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.
Automation is used to complete a variety of everyday and professional tasks. Trust has been shown to be a critical factor that contributes to successful human-automation interaction. Modern theories of automation trust adapted theories of interpersonal trust and have been tested in a variety of domains. Specifically, a triadic model of trust, with performance, process, and purpose as factors, has emerged. From this theory, Chancey et al. (2017) adapted Madsen and Gregor’s (2000) trust scale to align with Lee and See’s (2004) trust framework. Conversely, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. This study aims to use questionnaire data from previous experiments to explore the relationship between the empirically driven Jian et al. trust scale and the theoretically driven Chancey et al. trust scale. We will perform a multilevel confirmatory factor analysis (CFA) to test the distinctiveness of the two trust measures, as well as their structures and the correlation between the measures. The findings of this work will help researchers understand the relationship between the two trust scales, assess if Jian et al.’s scale contains a three-factor structure, and provide more information about the psychological structure of automation trust.
Researchers have heavily debated the definition and role of trust in human behavior over the past few decades. As robots begin to be used more often, particularly in international military applications, understanding human-robot trust becomes increasingly important. The current study aims to investigate trust differences in robotic peacekeepers between Americans living in the United States, China, and Japan using a simulated environment. We predicted that trust in robots would differ as a function of culture. Results showed that Americans residing in Japan were significantly more trusting than Americans in the United States or China overall. Further, Americans living in America trusted robotic peacekeepers significantly more than Americans residing in China. This suggests that people who adopt a certain trust framework are those who have chosen to live abroad, but more research is needed to understand the differences between resident and expatriate Americans.
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