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Theoretical considerations and development of a questionnaire to measure trust in automation
Abstract
The increasing number of interactions with automated systems has sparked the interest of researchers in trust in automation because it predicts not only whether but also how an operator interacts with an automation. In this work, a theoretical model of trust in automation is established and the development and evaluation of a corresponding questionnaire (Trust in Automation, TiA) are described. Building on the model of organizational trust by Mayer, Davis, and Schoorman (1995) and the theoretical account by Lee and See (2004), a model for trust in automation containing six underlying dimensions was established. Following a deductive approach, an initial set of 57 items was generated. In a first online study, these items were analyzed and based on the criteria item difficulty, standard deviation, item-total correlation, internal consistency, overlap with other items in content, and response quote, 40 items were eliminated and two scales were merged, leaving six scales (Reliability/Competence, Understandability/Predictability, Propensity to Trust, Intention of Developers, Familiarity, and Trust in Automation) containing a total of 19 items. The internal structure of the resulting questionnaire was analyzed in a subsequent second online study by means of an exploratory factor analysis. The results show sufficient preliminary evidence for the proposed factor structure and demonstrate that further pursuit of the model is reasonable but certain revisions may be necessary. The calculated omega coefficients indicated good to excellent reliability for all scales. The results also provide evidence for the questionnaire’s criterion validity: Consistent with the expectations, an unreliable automated driving system received lower trust ratings as a reliably functioning system. In a subsequent empirical driving simulator study, trust ratings could predict reliance on an automated driving system and monitoring in form of gaze behavior. Possible steps for revisions are discussed and recommendations for the application of the questionnaire are given.
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Even if an AI system is trusted, reliance must not necessarily follow.
“Even if an AI system is trusted, reliance must not necessarily follow (Kirlik, 1993; Körber, 2018)”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Körber, 2018
Whether trust translates into reliance is more nuanced than often assumed and depends on an interaction between the operator, the automation, and the situation.
“Whether trust translates into reliance is more nuanced than often assumed and depends on an interaction between the operator, the automation, and the situation (Körber, 2018).”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Körber, 2018
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Metadata
- Authors
- Körber, M.
- Publisher
- Center for Open Science
- Published
- 2018-03-07
- Cited by
- 71
- References
- 16
- Type
- Preprint
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