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Characterizing and comparing innovation systems by different ‘modes’ of knowledge production: A proximity approach

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Listed:
  • Sjoerd Hardeman
  • Koen Frenken
  • Önder Nomaler
  • Anne L. J. Ter Wal

Abstract

Though the concept of innovation systems has become influential in both academia and policy-making, an analytical approach to understanding innovation systems is still lacking. In particular, there is no analytical framework to measure ‘Mode 1’ and ‘Mode 2’ knowledge production. We propose a framework based on the proximity concept. Mode 1 and Mode 2 knowledge production are characterized by collaborations with cognitive, organizational, social, institutional and geographical proximity, and distance, respectively. Using a gravity model approach we apply our framework to the case of type 2 diabetes research and provide a characterization of the global innovation system and a comparative analysis of the North American and European innovation systems. Our main results hold that although collaborative research on type 2 diabetes generally follows a logic of proximity and hence is not characterized as Mode 2, important differences and similarities exist between the North American and European innovation systems.

Suggested Citation

  • Sjoerd Hardeman & Koen Frenken & Önder Nomaler & Anne L. J. Ter Wal, 2015. "Characterizing and comparing innovation systems by different ‘modes’ of knowledge production: A proximity approach," Science and Public Policy, Oxford University Press, vol. 42(4), pages 530-548.
    • Handle: RePEc:oup:scippl:v:42:y:2015:i:4:p:530-548.
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    File URL: http://hdl.handle.net/10.1093/scipol/scu070
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    References listed on IDEAS

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    1. Tom Broekel & Pierre-Alexandre Balland & Martijn Burger & Frank Oort, 2014. "Modeling knowledge networks in economic geography: a discussion of four methods," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 423-452, September.
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    Cited by:

    1. Nebojša Stojčić, 2021. "Collaborative innovation in emerging innovation systems: Evidence from Central and Eastern Europe," The Journal of Technology Transfer, Springer, vol. 46(2), pages 531-562, April.
    2. Santos Arteaga, Francisco J. & Tavana, Madjid & Di Caprio, Debora & Toloo, Mehdi, 2019. "A dynamic multi-stage slacks-based measure data envelopment analysis model with knowledge accumulation and technological evolution," European Journal of Operational Research, Elsevier, vol. 278(2), pages 448-462.
    3. Pierre-Alexandre Balland & Ron Boschma & Koen Frenken, 2020. "Proximity, Innovation and Networks: A Concise Review and Some Next Steps," Papers in Evolutionary Economic Geography (PEEG) 2019, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Mar 2020.
    4. Susan Roelofs & Nancy Edwards & Sarah Viehbeck & Cody Anderson, 2019. "Formative, embedded evaluation to strengthen interdisciplinary team science: Results of a 4-year, mixed methods, multi-country case study," Research Evaluation, Oxford University Press, vol. 28(1), pages 37-50.
    5. Davids, Mila & Frenken, Koen, 2015. "Proximity, knowledge base and the innovation process The case of Unilever’s Becel diet margarine," Papers in Innovation Studies 2015/7, Lund University, CIRCLE - Centre for Innovation Research.
    6. Andrea Morescalchi & Sjoerd Hardeman, 2015. "Technological diversity and the impact of regional innovation: evidence for the EU," ERSA conference papers ersa15p1250, European Regional Science Association.
    7. Anna Jonsson & Maria Grafström & Mikael Klintman, 2022. "Unboxing knowledge in collaboration between academia and society: A story about conceptions and epistemic uncertainty [De-essentializing the Knowledge Intensive Firm: Reflections on Skeptical Resea," Science and Public Policy, Oxford University Press, vol. 49(4), pages 583-597.
    8. Tobias Koopmann & Maximilian Stubbemann & Matthias Kapa & Michael Paris & Guido Buenstorf & Tom Hanika & Andreas Hotho & Robert Jäschke & Gerd Stumme, 2021. "Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9847-9868, December.
    9. Santos-Arteaga, Francisco J. & Di Caprio, Debora & Tavana, Madjid & O’Connor, Aidan, 2017. "Innovation dynamics and labor force restructuring with asymmetrically developed national innovation systems," International Business Review, Elsevier, vol. 26(1), pages 36-56.

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