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Multilevel heterogeneity of R&D cooperation and innovation determinants

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  • Sara Amoroso

    (Joint Research Centre, European Commission)

Abstract

Assessing the impact of public support to innovation on R&D collaboration may require a more complex multilevel design, that describes the likely correlation present among firms characteristics within a particular sector. Using data from the 2006 edition of the Community Innovation Survey (CIS) for the Netherlands, we propose a methodology to study the effect of firm-level characteristics on the propensity to undertake a research collaborative agreement. In particular, we show that controlling for a richer variance structure yields a different picture with respect to simpler regression frameworks adopted in the literature of R&D cooperation determinants. Moreover, such a hierarchical framework can be generalized allowing for clustering at higher levels, such as sectors or geographical areas. Besides the link between public funding and R&D collaboration, our results confirm the findings of the literature: technological spillovers, risk and cost sharing rationales, firm’s size, and type of innovative activity are related to the decision of engaging in different sorts of research alliances.

Suggested Citation

  • Sara Amoroso, 2017. "Multilevel heterogeneity of R&D cooperation and innovation determinants," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(1), pages 93-120, April.
  • Handle: RePEc:spr:eurasi:v:7:y:2017:i:1:d:10.1007_s40821-015-0041-1
    DOI: 10.1007/s40821-015-0041-1
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    References listed on IDEAS

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    Cited by:

    1. Vivarelli, Marco, 2018. "Globalisation, structural change and innovation in emerging economies: The impact on employment and skills," MERIT Working Papers 2018-037, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    2. Gkypali, Areti & Arvanitis, Spyros & Tsekouras, Kostas, 2018. "Absorptive capacity, exporting activities, innovation openness and innovation performance: A SEM approach towards a unifying framework," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 143-155.
    3. Castellani, Davide & Piva, Mariacristina & Schubert, Torben & Vivarelli, Marco, 2019. "R&D and productivity in the US and the EU: Sectoral specificities and differences in the crisis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 279-291.
    4. Van Roy, Vincent & Vértesy, Dániel & Vivarelli, Marco, 2018. "Technology and employment: Mass unemployment or job creation? Empirical evidence from European patenting firms," Research Policy, Elsevier, vol. 47(9), pages 1762-1776.
    5. Alfonso Expósito & Juan A. Sanchis-Llopis, 2019. "The relationship between types of innovation and SMEs’ performance: a multi-dimensional empirical assessment," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(2), pages 115-135, June.
    6. Piotr Trąpczyński & Łukasz Puślecki & Michał Staszków, 2018. "Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-32, November.

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    More about this item

    Keywords

    R&D collaboration; Innovation; R&D policy; Bayesian analysis;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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