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R&D partnerships: An exploratory approach to the role of structural variables in joint project performance

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  • Arroyabe, Marta F.
  • Arranz, Nieves
  • Fdez. de Arroyabe, Juan Carlos

Abstract

Traditionally the literature on partnership has focused on understanding why firms choose to cooperate and with whom, however, our understanding of the impact of the resulting network structure on the performance of the project is limited. This study builds on joint R&D projects developed in Europe in order to analyse certain structural variables – number and typology of partners, and intensity and density of interactions in the network – which may result in a greater performance of exploration and exploitation R&D projects. Findings show that these structural variables are good predictors of project performance; in particular, the joint performance function in exploration projects is positively dependent on the number of partners; however, in exploitation projects this function depends positively on the density and intensity of interactions, and negatively on the number of partners. Our results complement previous research while adding empirical evidence on the nonlinear and contingent character of structural variables and the performance of joint projects.

Suggested Citation

  • Arroyabe, Marta F. & Arranz, Nieves & Fdez. de Arroyabe, Juan Carlos, 2015. "R&D partnerships: An exploratory approach to the role of structural variables in joint project performance," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 623-634.
  • Handle: RePEc:eee:tefoso:v:90:y:2015:i:pb:p:623-634
    DOI: 10.1016/j.techfore.2014.01.007
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    References listed on IDEAS

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    5. Hall, Bronwyn H & Link, Albert N & Scott, John T, 2001. "Barriers Inhibiting Industry from Partnering with Universities: Evidence from the Advanced Technology Program," The Journal of Technology Transfer, Springer, vol. 26(1-2), pages 87-98, January.
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    7. Bronwyn H. Hall & Albert N. Link & John T. Scott, 2003. "Universities as Research Partners," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 485-491, May.
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    Cited by:

    1. repec:eee:epplan:v:66:y:2018:i:c:p:147-155 is not listed on IDEAS
    2. Rossi, Federica & Caloffi, Annalisa & Russo, Margherita, 2016. "Networked by design: Can policy requirements influence organisations' networking behaviour?," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 203-214.
    3. Gilsing, Victor A. & Cloodt, Myriam & Bertrand–Cloodt, Danielle, 2016. "What makes you more central? Antecedents of changes in betweenness-centrality in technology-based alliance networks," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 209-221.
    4. Rezaeian, M. & Montazeri, H. & Loonen, R.C.G.M., 2017. "Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 270-280.
    5. Lee, Young Hoon & Kim, YoungJun, 2016. "Analyzing interaction in R&D networks using the Triple Helix method: Evidence from industrial R&D programs in Korean government," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 93-105.

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