The Mechanisms of Technological Innovation in Smes: A Bayesian Network Analysis of Eu Regional Policy Impact on Polish Firms

Abstract

We study the underlying mechanisms of technological innovation in SMEs in the context of ex-post evaluation of European Union’s regional policy. Our aim is to explain the observed change in firms’ innovativeness after receiving EU support for technological investment. To do so, we take an approach that is novel in innovation studies: a Bayesian Network Analysis to assess the effectiveness of EU policy instrument for technological innovation and to determine the mechanisms by which the policy works within firms. Our data draw from a unique survey of 200 Polish firms that received “Technological Credit” during the 2007−2013 programming period. First, we confirm the short-term positive impact of the EU innovation policy (i.e. a wider range of products/services offered, increased sales and exports). More importantly, we determine the causal chain between economically quantifiable outcomes and behavioural change in the firm, which is an important node in the network of effects generated. We find that only the financially sounder and more internationalised firms managed to take advantage of the policy. These findings suggest that programmes based on technological credits are not well suited to foster innovation in more fragile and domestically oriented SMEs, which may require different policy instruments.

Financial constraints, government subsidies, and corporate innovation

“…However, this strategy might cause corruption, and thus lead to the under-efficiency of subsidies [ 4 , 6 ]. For example, it is argued that governments contribute more to corporations with better financing in Poland [ 21 ]. Thus, many studies indicate distortion of resource allocation by public bureaucracies [ 9 ], and argue that the political resource curse effect and crowding out effect may hinder corporate innovation and its performance [ 6 ].…”

Success Factors for Innovation: A Bayesian Network Approach

“…A similar study of innovation utilising Bayesian networks is cited by Kim and Park (2008) and Florio et al (2018). Kim and Park (2008) applied the Bayesian network as a methodology to investigate the impact of R&D collaboration on innovation performance among Korean companies.…”

“…In this study, a causal chain was presented between variables and R&D collaboration had a significant impact on process innovation, but it did not affect product innovation, and government support was the most important factor for product innovation. Florio et al (2018) studied the mechanisms of technological innovation for small and medium-sized Polish enterprises in terms of evaluating the EU's regional policies. Explaining the change in innovation of companies after being supported by technology investment through Bayesian networks, they claimed that Bayesian network analysis confirmed that Technological Credit is taking a link between the adoption of new technologies and the discovery of new overseas markets.…”

Evaluating business support measures: The Bayesian Network approach

“…Survey responses were complemented with data on the volume of ERDF support provided, the types of projects funded and additional variables made available by the Managing Authorities on the beneficiary firms (such as their sector and size). 7 More information on the instruments, their theory of change, the sample of respondent firms, and details on all the analyses performed can be found in the European Commission report (2015) and in the related working papers by Sirtori et al (2017) and Florio et al (2017). Here our aim is not to discuss in detail the theory of each policy intervention and the results of our evaluation.…”