The Five-Factor Perceived Shared Mental Model Scale: A Consolidation of Items Across the Contemporary Literature

Literature on Shared Mental Models (SMMs) has been burgeoning in recent years and this has provided increasingly detailed insight and evidence into the importance of SMMs within specific contexts. However, because past research predominantly focused on SMM structure as measured by diverse, context-dependent measures, a consolidated multi-dimensional measure of perceived SMMs that can be used across diverse team contexts is currently lacking. Furthermore, different conceptualizations of the dimensionality of SMMs exist, which further impedes the comparison between studies. These key limitations might hinder future development in the SMM literature. We argue that the field of SMMs has now matured enough that it is possible to take a deductive approach and evaluate the prior studies in order to refine the key SMMs dimensions, operationalizations, and measurement. Hence, we take a three-stage approach to consolidate existing literature scale-based measures of SMMs, using four samples. Ultimately, this leads to a 20-item five-dimensional scale (i.e., equipment, execution, interaction, composition, and temporal SMMs) – the Five Factor Perceived Shared Mental Model Scale (5-PSMMS). Our scale provides scholars with a tool which enables the measurement, and comparison, of SMMs across diverse team contexts. It offers practitioners the option to more straightforwardly assess perceived SMMs in their teams, allowing the identification of challenges in their teams and facilitating the design of appropriate interventions.

Watch For Failing Objects: What Inappropriate Compliance Reveals About Shared Mental Models In Autonomous Cars

This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.

Understanding Knife Crime and Trust in Police with Young People in East London

We explore young people’s experiences and perceptions of knife crime, and we compare these to the understanding of police experts, to explore the perceptions shaping trust in the police and policing. We carry out an experience sampling survey deployed using a mobile application reflecting on safety and knife crime, to understand young people’s daily lived experiences. We then use the mental models approach to interview young people and police experts and construct a shared mental model which identifies mismatches between the two groups and key areas of discord related to breakdown of trust and communication. We identify gaps, misconceptions and expectations for re-establishing trust and propose strategies to tackle knife crime and improve trust between young people and the police.

Research on the influence of relation embeddedness on innovation performance of manufacturing supply chain alliances using expert fuzzy rule –intermediary role of shared mental model

The innovation and development of manufacturing supply chain alliance is an important way for enterprises to meet the increasing market demand and maintain the competitive advantage. From the perspective of embeddedness, the research model of relation embeddedness on innovation performance of manufacturing supply chain was constructed based on AMOS. Shared mental model was selected as intermediary variable to study the influence of relation embeddedness, shared mental model and innovation performance of manufacturing supply chain alliances. Expert fuzzy rule based system is utilized for measuring the performance of manufacturing supply chain alliances. The conclusion shows that relation embeddedness is significantly positive shared mental model and innovation performance. Shared mental model is positively affects alliance innovation performance and plays a part of intermediary role between relational embedding and alliance innovation performance. Practice implicates that enhance the level of relation embeddedness can promote the formation of shared mental model and improve the innovation performance of manufacturing supply chain alliance.