A Meta-Analysis of Factors Influencing the Development of Trust in Automation

Schaefer, Kristin E.; Chen, Jessie Y. C.; Szalma, James L.; Hancock, Peter A.

doi:10.1177/0018720816634228

Cited by 485 publications

(322 citation statements)

References 138 publications

(169 reference statements)

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“…In this study, we aim to transfer the mentioned findings to conditional automated driving and investigate if prior information determines the interaction with an automated driving system. Given the mentioned findings, automation that raises positive expectations of its performance (Schaefer, Chen, Szalma, & Hancock, 2016), signals high reliability (Mayer et al, 2006) and is experienced as reliable (Sauer et al, 2015) should increase subjective trust in automation. Based on this, we expect the following relationship:…”

Section: Prior Information Influences Trustmentioning

confidence: 88%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

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Section: Prior Information Influences Trustmentioning

confidence: 88%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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“…Previous studies have also identified personal influences on users' trust in and reliance on automated systems [15,18,52]. Although the human element is often highlighted as important in the literature, personal factors are still often overlooked or relegated to a minor focus in human-agent trust research [15,52].…”

Section: Personal Influences: Empirical Gaps In Human-agent Trustmentioning

confidence: 99%

“…The formation of human-agent trust, similar to that of human-human trust, is a dynamic process influenced by numerous factors, such as human factors, the performance of automated systems, and the environment [18,25,52]. Nevertheless, the majority of studies on human-agent interactions focus on how the reliability, dependability, and predictability of automated systems influence trust formation [1,3,7,10,32,33,40,48,50,55,61].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, investigating the effect of personal differences on the formation of human-agent trust is indispensible, especially for future designs of human-agent interaction. Since very few empirical studies focus on personal influences on the formation of human-agent trust [18,52], in this study, we conducted an online experiment to examine three types of personal influences on human-automation trust formation, including dispositional trust, decisionmaking style and attachment style. This study, to the best of our knowledge, is the first empirical study investigating personal influences on human-agent trust.…”

Section: Introductionmentioning

confidence: 99%

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Personal Influences on Dynamic Trust Formation in Human-Agent Interaction

Huang

Bashir

2017

Proceedings of the 5th International Conference on Human Agent Interaction

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The development of automated technologies in our daily life has transformed the role of human operators from a controller to a teammate who shares control with automated agents. However, this 'teammate' relationship between humans and automation raises an important but challenging research question regarding the formation of human-agent trust. Considering that the formation of human-agent trust is a dynamic and sophisticated process involving human factors, this study conducted a two-phase online experiment to examine personal influences on users' trust propensity and their trust formation in human-agent interactions. Our findings revealed distinctive personal influences on dispositional trust and the formation of human-agent trust at different stages. We found that users who exhibit higher trust propensities in humans also develop higher trust toward automated agents in initial stages. This study, as the first of its kind, not only fills the gap of knowledge about personal influences on human-agent trust, but also offers opportunities to enhance the future design of automated agent systems.

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“…Molloy & Parasuraman, 1996;Stanton, 2015). Despite the undeniable benefits of vehicle automation to improve road safety, deliver mobility to all, and reduce the number of accidents occurring on the road (Stanton & Marsden, 1996;), human-automation interaction is often overlooked throughout the design process in the pursuit of growing functional capabilities (Schaefer et al 2016) putting the safety of drivers and other road users at risk.…”

Section: Introductionmentioning

confidence: 99%

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

2018

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A Meta-Analysis of Factors Influencing the Development of Trust in Automation

Abstract: This work has important implications to the enhancement of current and future human-automation interaction, especially in high-risk or extreme performance environments.

Cited by 485 publications

References 138 publications

Introduction matters: Manipulating trust in automation and reliance in automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

Personal Influences on Dynamic Trust Formation in Human-Agent Interaction

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

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