Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach

Rouven E. Haschka and Helmut Herwartz

Research Policy, 2020, vol. 49, issue 8

Abstract: Innovation output is key to the long run business success in high-technology markets. Since research is costly, the absorption of external knowledge might be a powerful device for improving innovation processes. Considering four high-technology sectors in Europe, we examine to what extent local networking, competitive spillovers, or (unobserved) locational advantages influence innovation processes at the firm level. Since high-technology markets require strong innovative capacity, we specifically investigate the effectiveness of patent blocking as a promising strategy to exclude competition and to improve own competitiveness. We model innovation processes empirically by means of a recent Bayesian stochastic frontier approach that allows for spatial dependence and spillover effects. Our results indicate that growing pressures to innovate could create vicious levels of innovation competition. Moreover, the access to local networks boosts the pursuit of innovation and enhances innovativeness.

Keywords: Knowledge production; Knowledge spillovers; Innovation efficiency; Stochastic frontier analysis; Bayesian inference; Spatial dependence; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C23 L21 M21 O32 R12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:respol:v:49:y:2020:i:8:s0048733320301323

DOI: 10.1016/j.respol.2020.104054

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