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Understanding innovation: An analysis of persistence for Spanish manufacturing firms

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  • Triguero, Ángela
  • Córcoles, David

Abstract

This study focuses on the persistence of innovation in a panel of Spanish manufacturing firms for the period 1990–2008. In particular, we analyse whether persistence in firms’ innovation activities over time is the result of previous experience, the dynamic capabilities of the firm or industry-market related characteristics. We find that R&D (input) and innovation (output) are highly persistent at the firm level. After controlling for unobserved heterogeneity and initial conditions and by using a dynamic random effects probit, we conclude that there are similar determinants of persistence in R&D and innovative activities. Among external/environmental factors, market dynamism affects R&D and innovation. Regarding firm specific characteristics, size and outsourcing also have a positive impact on both processes. Past innovative behaviour is clearly more decisive in explaining the current state of R&D and innovation activities than external factors or firm-level heterogeneity.

Suggested Citation

  • Triguero, Ángela & Córcoles, David, 2013. "Understanding innovation: An analysis of persistence for Spanish manufacturing firms," Research Policy, Elsevier, vol. 42(2), pages 340-352.
    • Handle: RePEc:eee:respol:v:42:y:2013:i:2:p:340-352
    DOI: 10.1016/j.respol.2012.08.003
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    More about this item

    Keywords

    R&D; Innovation; Persistence; Panel data; Firm heterogeneity; Market characteristics;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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