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

Hum Factors. 2016 May;58(3):377-400. doi: 10.1177/0018720816634228. Epub 2016 Mar 22.

Abstract

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.

Keywords: human–automation interaction; human–robot interaction; meta-analysis; trust.

Publication types

  • Meta-Analysis
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Automation*
  • Humans
  • Man-Machine Systems*
  • Trust*