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Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting

·2019·Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Abstract

This lecture discusses the validation of a new scale, the Trust of Automated Systems Test (TOAST). As we increasingly rely on automated systems to perform tasks, it becomes ever more important to understand the ways in which real operators will use these systems. Trust is a critical determinant of this use. Previous efforts to translate trust, typically an interpersonal human experience, into the domain of artificial cognition and inanimate objects, has met with mixed success. Existing scales tend to anthropomorphize systems and attribute intent to relatively simplistic decision processes. This anthropomorphization has led to operator dissatisfaction with these scales and, consequently, poor data quality. In two studies, I present the validation effort for a scale that avoids anthropomorphizing while still assessing the theoretically-driven underlying dimensions of trust formation. Confirmatory Factor Analyses provide evidence that the TOAST, a nine-item scale measuring operator Understanding of the system and belief in its Efficacy is a valid metric of operator trust of both military and civilian systems.

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Metadata

Venue
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2019-11
Volume
63
Issue
1
Pages
2169-2169
ISSN
  • 0163-5182 (ISSN-L)
  • 1071-1813
  • 2169-5067
OpenAlex
W4237513721
Cited by
1,432
References
0
FWCI
185.03
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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CoChat. (2026). Citation check: Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting. https://app.cochat.ai/papers/10.1177/1071181319631171
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Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting. (2019). Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/1071181319631171

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.