The mismatch between demand and beneficiaries of R&D support programs for SMEs: Evidence from Korean R&D planning programs
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DOI: 10.1016/j.techfore.2016.10.007
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Cited by:
- Hyensup Shim & Kiyoon Shin, 2021. "Empirical Analysis of Evidence-Based Policymaking in R&D Programmes," Sustainability, MDPI, vol. 14(1), pages 1-15, December.
- 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).
- 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).
- 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.
- 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.
- 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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Keywords
R&D planning; Efficiency and mismatch of policy; Data mining and profiling; Demand forecasting model; Evidence based decision making;All these keywords.
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