What's Driving Me? Exploration and Validation of a Hierarchical Personality Model for Trust in Automated Driving

OBJECTIVE

This paper presents a comprehensive investigation of personality traits related to trust in automated vehicles. A hierarchical personality model based on Mowen's (2000) 3M model is explored in a first and replicated in a second study.

BACKGROUND

Trust in automation is established in a complex psychological process involving user-, system- and situation-related variables. In this process, personality traits have been viewed as an important source of variance.

METHOD

Dispositional variables on three levels were included in an exploratory, hierarchical personality model (full model) of dynamic learned trust in automation, which was refined on the basis of structural equation modeling carried out in Study 1 (final model). Study 2 replicated the final model in an independent sample.

RESULTS

In both studies, the personality model showed a good fit and explained a large proportion of variance in trust in automation. The combined evidence supports the role of extraversion, neuroticism, and self-esteem at the elemental level; affinity for technology and dispositional interpersonal trust at the situational level; and propensity to trust in automation and a priori acceptability of automated driving at the surface level in the prediction of trust in automation.

CONCLUSION

Findings confirm that personality plays a substantial role in trust formation and provide evidence of the involvement of user dispositions not previously investigated in relation to trust in automation: self-esteem, dispositional interpersonal trust, and affinity for technology.

APPLICATION

Implications for personalization of information campaigns, driver training, and user interfaces for trust calibration in automated driving are discussed.

The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent

We use the Situation awareness-based Agent Transparency model as a framework to design a user interface to support agent transparency. Participants were instructed to supervise an autonomous robotic agent as it traversed simulated urban environments. During this task, participants were exposed to one of three levels of information used to support agent transparency in the interface display. Our findings suggest that providing agent transparency information allows operators to properly calibrate trust without excess workload. Though, increased agent transparency information did not support operator situation awareness.

A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Understanding Autonomy in Future Systems

OBJECTIVE

We used meta-analysis to assess research concerning human trust in automation to understand the foundation upon which future autonomous systems can be built.

BACKGROUND

Trust is increasingly important in the growing need for synergistic human-machine teaming. Thus, we expand on our previous meta-analytic foundation in the field of human-robot interaction to include all of automation interaction.

METHOD

We used meta-analysis to assess trust in automation. Thirty studies provided 164 pairwise effect sizes, and 16 studies provided 63 correlational effect sizes.

RESULTS

The overall effect size of all factors on trust development was ḡ = +0.48, and the correlational effect was [Formula: see text]  = +0.34, each of which represented medium effects. Moderator effects were observed for the human-related (ḡ  = +0.49; [Formula: see text] = +0.16) and automation-related (ḡ = +0.53; [Formula: see text] = +0.41) factors. Moderator effects specific to environmental factors proved insufficient in number to calculate at this time.

CONCLUSION

Findings provide a quantitative representation of factors influencing the development of trust in automation as well as identify additional areas of needed empirical research.

APPLICATION

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

Trust in automation: integrating empirical evidence on factors that influence trust

OBJECTIVE

We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge.

BACKGROUND

Much of the existing research on factors that guide human-automation interaction is centered around trust, a variable that often determines the willingness of human operators to rely on automation. Studies have utilized a variety of different automated systems in diverse experimental paradigms to identify factors that impact operators' trust.

METHOD

We performed a systematic review of empirical research on trust in automation from January 2002 to June 2013. Papers were deemed eligible only if they reported the results of a human-subjects experiment in which humans interacted with an automated system in order to achieve a goal. Additionally, a relationship between trust (or a trust-related behavior) and another variable had to be measured. All together, 101 total papers, containing 127 eligible studies, were included in the review.

RESULTS

Our analysis revealed three layers of variability in human-automation trust (dispositional trust, situational trust, and learned trust), which we organize into a model. We propose design recommendations for creating trustworthy automation and identify environmental conditions that can affect the strength of the relationship between trust and reliance. Future research directions are also discussed for each layer of trust.

CONCLUSION

Our three-layered trust model provides a new lens for conceptualizing the variability of trust in automation. Its structure can be applied to help guide future research and develop training interventions and design procedures that encourage appropriate trust.