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The mismatch between demand and beneficiaries of R&D support programs for SMEs: Evidence from Korean R&D planning programs

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  • Jun, Seung-Pyo
  • Kim, Sang-Gook
  • Park, Hyun-Woo

Abstract

Many countries plan and implement various programs to provide direct and indirect support for small and medium businesses to undertake technological innovation. This study focuses on R&D planning support programs, which are one of the policies designed to provide indirect support and improve investment efficiency. This study compares cases of R&D planning support programs in Korea to identify the differences between demanding companies and beneficiary companies and evaluates whether the implementation of the policy was efficient. To compare the characteristics of differing groups of companies, we applied an analysis method based on data mining to profile the characteristics of the companies. In addition, to help improve the efficiency of policy implementation in the future, we used discriminant analysis to present a model for forecasting how high the demand for R&D planning support will be among start-up companies. Based on the model we propose in this study, companies that have experienced R&D planning support have a very different profile from those that are in need of planning support. In other words, we found a mismatch between companies that have been beneficiaries of the policy and demanding companies, those that are still in need of the support. This study proposes a demand forecasting model to redress this mismatch, which we hope will contribute to enhancing the efficiency of R&D support policies and the evidence based decision making.

Suggested Citation

  • Jun, Seung-Pyo & Kim, Sang-Gook & Park, Hyun-Woo, 2017. "The mismatch between demand and beneficiaries of R&D support programs for SMEs: Evidence from Korean R&D planning programs," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 286-298.
    • Handle: RePEc:eee:tefoso:v:116:y:2017:i:c:p:286-298
    DOI: 10.1016/j.techfore.2016.10.007
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    Cited by:

    1. Hyensup Shim & Kiyoon Shin, 2021. "Empirical Analysis of Evidence-Based Policymaking in R&D Programmes," Sustainability, MDPI, vol. 14(1), pages 1-15, December.
    2. Jun, Seung-Pyo & Lee, Jae-Seong & Lee, Juyeon, 2020. "Method of improving the performance of public-private innovation networks by linking heterogeneous DBs: Prediction using ensemble and PPDM models," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Jun, Seung-Pyo & Yoo, Hyoung Sun & Hwang, Jeena, 2021. "A hybrid recommendation model for successful R&D collaboration: Mixing machine learning and discriminant analysis," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    4. Zhao, Shukuan & Xu, Baoda & Zhang, Weiyong, 2018. "Government R&D subsidy policy in China: An empirical examination of effect, priority, and specifics," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 75-82.
    5. Sungmoon Jung & Gihyun Kwak, 2018. "Firm Characteristics, Uncertainty and Research and Development (R&D) Investment: The Role of Size and Innovation Capacity," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    6. Hyoung Sun Yoo & Chul Lee & Seung-Pyo Jun, 2018. "The Characteristics of SMEs Preferring Cooperative Research and Development Support from the Government: The Case of Korea," Sustainability, MDPI, vol. 10(9), pages 1-18, August.

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