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

Published

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Hoff, KA; Bashir, M

Published Date

  • May 2015

Published In

Volume / Issue

  • 57 / 3

Start / End Page

  • 407 - 434

PubMed ID

  • 25875432

Pubmed Central ID

  • 25875432

International Standard Serial Number (ISSN)

  • 0018-7208

Digital Object Identifier (DOI)

  • 10.1177/0018720814547570

Language

  • eng

Conference Location

  • United States