Leading complexity

This research is an integrated urban economics planning study that analyses the evolution of a creative digital cluster in inner East London. Its focus lies on trying to generate a better understanding of the complex actions and events that have led to its emergence and expansion in the last decade. After discussing various conceptual understandings of how industry clusters develop over time the adaptive cycle model from panarchy theory is adopted as a lens to describe this cluster’s evolution. The model is based on the assumption that clusters can be seen as complex adaptive systems with interconnected agents constantly adapting to changing environments. In order to attempt to explain the cluster’s evolution, a model based on complexity leadership theory is adopted. It focuses on the identification of different administrative, adaptive and enabling leadership strategies that help support system dynamics. A qualitative case study approach is used to generate a descriptive narrative of the cluster’s evolution. This narrative, developed through semi-structured interviews and secondary data, serves as the basis for analysis. Changes in specific adaptive dimensions are identified as well as key actions and events that have influenced the cluster’s evolution. The results of this study suggest that to date the cluster has evolved through two phases of the adaptive cycle model – reorganisation and exploitation. The level of accumulated capital and connectedness has grown during this time and the cluster appears to be relatively resilient to changing dynamics. Analysis also indicates that administrative leadership appears to have been more influential in developing the cluster during the exploitation phase than the reorganisation phase. Enabling leadership strategies have been important in both phases but they appear to have been more purposeful during the exploitation phase. The cluster has generated an increasing amount of adaptive leadership outcomes as it has evolved. A key enabling leadership action has been identified as transitioning the cluster between reorganisation and exploitation phases. The study’s findings would infer that there is a role for more administrative actions within cluster-based policy. However, these actions should be taken considering respect of the need for a certain amount of autonomy within the cluster to allow it to adapt to evolving circumstances. Due to these two complexity theory based models still being relatively undeveloped, it is recommended that further application of the models to other cases be conducted in order to compare methods and findings.