This research draws on social network data of organisations in the creative industries and cultural sector in George Town UNESCO World Heritage Site. We collected five types of network: 1) Collaborative Network 2) Advice Network 3) Funding Network (giving and receiving) 4) Training 5) Talents. The data set also contains the background information of the organisation (anonymised) and their corresponding quantitative answers on Entrepreneurial Orientation (innovativeness, risk-taking behaviour, pro-activeness of the organisation, based on 5-point Likert scale). This data enables the identification of the type of actors and their networks, and the respective organisation's entrepreneurial orientation.This research addresses the phenomenon of creative and cultural cluster in a site with a rich cultural heritage. The project will explore questions regarding the role of different actors in developing businesses in cultural districts while preserving and disseminating their cultural heritage. Hence, the research is positioned in a wider debate regarding the links between cultural preservation, urban regeneration and tourism. The selected site is George Town, a post-British colonial town in Malaysian. The town has experienced gentrification since 1997, exacerbated with the growth of tourism and influx of external capital investment in properties, after it has been inscribed as a UNESCO World Heritage Site in 2008. Gentrification induces displacement of long-term residents and causes erosion of local culture and heritage - a global phenomenon in many World Heritage Sites. Nonetheless, a cluster of creative industries and cultural sector has been developed with involvement of various local and external actors. The development of cultural district has become an important tool for many urban planners aiming to foster the development of urban centres and revitalise neighbourhoods-in-decline. Reviewing the existing models of innovation and entrepreneurship ecosystem, there is a gap in the scope of these models in capturing civil society actors and insufficiently explaining the phenomena of cultural clusters. Thereby, they are inadequate in informing urban planning policy makers, the innovation community, cultural organisations and small businesses. The main objectives of this project are: 1) To develop a relational model of innovation and entrepreneurship ecosystem for creative and cultural districts, 2) To provide social network insights for the development of creative economy and conservation of cultural heritage in George Town. 3) To produce a policy brief on strategies for developing creative and cultural districts in George Town. This research deployed Social Network Analysis to identify actors and their networks. Understanding these networks and roles will enable the conceptual development of a novel relational model of entrepreneurship and innovation ecosystem, specifically for cultural districts. This research will collect, via face-to-face administrated questionnaires, data on network relations of actors in the creative industries and cultural sector in George Town. We used UCINET to analyse and map this network and identify its strengths and weaknesses. We also conducted six in-depth case studies of core actors in the networks to gain rich qualitative insights on how they build innovative and entrepreneurial networks. The model is applicable more generally to other small/medium size towns and cities, particularly world heritage sites, where there is a potential to build a creative and cultural district empowering local residents with development opportunities in terms of culture, social and economy.

The data was collected by face-to-face administration of the questionnaire between June to December 2017, with some following up after checking data. We interviewed 33 organisations from the bottom-up approach. Sampling is based on snowballing as we followed the network of the organisations (limited to creative and cultural sector's directly related organisation). The original set of organisations were identified using two approaches: top-down, where 20 out of 38 organisations were interviewed based on a list recommended by local expert; and bottom-up, where we sampled organisation, using random number, from an existing database collected by local partner ThinkCity (421), and Yellow Pages and Google Map (227). We cleaned the list by removing organisations which do not meet our criteria, such as out of the study area, dissolved, overlapping with the first list, or not within the scope of creative industries and cultural sector.