A logistic regression model for predicting trust in an inspection system

Inspection plays a very important role to ensure the product reliability demanded by modern consumers. Traditionally, objective performance measures such as speed and accuracy are often used in an inspection system. However, with more and more inspection process being automated, human inspector's role becomes more like a supervisor. Studies of supervisory control have confirmed that trust in automation, a subjective measure, has great impact on system performance. If an operator trusts a system, he or she tends to agree with the system more often. On the other hand, if an operator distrusts a system, he or she might override the system more. This study aims to investigate the relationship between inspector's trust on the system and system performance. A logistic regression model was developed to predict trust based on data collected from an inspection task. The findings from, the research will have the potential to better design inspection systems in the future.