A Meta-Analysis of Factors Influencing the Development of Trust in Automation
By Kristin E. Schaefer, Jessie Y.C. Chen, James L. Szalma, Peter A. Hancock. Hum. Factors, 2016
588 citations recorded by the source corpus.
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 r ¯ = +0.34, each of which represented medium effects. Moderator effects were observed for the human-related (ḡ = +0.49; r ¯ = +0.16) and automation-related (ḡ = +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.
Subfields: Experimental and Cognitive Psychology, Information Systems and Management, Social Psychology
View Paper: https://www.semanticscholar.org/paper/629bf1f076f8d5bc203c573d4ba1dad5bb6743cf
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Citations in the corpus
- Trust in Artificial Intelligence: Meta-Analytic Findings (2021, methodology): “Almost half of these arti- for evaluation under more stringent criteria, as Schaefer et al., 2016 45 from the most recent meta- analysis on trust Hancock et al., 2020 total of 715 articles remained, 640 potentially relevant articles from the initial search and 75 articles from previous meta-…”
- From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction (2018, background): “…factors surrounding the nature of the trust violation, automation research has shown that context is critically important in how diff erent users perceive and react to automation failures (Hoff and Bashir 2015; Hoffman et al. 2013; Mittu et al. 2016; Pak et al. 2016; Schaefer et al. 2016).”
- Trustworthy artificial intelligence (2020, background): “Of particular concern in relation to this principle is research on trust in autonomous systems such as autonomous vehicles (Schaefer et al. 2016; Stormont 2008), as well as research on adjustable autonomy, which refers to agents dynamically changing their autonomy and transferring it to other entiti”