Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System. (June 2018)

• Record Type: Journal Article
• Authors: Balfe, Nora; Sharples, Sarah; Wilson, John R.
• Is Part Of: Human factors. Volume 60:Number 4(2018)
• Page Start: 477
• Page End: 495
• Publication Date: 2018-06
• Subjects: human-automation interaction -- supervisory control -- technology acceptance -- trust in automation -- ethnographic observations; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720818761256
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector. Background: Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting. Method: Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire. Results: The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling. Conclusion: Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation. Application: The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.