The role of regional economic specialization in the production of university-owned patents

Kaspříková

Moravian Geographical Reports

2018

The level of technological specialisation in the regions of Germany is assessed in this paper, as well as how such specialisation has evolved over time. Further, in three selected regions (Munich, Düsseldorf and Oberes Elbtal/Osterzgebirge), the knowledge space is explored in detail and compared to existing smart specialisation strategies. Average relatedness and knowledge space based upon EPO patent applications are used to measure the specialisation and technology trajectories of the German regions. Between three periods 1988–1992, 1998–2002 and 2008–2012, the specialisation of Germany based on EPO patent applications increased by 10%, despite a decline in many regions. Machinery and transportation industries have increased their significance. The assessment of regional smart specialisation strategies in the three German states shows that the methodology in terms of the identification of prospective industries is largely variegated and insufficiently developed. More attention should also be given to the choice of an appropriate geographical level of aggregation for analysis. Knowledge relatedness and knowledge complexity could be used as methodological tools for selecting prospective industries in smart specialisation strategies.

Section: Theoretical Background: Innovation Specialisation and Relatmentioning

confidence: 99%

Technological relatedness, knowledge space and smart specialisation: The case of Germany

Kaspříková

Moravian Geographical Reports

2018

“…However, only a few scholarly works have empirically tested the impact of academic research on the technological trajectories of geographical areas and vice versa (Acosta et al, 2009;Braunerhjelm, 2008;Calderini & Scellato, 2005;Coronado et al, 2017). Overall, these contributions have provided mixed results concerning the existence, the direction, and the causal relationship between academic research and industrial specialization.…”

Section: Technological Specialization In Regional Innovation Systemsmentioning

confidence: 99%

“…This evidence was explained referring to two possible reasons: i) universities tend to satisfy only a fraction of the demand for technological knowledge; ii) academic research is more focused on internal objectives (i.e., scientific publications) and therefore does not consider the external demand for knowledge. Finally, Coronado et al (2017) studied the effects of reverse spillovers in high-tech sectors and found that the productive specialization of a region has a significant effect on the patenting activity of universities located in the same area. Overall, these contributions provide mixed results concerning the existence, the direction, and the causal relationship between academic research specialization and industrial one.…”

Section: Technological Specialization In Regional Innovation Systemsmentioning

confidence: 99%

“…In divergent processes, the technological specialization of universities and local firms follows different trajectories (Acosta et al, 2009), while convergent processes are characterized by increasing technological proximity over time between local firms and universities. In the case of region-pull processes, local firms exert the leading role and guide the evolution of the local technological specialization (Coronado et al, 2017), while in university-push processes, regional technological trajectories are driven by local universities through their entry into new technological fields (Braunerhjelm, 2008;Calderini & Scellato, 2005).…”

Section: Co-evolution Patterns Of Industry and Academic Innovation Activitiesmentioning

confidence: 99%

See 1 more Smart Citation

Co-evolution patterns of university patenting and technological specialization in European regions

et al. 2022

This paper provides novel evidence on co-evolution patterns of the technological specialization of innovation activities of firms and academic institutions located in the same European region during the years from 2003 to 2014. We exploit a novel and unique dataset merging data on EU-funded R&D projects, universities, patents, and economic region-level data for a large sample of universities and firms co-located in geographical areas at the third level of the Nomenclature of Territorial Units for Statistics (NUTS3), which correspond to a sub-regional scale of analysis. Our results indicate the presence of substantial heterogeneity across the analyzed EU regions with respect to the co-evolution of industry and academia specializations. In particular, we find that the specialization into a new technological domain is led by the local academic research system only in a few cases. We also document that a number of factors, at both the university and region levels, are associated with convergent or divergent processes in the relative specialization of the innovation activities carried out by firms and universities co-located in the same region.

Journal of Open Innovation: Technology, Market, and Complexity

“…Several authors have reported on how various firm characteristics influence the establishment of university collaborations: size [22,23]; time of existence [24]; geographic location [21,25,26]; operating sector [19]; and specialization in the operating sector [27]. Ahrweiler et al [28] investigated the role of university-industry links for innovation generation and diffusion in networks in two contexts: large, diversified companies and small technology companies.…”

Section: Contextmentioning

confidence: 99%

Socioeconomic Impacts of University–Industry Collaborations–A Systematic Review and Conceptual Model

Lima

Torkomian

Pereira

et al. 2021

University–industry collaborations create socioeconomic impacts for the areas where they are undertaken. Although these collaborations have recognized importance and a high potential to generate economic and social benefits, there is no consensus in the literature on a consolidated conceptual model for assessing their socioeconomic impacts. Given this scenario, this study reviews 94 studies on the socioeconomic impact of university–industry collaborations using a context–intervention–mechanism–outcomes configuration. The impacts identified in the systematic literature review are classified into: (1) economic, (2) social, and (3) financial. The systematic literature review also indicates that the impact of collaborations can change the context and enhance the mechanisms of technology transfer. From a theoretical viewpoint, this work contributes to the structuring of a conceptual model for assessing the socioeconomic impacts of university–industry collaborations. In addition, the results have contributions for management in each strand of the triple helix: they may be useful to guide universities and companies on how to assess the socioeconomic impacts of each collaboration, direct public agents in the evaluation of results of investments, and support the development of policies for innovation and technology management.