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Integrating quantitative and qualitative methodologies to build a national R&D plan using data envelopment analysis
1Science and Technology Management Policy, University of Science & Technology, Daejeon Metropolitan City, Republic of Korea.
Plos One
|March 11, 2022
Summary
Government R&D investment decisions for smart farms can be distorted. This study proposes a quantitative approach integrating expert opinions to improve R&D performance and budget allocation for policymakers.
Area of Science:
- Agricultural Science
- Economics
- Public Policy
Background:
- National R&D investment is increasing, necessitating improved decision-making systems for R&D performance and budget efficiency.
- Effective allocation of limited national R&D resources requires sound decision-making at both the selection and pursuit stages.
Purpose of the Study:
- To evaluate the theoretical efficiency of R&D investment in smart farm technologies.
- To compare quantitative R&D investment analysis with expert opinions reflecting real-world conditions.
- To provide policymakers with a quantitative approach for R&D investment decisions, incorporating field experiences.
Main Methods:
- Data Envelopment Analysis (DEA) with an Assurance Region model.
- Integration of Analytic Hierarchy Process (AHP) with DEA.
- Incorporation of expert opinions to refine R&D investment priorities.
Main Results:
- R&D actor perspectives (academia, industry, research) did not significantly influence investment allocation decisions in the smart farm sector.
- The relative efficiency of certain R&D technologies increased when expert qualitative insights were integrated.
- Government top-down decision-making was found to potentially distort R&D investment allocation, excluding necessary technology groups.
Conclusions:
- A quantitative approach integrating expert opinions can compensate for distortions in top-down national R&D investment decisions.
- The Korean government's R&D investment priorities in smart farms showed considerable distortion when compared to expert-informed analysis.
- This study offers an alternative methodology for policymakers to ensure more accurate and effective R&D investment allocation.
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