In human–robot interaction studies, trust is often defined as a process whereby a trustor makes themselves vulnerable to a trustee. The role of vulnerability however is often overlooked in this process but could play an important role in the gaining and maintenance of trust between users and robots. To better understand how vulnerability affects human–robot trust, we first reviewed the literature to create a conceptual model of vulnerability with four vulnerability categories. We then performed a meta-analysis, first to check the overall contribution of the variables included on trust. The results showed that overall, the variables investigated in our sample of studies have a positive impact on trust. We then conducted two multilevel moderator analysis to assess the effect of vulnerability on trust, including: (1) an intercept model that considers the relationship between our vulnerability categories and (2) a non-intercept model that treats each vulnerability category as an independent predictor. Only model 2 was significant, suggesting that to build trust effectively, research should focus on improving robot performance in situations where the users are unsure how reliable the robot will be. As our vulnerability variable is derived from studies of human–robot interaction and researcher reflections about the different risks involved, we relate our findings to these domains and make suggestions for future research avenues.

In human-robot interaction, trust is affected by human, robot, and environmental factors. In the proposed research, we consider each of these factors, by focusing on the contribution of robot theory of mind (ToM), human visual perspective taking (a concept related to ToM), and environmental complexity, to the development and maintenance of human-robot trust. To do so, our experiment combines a psychological assessment of visual perspective taking (the Director Task), with a trust-based robot navigation task. Using the AREA model of Responsible Research and Innovation (RRI), we also highlight the implications of our experiment, in the context of trustworthy robotics and human-robot collaboration. We round off the article with a theoretical and research development synopses related to robot ToM and trust, and future work to be conducted in this area.

This study implemented a Delphi Method, a systematic technique which relies on a panel of experts to achieve consensus, to evaluate which questionnaire items would be the most relevant for developing a new Propensity to Trust scale. Two surveys were administered to academic lecturers, professors and Ph.D. candidates specialising in the fields of either individual differences, human robot interaction, or occupational psychology. Results from 28 experts produced 33 final questionnaire items that were deemed relevant for evaluating trust. We discuss the importance of content validity when implementing scales, while we also emphasise the need for more documented scale development processes in psychology. Furthermore, we propose that the Delphi approach could be utilised as an effective and economical method for achieving content validity while also providing more transparency of a scale’s creation.