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Revisiting human-machine trust: a replication study of Muir and Moray (1996) using a simulated pasteurizer plant task

This study aimed to replicate Muir and Moray that demonstrated operators' trust in automated machines developing from faith, then dependability, and lastly predictability. Following the procedure of Muir and Moray, we asked undergraduate participants to complete a training program in a simulate...

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Bibliographic Details
Published in:Ergonomics 2021-09, Vol.64 (9), p.1132-1145
Main Authors: Lee, Jieun, Yamani, Yusuke, Long, Shelby K., Unverricht, James, Itoh, Makoto
Format: Article
Language:English
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Summary:This study aimed to replicate Muir and Moray that demonstrated operators' trust in automated machines developing from faith, then dependability, and lastly predictability. Following the procedure of Muir and Moray, we asked undergraduate participants to complete a training program in a simulated pasteuriser plant and an experimental program including various errors in the pasteuriser. Results showed that the best predictor of overall trust was not faith but dependability, and that dependability consistently governed trust throughout the interaction with the pasteuriser. Thus, the obtained data patterns were inconsistent with those reported in Muir and Moray. We observed that operators in the current study used automatic control more frequently than manual control to successfully produce performance scores contrary to the operators in Muir and Moray. The results imply that dependability is a critical predictor of human-machine trust, which automation designer may focus on. More extensive future research using more modern automated technologies is necessary for understanding what factors control human-autonomy trust in modern ages. Practitioner Summary: The results suggest that dependability is a key factor that shapes human-machine trust across the time course of the trust development. This replication study suggests a new perspective for designing effective human-machine systems for untrained users who do not go through extensive training programs on automated systems.
ISSN:0014-0139
1366-5847
DOI:10.1080/00140139.2021.1909752