Kristin E Schaefer1, Jessie Y C Chen2, James L Szalma3, P A Hancock4. 1. U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland kristin.e.schaefer2.ctr@mail.mil. 2. U.S. Army Research Laboratory, Orlando, Florida. 3. U.S. Army Research Laboratory, Aberdeen Proving Ground, MarylandU.S. Army Research Laboratory, Orlando, FloridaUniversity of Central Florida, Orlando. 4. University of Central Florida, Orlando.
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.
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.
Authors: Katharine E Henry; Roy Adams; Cassandra Parent; Hossein Soleimani; Anirudh Sridharan; Lauren Johnson; David N Hager; Sara E Cosgrove; Andrew Markowski; Eili Y Klein; Edward S Chen; Mustapha O Saheed; Maureen Henley; Sheila Miranda; Katrina Houston; Robert C Linton; Anushree R Ahluwalia; Albert W Wu; Suchi Saria Journal: Nat Med Date: 2022-07-21 Impact factor: 87.241