{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:12:59Z","timestamp":1781298779274,"version":"3.54.1"},"reference-count":159,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2016,3,22]],"date-time":"2016-03-22T00:00:00Z","timestamp":1458604800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Factors"],"published-print":{"date-parts":[[2016,5]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>We used meta-analysis to assess research concerning human trust in automation to understand the foundation upon which future autonomous systems can be built.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Trust is increasingly important in the growing need for synergistic human\u2013machine teaming. Thus, we expand on our previous meta-analytic foundation in the field of human\u2013robot interaction to include all of automation interaction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method:<\/jats:title>\n                    <jats:p>We used meta-analysis to assess trust in automation. Thirty studies provided 164 pairwise effect sizes, and 16 studies provided 63 correlational effect sizes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The overall effect size of all factors on trust development was \u1e21 = +0.48, and the correlational effect was [Formula: see text] \u2009= +0.34, each of which represented medium effects. Moderator effects were observed for the human-related ( \u1e21 \u2009= +0.49; [Formula: see text] = +0.16) and automation-related ( \u1e21 = +0.53; [Formula: see text] = +0.41) factors. Moderator effects specific to environmental factors proved insufficient in number to calculate at this time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Findings provide a quantitative representation of factors influencing the development of trust in automation as well as identify additional areas of needed empirical research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Application:<\/jats:title>\n                    <jats:p>This work has important implications to the enhancement of current and future human\u2013automation interaction, especially in high-risk or extreme performance environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0018720816634228","type":"journal-article","created":{"date-parts":[[2016,3,23]],"date-time":"2016-03-23T00:05:00Z","timestamp":1458691500000},"page":"377-400","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":654,"title":["A Meta-Analysis of Factors Influencing the Development of Trust in Automation"],"prefix":"10.1177","volume":"58","author":[{"given":"Kristin E.","family":"Schaefer","sequence":"first","affiliation":[{"name":"U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessie Y. 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