Measurement of human trust in a hybrid inspection system based on signal detection theory measures

Abstract Human trust plays an important role in influencing operator's strategies toward the use of automated systems. Therefore, a study was conducted to measure the effect of human trust in a hybrid inspection system given different types of errors (i.e., false alarms and misses). The study also looked at which of the four dimensions of trust (competence, predictability, reliability and faith) were the best predictors of overall trust. Results from the study reveal that trust is sensitive to the type of errors made by a system and suggest that subjective ratings of trust and the properties of the system can be used to predict the allocation of functions in hybrid inspection systems. Relevance to industry The study conducted here is applicable to inspection tasks in manufacturing and service industries. The results obtained indicate that subjective ratings of operators’ trust can be used as a basis for predicting and optimizing operator's allocation behavior and system performance. Furthermore, designers can use these results to help decide which functions to allocate to the human or to the system based on previous experiences and interaction with the system.