Date of Award
Master of Science (MS)
Human Factors Psychology
Previous research has shown that good automation etiquette can yield positive effects on user performance, trust, satisfaction, and motivation. Automation etiquette is especially influential in personified technologies – users have increased etiquette expectations from technology that has human characteristics. Designers deliberately integrate etiquette into personified technologies to account for users’ anthropomorphization and meet user needs. The current study examined the impact of etiquette in non-personified technologies. The study aimed to demonstrate that automation etiquette also affects performance, trust, perceived workload, and motivation in technologies that possess little to no human characteristics. The study used a computer-based automation task to examine good and bad etiquette models and different domain-based perceived task-importance, or “criticality” levels (between-subjects) that contained various stages of automation and automation reliability levels (within-subjects). The study found that bad etiquette automation produced better performance in certain conditions. Confirming previous research, we found that users trust good etiquette automation more than bad etiquette automation in some trust categories. This study provides evidence that automation complexity correlates with automation etiquette’s impact – as automation complexity increases, so does automation etiquette’s impact on performance and in some cases trust. We found that bad automation etiquette can increase user’s subjective workload. Last, we confirmed that our domain-based task criticality manipulation was effective. Future research should examine additional domains, tasks, etiquette delivery mechanisms, and etiquette scales coupled with varied degrees of automation complexity to better understand etiquette’s role in human-automation interaction.
Guyton, Zachary Joseph, "The Impact of Automation Etiquette on User Performance and Trust in Non-Personified Technology" (2021). All Theses. 3516.