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 = +0.34, each of which represented medium effects. Moderator effects were observed for the human-related (ḡ = +0.49; = +0.16) and automation-related (ḡ = +0.53; = +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.
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