Multi-level dynamics in the cultural and creative industry clustering - the case of TV Drama in Taiwan

Abstract

Since Alfred Marshall’s explorations into industrial districts in England, strands of studies have developed to articulate the clustering phenomena taking place in geographical proximities. Michael Porter’s theorisation on industrial clustering acting as the engine of regional growth, henceforth, has attracted both academic and public policymakers’ interests in understanding the clustering mechanism. Amidst various schools’ interpretation of how the cluster develops, and how it promotes regional growth, studies of ‘Evolutionary Economic Geography (EEG)’ stresses clustering as a dynamic process that involves varying co-located actors contributing to the constant cluster evolutions. To elaborate the complex mechanism of cluster evolutions, the Evolutionary Economic Geography approach has adapted Marshall’s regional agglomeration concept into a ‘multi-scaled' structure, which considers system-wide actors’ behaviour and activities are relative to the transforming clustering patterns. Hence, Evolutionary Economic Geography thinking proposes cluster studies to include analyses of system-wide actors and regional contexts to configure the full picture of the clustering dynamics. This ‘multi- scaled clustering framework’ presents an interesting approach to understand the development of the Cultural and Creative Industrial (CCI) Cluster - which interacts with region-specific culture, value and network to produce artistic products. Hence, the multi- scaled clustering framework suggested a valuable perspective to systematically analyse the CCI cluster's developments. Grounding on the multi-levelled framework, this research aims to understand the evolutionary process of the Taiwanese CCI cluster. The case study on the Taiwanese TV drama sector, one of the longest developed sectors in Taiwan’s cultural and creative industry, indicated the co-located actors are associated with changing clustering patterns, and the aggregated activities of actors at varying scales drove multiple stages of cluster evolution. The findings of this research also showcase the valuable cluster examinations through the multi-levelled framework, which is worth future research to apply for further explorations into clusters of different contexts and industries.

Date of Award	31 Dec 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Kieron Flanagan (Supervisor) & Elvira Uyarra (Supervisor)