work in human-human trust, human-automation trust, and emotions, this dissertation investigates the relationship between emotion and human-automation trust interaction.
In the first part of the thesis, we investigate how different factors such as reliability, risk, and anthropomorphism influence users’ experienced emotions. We also identify the emotions that are relevant to human-automation interaction and establish the mediating effect of these emotions on system attributes and different trustworthiness perception relationships.
Next, we investigate whether we can influence users’ experienced emotions during interaction with automation by providing affective feedback after critical system events (e.g., errors, warnings). Our results confirmed the success of affective feedback at influencing users’ experienced emotions and subsequently influencing users’ trust in automation.
Finally, to mitigate the potential negative effect of emotions on users’ usage of automation, we investigate the effect of raising emotional awareness on users’ emotions and trust by providing information regarding emotional expectations. The insights from this dissertation can be utilized to develop user-aware intelligent systems, induce and sustain an appropriate emotional state through user interfaces, and facilitate an appropriate level of trust.
