Yusuke Yamani, Shelby K Long, Tetsuya Sato, Abby L Braitman, Michael S Politowicz, Eric T Chancey
{"title":"多层次确认因素分析揭示了两种不同的人机信任结构。","authors":"Yusuke Yamani, Shelby K Long, Tetsuya Sato, Abby L Braitman, Michael S Politowicz, Eric T Chancey","doi":"10.1177/00187208241263774","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This work examined the relationship of the constructs measured by the trust scales developed by Chancey et al. (2017) and Jian et al. (2000) using a multilevel confirmatory factor analysis (CFA).</p><p><strong>Background: </strong>Modern theories of automation trust have been proposed based on data collected using trust scales. Chancey et al. (2017) adapted Madsen and Gregor's (2000) trust scale to align with Lee and See's (2004) human-automation trust framework. In contrast, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. However, it remains unclear whether these two scales measure the same construct.</p><p><strong>Method: </strong>We analyzed data collected from previous experiments to investigate the relationship between the two trust scales using a multilevel CFA.</p><p><strong>Results: </strong>Data provided evidence that Jian et al. (2000) and Chancey et al. (2017) automation trust scales are only weakly related. Trust and distrust are found to be distinct factors in Jian et al.'s (2000) scale, whereas performance, process, and purpose are distinct factors in Chancey et al.'s (2017) trust scale.</p><p><strong>Conclusion: </strong>The analysis suggested that the two scales purporting to measure human-automation trust are only weakly related.</p><p><strong>Application: </strong>Trust researchers and automation designers may consider using Chancey et al. (2017) and Jian et al. (2000) scales to capture different characteristics of human-automation trust.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"166-180"},"PeriodicalIF":2.9000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multilevel Confirmatory Factor Analysis Reveals Two Distinct Human-Automation Trust Constructs.\",\"authors\":\"Yusuke Yamani, Shelby K Long, Tetsuya Sato, Abby L Braitman, Michael S Politowicz, Eric T Chancey\",\"doi\":\"10.1177/00187208241263774\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This work examined the relationship of the constructs measured by the trust scales developed by Chancey et al. (2017) and Jian et al. (2000) using a multilevel confirmatory factor analysis (CFA).</p><p><strong>Background: </strong>Modern theories of automation trust have been proposed based on data collected using trust scales. Chancey et al. (2017) adapted Madsen and Gregor's (2000) trust scale to align with Lee and See's (2004) human-automation trust framework. In contrast, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. However, it remains unclear whether these two scales measure the same construct.</p><p><strong>Method: </strong>We analyzed data collected from previous experiments to investigate the relationship between the two trust scales using a multilevel CFA.</p><p><strong>Results: </strong>Data provided evidence that Jian et al. (2000) and Chancey et al. (2017) automation trust scales are only weakly related. Trust and distrust are found to be distinct factors in Jian et al.'s (2000) scale, whereas performance, process, and purpose are distinct factors in Chancey et al.'s (2017) trust scale.</p><p><strong>Conclusion: </strong>The analysis suggested that the two scales purporting to measure human-automation trust are only weakly related.</p><p><strong>Application: </strong>Trust researchers and automation designers may consider using Chancey et al. (2017) and Jian et al. 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Multilevel Confirmatory Factor Analysis Reveals Two Distinct Human-Automation Trust Constructs.
Objective: This work examined the relationship of the constructs measured by the trust scales developed by Chancey et al. (2017) and Jian et al. (2000) using a multilevel confirmatory factor analysis (CFA).
Background: Modern theories of automation trust have been proposed based on data collected using trust scales. Chancey et al. (2017) adapted Madsen and Gregor's (2000) trust scale to align with Lee and See's (2004) human-automation trust framework. In contrast, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. However, it remains unclear whether these two scales measure the same construct.
Method: We analyzed data collected from previous experiments to investigate the relationship between the two trust scales using a multilevel CFA.
Results: Data provided evidence that Jian et al. (2000) and Chancey et al. (2017) automation trust scales are only weakly related. Trust and distrust are found to be distinct factors in Jian et al.'s (2000) scale, whereas performance, process, and purpose are distinct factors in Chancey et al.'s (2017) trust scale.
Conclusion: The analysis suggested that the two scales purporting to measure human-automation trust are only weakly related.
Application: Trust researchers and automation designers may consider using Chancey et al. (2017) and Jian et al. (2000) scales to capture different characteristics of human-automation trust.
期刊介绍：
Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.
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