{'fraction_non_alphanumeric': 0.04013840830449827, 'fraction_numerical': 0.01730103806228374, 'mean_word_length': 5.17948717948718, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Meta-Analysis of Factors Influencing the Development of Trust in Automation

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.