Abstract

Transformative Organizational Change becomes more and more significant both practically and academically, especially in the context of organizational culture and learning. However computational modeling and a formalization of organizational change and learning processes are still largely unexplored. This paper aims to provide an adaptive network model of transformative organizational change and translate a selection of organizational learning and change processes into computationally modelled processes. Additionally, it sets out to connect the dynamic systems view of organizations to self-modelling network models. The creation of the model and the implemented mechanisms of organizational processes are based on extrapolations of an extensive literature study and grounded in related work in this field, and then applied to a specified hospital-related case scenario in the context of safety culture. The model was evaluated by running several simulations and variations thereof. The results of these were investigated by qualitative analysis and comparison to expected emergent behaviour based on related available academic literature. The simulations performed confirmed the occurrence of an organizational transformational change towards a constant learning culture by offering repeated and effective learning and changes to organizational processes. Observations about various interplays and effects of the mechanism have been made, and they exposed that acceptance of mistakes as a part of learning culture facilitates transformational change and may foster sustainable change in the long run. Further, the model confirmed that the self-modelling network model approach applies to a dynamic systems view of organizations and a systems perspective of organizational change. The created model offers the basis for the further creation of self-modelling network models within the field of transformative organizational change and the translated mechanisms of this model can further be extracted and reused in a forthcoming academic exploration of this field.