New perspectives on university-industry relations: an analysis of the knowledge flow within two sectors and two countries

Abstract

University-industry relations (UIR) are usually analysed by the knowledge transfer channels, but existing studies have failed to address what knowledge content is being transferredimpacting the technology output aimed by the partnership. We aim to identify the Knowledge Flow in UIR, defined by the actor responsible for initiating a partnershipdirection of the flowand the type of technology output being transferredcontent of the flow. We compare the knowledge flow in innovation systems of agriculture and aerospace sectors in Brazil and the Netherlands. The cases show that the knowledge flow is influenced more by the maturity of the national and sectoral innovation system than by the technological trajectory of the sector itself. While the Dutch cases demonstrate that both sectors are part of a mature innovation system, the Brazilian cases, although expected to have a similar technological trajectory, demonstrated a more immature innovation system.

Innovation ability of universities and the efficiency of university–industry knowledge flow: the moderating effect of provincial innovative agglomeration

“…Knowledge network theory emphasizes that the premise of realizing efficient knowledge flow is to form a knowledge potential difference between knowledge subjects, which is mainly reflected in the effect of knowledge distance between different subjects on the efficiency of knowledge flow (Moller and Rajala, 2007;Dalmarco et al, 2019). Because of substantial differences in professional background, management mode and knowledge level between enterprises and universities, knowledge distance exist between the two sides (Perkmann et al, 2013), especially in the aspect of innovation ability.…”

“…In the mechanism of University–Industry collaborative innovation, universities, as the subject of knowledge innovation, are both the beginning and the output of knowledge flow. However, most scholars focus on the perspective of enterprise subjects in their research (Soh and Subramanian, 2013; Dalmarco et al , 2019). Although the research on innovation performance of universities has been increasing in recent years (Perkmann and Walsh, 2007; Huang and Chen, 2017), few scholars have studied the efficiency of University–Industry knowledge flow from the perspective of the subject of universities.…”

Research on the Performance of Knowledge Co-Creation of Science and Technology Enterprises Based on IUR Network

“…This kind of "corporate knowledge" and "theoretical knowledge" collide and combine in the network [20], and jointly create new knowledge meaning that knowledge co-creates performance. In the process of knowledge co-creation of enterprises and academic research institutions, the enterprise first sends out knowledge requests as the knowledge output terminal, and the academic research institutions receive the knowledge requests for theoretical analysis, and output the theoretical results obtained by the enterprise to accurately guide the enterprise to solve practical problems [21][22][23]. At the same time, at the input end of knowledge, enterprises receive the theoretical results of academic research institutions, conduct practical tests, and complete the actual transformation of theoretical results.…”

Description and assessment of a collaborative agricultural extension program adopted under the triple helix model of innovation

“…Despite its potential, the study and empirical evidence of the role of the THM in inclusive innovation are limited, especially in the case of emerging and developing economies (Daniels et al, 2017). The current research on the THM in agriculture is focused on exploring the role of the institutional sectors involved and their interactions in specific cases (e.g., Dalmarco et al, 2019;Derunova, 2021;Yongabo & Goktepe-Hulten, 2021). Some authors have proposed extending the model to include additional actors such as civil society and the environment in a quadruple or even quintuple helix model (Carayannis et al, 2017).…”