Table 3 Research steps, data collection... | Emerald Publishing

Table 3

Research steps, data collection processes and output

	Step 1 (June 2016–June 2019) Participation in the FH project	Step 2 (March 2018–June 2019) University in action	Step3 (March 2018-ongoing) Institutionalisation
Research step	Forget Heritage EU project aiming to identify innovative, replicable and sustainable Public Private Cooperation management models of the historical sites by valorising them through setting up Cultural and Creative Industries	University in action (translation in action of the outcomes emerged from step 1)	Research outcomes recombination and Conceptual model design
Data collection	Interviews with 100 entrepreneurs and actors involved in urban restoration programs in ten cities/regions across Europe 6 meetings involving the representatives of the project partners 6 meetings involved a large audience of public officials in cultural heritage and private managers or entrepreneurs in cultural and creative firms and start-ups	Unstructured dialogues with 140 aspirant entrepreneurs Brainstorming with 47 master students 3 meeting of the Panel of experts in the cultural field involving 18 members from public administration, companies, not-for-profit organisations and cultural institutions	Use of data collected in the previous steps Data and knowledge recombination Independent reading of all the materials from all the authors Comparison and discussion
Output	Best practices and transferable elements in cultural heritage management, summarised in nine cards of best practice cultural start-ups and in the document: Analysis of the transferable elements Educational gaps in management for aspirant cultural entrepreneurs, summarised in the document: Transnational training model Notes about experiences arising from the meetings and summarised in the Strategy of management of cultural heritage through cultural and creative firms	Cooperation with the emerging start-ups in the cultural sector Practical problems discussion and solution Teaching and mentoring based on the data collection and analysis Observation and notes collection	Data categorisation via manual open coding Conceptual model or knowledge translation tailored for the cultural ecosystem (see Figure 2)

	Step 1 (June 2016–June 2019) Participation in the FH project	Step 2 (March 2018–June 2019) University in action	Step3 (March 2018-ongoing) Institutionalisation
Research step	Forget Heritage EU project aiming to identify innovative, replicable and sustainable Public Private Cooperation management models of the historical sites by valorising them through setting up Cultural and Creative Industries	University in action (translation in action of the outcomes emerged from step 1)	Research outcomes recombination and Conceptual model design
Data collection	Interviews with 100 entrepreneurs and actors involved in urban restoration programs in ten cities/regions across Europe 6 meetings involving the representatives of the project partners 6 meetings involved a large audience of public officials in cultural heritage and private managers or entrepreneurs in cultural and creative firms and start-ups	Unstructured dialogues with 140 aspirant entrepreneurs Brainstorming with 47 master students 3 meeting of the Panel of experts in the cultural field involving 18 members from public administration, companies, not-for-profit organisations and cultural institutions	Use of data collected in the previous steps Data and knowledge recombination Independent reading of all the materials from all the authors Comparison and discussion
Output	Best practices and transferable elements in cultural heritage management, summarised in nine cards of best practice cultural start-ups and in the document: Analysis of the transferable elements Educational gaps in management for aspirant cultural entrepreneurs, summarised in the document: Transnational training model Notes about experiences arising from the meetings and summarised in the Strategy of management of cultural heritage through cultural and creative firms	Cooperation with the emerging start-ups in the cultural sector Practical problems discussion and solution Teaching and mentoring based on the data collection and analysis Observation and notes collection	Data categorisation via manual open coding Conceptual model or knowledge translation tailored for the cultural ecosystem (see Figure 2)