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Human–Automation Trust to Technologies for Naïve Users Amidst and Following the COVID-19 Pandemic
Objective and background Trust is a critical factor that influences the success or failure of human–automation interaction in a variety of professional domains such as transportation, military, and healthcare. The unprecedented COVID-19 crisis will likely accelerate the implementation of automation...
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Published in: | Human Factors 2020-11, Vol.62 (7), p.1087-1094 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Objective and background
Trust is a critical factor that influences the success or failure of human–automation interaction in a variety of professional domains such as transportation, military, and healthcare. The unprecedented COVID-19 crisis will likely accelerate the implementation of automation and create unique problems involving human–automation trust for naïve users of automated technologies in the future.
Method
We briefly review factors that can influence the development of human–automation trust amidst and following the COVID-19 pandemic. We focus on two theories on human-automation trust and how naïve users develop and maintain their trust in unfamiliar technologies.
Results
The current review identifies user workload and perceived risk as critical factors that will impact human–automation trust during the COVID-19 pandemic. Both theories predict that it is important for naïve users to accumulate and analyze behavioral evidence of automated technologies to maintain appropriate trust levels as the pandemic progresses.
Conclusion and application
Theories of human–automation trust inform trajectories of trust development toward unfamiliar technologies for naïve users. In application, manufacturers and distributers should focus on communicating system information effectively to retain users who may be “forced” to use unfamiliar technologies during the COVID-19 pandemic. |
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ISSN: | 0018-7208 1547-8181 |
DOI: | 10.1177/0018720820948981 |