Mental models are a widely accepted source why decision-makers fail to make effective decisions. The concept of mental models was first introduced in the mental model theory (Johnson-Laird, 1983), defining mental models as a medium for mental representation by manipulating mental objects (making decisions) to solve a problem at hand. (Senge, 1990) makes use of the mental model concept in his research on “the learning organization”, where he defines mental models as “deeply ingrained assumptions, generalizations, or even pictures or image that influence how we understand the world and how we take action. Very often we are not consciously aware of our mental models or the effect they have on our behavior” (Senge, 1990), p.8.
He determines mental models as one of five disciplines that can create an effective learning organization, Figure 1. Those five disciplines can only facilitate a learning organization, and thus optimal decision-making in a team, when they are practiced interchangeably. According to Senge the disciplines can be described as follows (Senge, 1990):
- Personal mastery helps team members to become aware of their personal vision and align their personal vision to the vision of the organization.
- Shared vision is created by establishing shared values and objectives. This is a process involving the whole organization, since each team member should support the shared vision.
- Team learning emphasizes the importance of dialogue and how to structure effective meetings. The team should be aware of issues like diversity and team settings that may affect team dynamics.
- Effective use of mental models can be formed by altering mental models, i.e. being aware of your personal mental models and the ones of your team members. If people are aware of the mental models present in a team it creates a good basis for teamwork. This is because team members understand the underlying assumptions and views of statements made in a discussion.
- System thinking describes the four different ways to look at a problem. Therefore, Senge established “the four levels of system thinking”, a similar method is also defined by (Kambiz Maani & Cavana, 2007), Figure 2.
“The four levels of system thinking”, namely events, pattern of behavior, systematic structures and mental models, are related. In business and policy studies decisions are shaped in such a way – events represent the most visible way of reality, whereas mental models display the most hidden and deeply ingrained assumptions and motivations of a person. In more detail, on the surface the events are the actions/decisions that take place and can be observed, once similar events are observed a pattern of behavior is established that encourages decision-makers to make recurring decisions. The systematic structures are the forces and dynamics that influence the pattern of behavior in the system. That means, the systematic structures reflect how the pattern relate to one another in a bigger system, understanding the interrelationships and differences of various factors that affect the outcome of the decision. Mental models are located on the lowest (deepest) level and are shaped through all the experiences a person makes. With different words, mental models are generated by the systematic structures the person constructed and validated with all past decisions (Kambiz Maani, Fan, & Einstein, 2008). Senge discusses several ways to surface mental models, e.g. the “Ladder of Inference”, the “Left-Hand Column”, scenario planning, double-loop learning and causal loop diagrams (Senge, 1990). Many other researchers also employ these techniques to alter mental models (Chermack, 2004; Diehl & Sterman, 1993; Kim, MacDonald, & Andersen, 2013; Kambiz Maani et al., 2008; Kambiz Maani & Maharaj, 2004). These methods can be applied to uncover the assumptions, norms and views of individuals in a team that hinder optimal decision-making.
Especially in team decisions we can adopt the concept of “collective mental models”. For example, (Kim et al., 2013) introduce a framework that utilizes double-loop learning in order to identify and build collective mental models and facilitate the accuracy of mental models. Simulation modelling is applied to strengthen management team decisions in a dynamically changing environment. Establishing such a collective mental model is a key success factor for effective multi-stakeholder decision-making. It implies that collaboration in a team requires emergence, alignment and adjustment of collective mental models. However, a collective mental model is not to be confused with a collection of individual mental models, since mental models of different people can also be contradictory. A collective mental model is the shared perception or interpretation of the system that is created at a group level where decisions are made (Kim et al., 2013). The presence of a strong collective mental model in a group facilitates team cohesion (Jansen, Kostopoulos, Mihalache, & Papalexandris, 2016). Team cohesion, on the other hand, supports members of a team to freely exchange information and to work in a group environment, where conflicting learning objectives can be exchanged and dealt with (Wong, 2004). The concept of team cohesion is based on the social identity theory (Tajfel & Turner, 1979), which suggests that team cohesion encourages supporting behavior and the ability to perform conflicting tasks. The higher the team cohesion, the more commitment for the team is created and members are more likely to adjust or integrate conflicting agendas (Nakata & Im, 2010). Thus, reaching cohesiveness in a team is important for problem-solving and complex decision-making (Jansen et al., 2016). This is in line with the findings of an earlier review, where it was identified that friction of knowledge is a necessity for successful team decisions. Friction of knowledge indicates that a certain friction between different members of a team is necessary in order to uncover biases in knowledge. This process is easier conducted when the team is also cohesive, i.e. has a strong unity feeling. Taken together, it is hypothesized that if there is friction of knowledge between members in a team and the team is cohesive, they are more likely to share the friction and include conflicting views in the decision-making process in order to make the best possible decision with the information available.
Nevertheless, in case the accuracy of team collective mental models is too high, meaning if the shared knowledge and belief structure overly reflect the current condition of the organizations environment, it may be that the group rejects opposing beliefs resulting in group think (Janis, 1991). The concept of group think was already discussed in an earlier review. Therefore, there is a fine line between social identity theory (a positive effect of collective mental models on complex team decisions) and group think (a negative effect of collective mental models on complex team decisions). The right accuracy of collective mental models for team decisions is yet to be determined.
An applied example of how to surface mental models is the Safety Cube. The Safety Cube is a tool that is used in the design process to identify hazards (Rajabalinejad, 2018). In contrast to methods like the Failure Mode and Effect Analysis (FMEA), the Safety Cube is not solely relying on structural hazards, but also includes functional and operational hazards into the analysis, thereby incorporating more perspectives. Especially, probable misuse and malfunctioning of the new system is often overlooked and the risk of doing so can be mitigated by performing use-scenarios with the Safety Cube. The use of scenarios is an effective method to alter mental models, because it surfaces the different views and assumptions of people in a team. By applying the Safety Cube in a workshop, the misuse and malfunctioning of the new system can be explored, and possible intervention strategies can be initiated.
The collaboration between NS and ProRail might also benefit from performing use-scenarios that alter and align mental models of different parties involved in the integration process. This can be done by exploring possible use-scenarios in an integral approach, preventing incidents and integration issues. A possible application case is the integration of ERTMS for cross border lines, which involves collaboration of various stakeholders.
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