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.

Introduction

     Organizational culture plays an important role in organizations’ success and failures (Johnson, Nguyen, Groth, Wang, & Ng, 2016), as organizational culture offers employees a framework they can apply to reality, which helps them to evaluate what is of significance for the organization and themselves, and what is irrelevant to the organization (Łukasik, 2018). Therefore to be successful in a change of strategy, e.g., towards more sustainability, a change in organizational culture is often inevitable (Bedford & Kucharska, 2021).

     Organizational culture is especially important nowadays as is its constant improvement in the context of Health Care, as Covid 19 is putting further pressure on public healthcare systems (Ojogiwa & Qwabe, 2021). Especially constant learning culture in healthcare organizations is vital as it supports their innovation performance thanks to human capital development (Kucharska, 2022). Furthermore, communication and cooperation patterns between employees directly impact care for patients, so demanding circumstances lead to lower-quality patient outcomes (Johnson et al., 2016). Patient safety is naturally the healthcare system’s priority, hence many healthcare organisations have deeply embedded safety-oriented cultures. To prevent healthcare safety-related harms, the Institute of Medicine (IOM) recommends a culture of safety, understood as a constant improvement of patient care (Kohn et al., 2000).

Discussion & conclusion

     This paper was based on material from (Rass et al., 2022). The goal of this research was to further explore the field of transformational change, in the context of organizational learning and culture by computational modelling of organizational and individual processes. This specifically realizes itself in the objective to create an adaptive multi-order self-modelling network model that conceptualizes and approximates transformative organizational cultural change. The implemented mechanisms of organizational processes were based on an extensive literature study and grounded in related work in this field (Canbaloğlu et al., 2021), creating the described computational model of this study.

۶٫۱٫ Evaluation of the computational model for the research focus

     To confirm the validity of the created computational model, a scenario and variations to it were created, enabling us to compare the models’ emergent behaviours. To further substantiate the model, the results of the variations of the scenario got compared to the base scenario, to gain knowledge about possible network effects (is there a better way to say something like “observe interplays and isolations of the mechanisms” again).