Iranian Association for Management of Technology Journal of Science and Technology Policy 20080840 18 3 2025 09 23 Revisiting the Role of the Innovation and Prosperity Fund and Science & Technology Parks in Shaping Technology Drivers Revisiting the Role of the Innovation and Prosperity Fund and Science & Technology Parks in Shaping Technology Drivers 97 115 14150 10.22034/jstp.2025.12111.1959 FA Atieh Bozorgipour PhD candidate, Department of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Manouchehr Manteghi Professor, Department of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Javad Mashayekh Assistant professor, Department of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, Iran Journal Article 2025 10 16 Innovation Fund and Technology Parks, as network-oriented intermediary institutions, play a pivotal role in designing and facilitating interactions among knowledge-based firms. Focusing on this role, the present study proposes an integrated framework that combines social network analysis and multi-criteria decision-making to identify firms with high potential to emerge as technology drivers within Iran’s innovation ecosystem. The dataset comprises the participation of 6,224 knowledge-based firms in 1,419 networking events organized by the Innovation Fund, forming a co-attendance network with 270,623 links. Based on these data, the co-attendance network was modeled and key structural metrics (density, average degree, diameter, clustering coefficient) were calculated. Community detection algorithms were then applied to extract natural technological clusters, and four centrality measures (degree, betweenness, closeness, eigenvector) were computed for each firm. These indicators were integrated using the TOPSIS multi-criteria decision-making method to generate a ranked list of firms with high potential to act as technology drivers.The results indicate that these high-potential firms are concentrated in several technological domains, primarily ICT (~22.5%), electronics and automation systems (~22.3%), advanced machinery and equipment (~20%), and advanced materials (~13.7%). The analysis also shows that approximately one-third of these potential driver firms are located in technology parks and incubators, with the share varying across technological clusters. Overall, the findings highlight that firms’ positions within the collaboration network—measured through centrality indicators and community detection outcomes—are the key determinants of their “driver potential.” Accordingly, the proposed framework offers a practical tool for policymakers to target support measures and design data-driven networking programs more effectively. Innovation Fund and Technology Parks, as network-oriented intermediary institutions, play a pivotal role in designing and facilitating interactions among knowledge-based firms. Focusing on this role, the present study proposes an integrated framework that combines social network analysis and multi-criteria decision-making to identify firms with high potential to emerge as technology drivers within Iran’s innovation ecosystem. The dataset comprises the participation of 6,224 knowledge-based firms in 1,419 networking events organized by the Innovation Fund, forming a co-attendance network with 270,623 links. Based on these data, the co-attendance network was modeled and key structural metrics (density, average degree, diameter, clustering coefficient) were calculated. Community detection algorithms were then applied to extract natural technological clusters, and four centrality measures (degree, betweenness, closeness, eigenvector) were computed for each firm. These indicators were integrated using the TOPSIS multi-criteria decision-making method to generate a ranked list of firms with high potential to act as technology drivers.The results indicate that these high-potential firms are concentrated in several technological domains, primarily ICT (~22.5%), electronics and automation systems (~22.3%), advanced machinery and equipment (~20%), and advanced materials (~13.7%). The analysis also shows that approximately one-third of these potential driver firms are located in technology parks and incubators, with the share varying across technological clusters. Overall, the findings highlight that firms’ positions within the collaboration network—measured through centrality indicators and community detection outcomes—are the key determinants of their “driver potential.” Accordingly, the proposed framework offers a practical tool for policymakers to target support measures and design data-driven networking programs more effectively. https://jstp.nrisp.ac.ir/article_14150_c87e856265237256c66f108acc5849e2.pdf