Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Abstract

Interdisciplinary collaboration frequently comes into play when existing problems cannot be solved by one discipline alone. However, the interlocking of contributions from different disciplines is by no means trivial. This exploratory study examines one foundation of successful teamwork, namely shared mental models. To this end, we compared the contents of mental models between members of different but interdependent disciplines who collaboratively solve knowledge-intensive, creative tasks. Five automation and five mechanical engineers were recruited from a company that produces packaging machines. In semi-structured interviews, participants reported their approach to evaluating the process behavior of a packaging machine, and their mental models were represented in concept maps. Quantitative analyses revealed that the maps of automation engineers were smaller than those of mechanical engineers. In qualitative analyses, the focus on different levels of abstraction and on contents from the two disciplines was examined. Automation engineers represented a large proportion of rather abstract machine functions, whereas mechanical engineers additionally represented the physical implementation of these functions. The disciplinary focus also differed in the sense that automation engineers mainly attended to automated machine processes, while mechanical engineers attended to both mechanical and automated processes. Overall, automation engineers' focus was narrower than that of mechanical engineers. We explain these results by considering typical tasks and reasoning processes in both disciplines, and discuss how shared mental models can aid the integration of different disciplinary perspectives, for instance, during Systems Engineering.