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The National Innovation System in a catching-up country: empirical evidence based on micro data of a triple helix in Poland

Author

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  • Arkadiusz Œwiadek

    (University of Zielona Góra, Poland)

  • Piotr Dzikowski

    (University of Zielona Góra, Poland)

  • Jadwiga Gor¹czkowska

    (University of Zielona Góra, Poland)

  • Marek Tomaszewski

    (University of Zielona Góra, Poland)

Abstract

Research background: There are two main directions for the research of the national innovation system (NIS): the international comparison of macro data from national statistic offices or specific micro research restricted mostly to analysing selected issues. There is a lack of empirical studies regarding the national innovation system as a whole based on micro raw data and using statistical models. Purpose of the article: To identify and evaluate the impact of the triple helix (an input and output approach) on the NIS in Poland, including internal interactions between industry, science and government. Methods: A questionnaire surveys were conducted by the authors over the past five years in 6,284 manufacturing enterprises. The multifactor stepwise logistics regression forward was used to evaluate what, where and how effectively the NIS institutions in catching-up and medium-sized countries such as Poland are working. Findings & value added: The NIS of Poland is a complicated and non-mature system. Some parts of the network are effective, while others are not. It is noted that cooperation between enterprises stimulates innovation to a greater extent than cooperation with scientific institutions and public administration. The vertical supply chain is the main driver of NIS in Poland. Domestic scientific institutions have an impact that is often short-term, fragmented, and non-continuous, though it can be strong from time to time. Strangely, organizations with low knowledge potential support industry innovation activity more efficiently and in a more organised way than science institutes, excluding foreign ones. For catching-up countries, this is an important bridging of the knowledge gap - it turns out that the quantity and quality of domestic knowledge in the national innovation system is inefficient. Scientific institutions need to achieve sufficient critical mass to stimulate innovative activity. The value of the conclusions is underlined by the fact that the analyses were based on micro data, which allowed to capture the relationships between the different elements of the triple helix.

Suggested Citation

  • Arkadiusz Œwiadek & Piotr Dzikowski & Jadwiga Gor¹czkowska & Marek Tomaszewski, 2022. "The National Innovation System in a catching-up country: empirical evidence based on micro data of a triple helix in Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 13(2), pages 511-540, June.
    • Handle: RePEc:pes:ieroec:v:13:y:2022:i:2:p:511-540
    DOI: 10.24136/oc.2022.016
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    More about this item

    Keywords

    national innovation system; triple helix; industry; catching-up country; Poland;
    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
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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