Creation of a Leadership Model with focus on Mistake Making Culture and Collective Intelligence
This master thesis focuses on the fields of mistake making culture and collective intelligence. These two sciences are important factors within today’s leadership. The thesis explores how a model based on these two sciences can benefit organizations in solving their problems in a sustainable and successful manner. The model sources the problems in the key performance indicators that are present in the balanced score card. The research is conducted using literary sources, as well as expert interviews. Literature first investigates the current state of todays leadership, before describing mistake making culture, collective intelligence and the balanced scorecard in more detail. Literature shows that several factors such as diversity, trust, open communication, agility and more contribute to the success of problem solving through groups. However, there is no combination of these fields available. Mistake making culture benefits from collective intelligence, as current approaches do not apply the best practices found in collective intelligence. The balanced scorecard is used to evaluate if measures that were developed using the model are successful through the use of the KPIs. The first draft of the model is developed out of the literature research. Then four expert interviews were conducted, each with a new iteration based on the feedback and inputs received from these interviews. The final model consists of the balanced score card, four solving element, each equipped with several solving aspects. Furthermore, a detailed questionnaire is developed to guide people using the model to solve the mistakes or problems at hand. The model is applicable by different organizations as well as all hierarchical levels, depending on the autonomy of the teams or departments. A hypothetic use case is described. It is recommended to share the outcomes of the model to ensure the sustainability and availability of the solution. Future research may focus on potential use cases for different sectors, as well as possible new KPIs that focus more on mistake discovery.
Heimann, Sandro Florian, 2022
Art der Arbeit Master Thesis
Betreuende Dozierende Gatziu Grivas, Stella, Imhof, Denis
Studiengang: Business Information Systems (Master)