Scared to Trust? - Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Kraus, Johannes; Scholz, David; Messner, Eva-Maria; Messner, Matthias · 2020 · OpenAlex

DOI: 10.3389/fpsyg.2019.02917

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Summary

This study investigates the psychological mechanisms underlying the formation of trust in highly automated driving systems, specifically examining how personality traits and emotional states influence initial trust calibration. The authors argue that appropriate use of automation requires calibrated trust, avoiding both misuse and disuse. While prior research has established the importance of trust, there is limited understanding of how individual differences in personality and anxiety affect trust formation when users are first introduced to unfamiliar automated technology. The research aims to determine if depressiveness, negative self-evaluations (self-esteem, self-efficacy), and locus of control predict trust in automation directly or indirectly through the mediation of state anxiety. The researchers conducted a driving simulator study with a final sample of 47 participants recruited from Ulm University. The experimental design was correlational. Before interacting with the automated system, participants completed questionnaires measuring depressiveness, self-esteem, self-efficacy, locus of control, and state anxiety. Positive and negative affect were also measured as control variables. Participants then engaged in a simulated drive using the automated system for approximately five minutes. Trust in the system was measured immediately after this interaction. The study hypothesized that personality traits would influence the experience of anxiety upon introduction to the new technology, which in turn would mediate the relationship between those traits and the resulting level of trust in the automation. The results indicated that trust in the automated driving system was significantly predicted by state anxiety, self-esteem, and self-efficacy. Specifically, higher levels of self-esteem and self-efficacy were associated with higher trust, while higher state anxiety was associated with lower trust. Mediation analysis revealed that the relationships between self-esteem and trust, as well as self-efficacy and trust, were mediated by state anxiety; that is, these personality traits influenced trust primarily by reducing the anxiety experienced when encountering the new system. For depressiveness, no direct significant relationship with trust was found, but an indirect effect through anxiety was supported, suggesting that depressiveness increases anxiety, which subsequently lowers trust. Locus of control showed no significant association with trust in automation. These findings highlight the critical role of individual differences in negative self-evaluations and anxiety in shaping initial trust in automated driving systems. The study suggests that anxiety acts as a key psychological mechanism linking personality traits to trust formation. This implies that users with lower self-esteem or self-efficacy may experience higher anxiety when introduced to automation, leading to reduced trust and potentially inefficient system use. The authors conclude that future research and the design of automated technology should account for these individual differences to facilitate better trust calibration. Understanding these psychological processes can inform the development of personalized interfaces or training strategies that mitigate anxiety and promote appropriate trust levels in automated driving systems.

Key finding

Trust in automated driving systems is significantly predicted by state anxiety and negative self-evaluations, with anxiety mediating the effects of self-esteem and self-efficacy on trust.

Methodology

simulator

Sample size: 47

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