A Meta-Analysis Of Factors Influencing The Development Of Trust In Automation: Implications For Understanding Autonomy In Future Systems

Keywords

human-automation interaction; human-robot interaction; meta-analysis; trust

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 g = +0.48, and the correlational effect was r = +0.34, each of which represented medium effects. Moderator effects were observed for the human-related (g = +0.49; r = +0.16) and automation-related (g = +0.53; r = +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.

Publication Date

5-1-2016

Publication Title

Human Factors

Volume

58

Issue

3

Number of Pages

377-400

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/0018720816634228

Socpus ID

84962920800 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84962920800

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