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Linking collaborative R&D strategies with the research and innovation performance of SMEs in peripheral regions: Do spatial and organizational choices make a difference?

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  • Timo Mitze
  • Björn Alecke
  • Janina Reinkowski
  • Gerhard Untiedt

Abstract

We examine the empirical link between collaborative R&D strategies and the research and innovation performance of small- and medium-sized enterprises in peripheral locations. Using a survey of German firms combined with time series information on patent applications obtained from the European Patent Office, we apply a comparison-group approach and estimate different “treatment effect” models to assess the notion of causality underlying this relationship. Besides accounting for observed and unobserved firm-specific heterogeneity, we thereby also control for the likely endogeneity of R&D collaboration as a strategic choice in the course of research and innovation activities. Our results for the period 2001–2007 indicate that engaging in R&D collaboration vis-á-vis a non-collaborative research strategy is related to higher outcome levels for a firm’s key research and innovation indicators such as R&D and patent intensity. We also find that this latter link varies by firm size and the organizational type of cooperating partner, and especially those firms that simultaneously engage in research collaborations with other private businesses and research institutes/universities show above average innovation performance. In contrast, the notion of spatial proximity to research partners is shown to be of less importance. Our results may be of help for the future design of regional policies supporting the research and innovation activity of small firms in peripheral and remote locations outside large metropolitan areas. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Timo Mitze & Björn Alecke & Janina Reinkowski & Gerhard Untiedt, 2015. "Linking collaborative R&D strategies with the research and innovation performance of SMEs in peripheral regions: Do spatial and organizational choices make a difference?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(2), pages 555-596, December.
    • Handle: RePEc:spr:anresc:v:55:y:2015:i:2:p:555-596
    DOI: 10.1007/s00168-015-0719-4
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    Cited by:

    1. Amitrajeet A. Batabyal & Peter Nijkamp, 2019. "The magnification of a lagging region’s initial economic disadvantages on the balanced growth path," Asia-Pacific Journal of Regional Science, Springer, vol. 3(3), pages 719-730, October.
    2. Kristina Jespersen & Damiana Rigamonti & Morten Berg Jensen & Rune Bysted, 2018. "Analysis of SMEs partner proximity preferences for process innovation," Small Business Economics, Springer, vol. 51(4), pages 879-904, December.
    3. Reher, Leonie & Runst, Petrik & Thomä, Jörg, 2024. "Personality and regional innovativeness: An empirical analysis of German patent data," Research Policy, Elsevier, vol. 53(6).
    4. Yong Sauk Hau, 2018. "SMEs’ External Technology R&D Cooperation Network Diversity and Their Greenhouse Gas Emission Reduction and Energy Saving: A Moderated Mediation Analysis," Sustainability, MDPI, vol. 11(1), pages 1-13, December.
    5. Xiaohua Yu & Yuan Qi & Longzhen Yu & Yuanyuan He, 2022. "Temporal and Spatial Evolution of Coupling Coordination Degree of Industrial Innovation Ecosystem—From the Perspective of Green Transformation," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    6. Petr Hajek & Roberto Henriques, 2017. "Modelling innovation performance of European regions using multi-output neural networks," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.

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    More about this item

    Keywords

    C14; C21; O32; O38;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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