The yearly increase in government R&D investment and top-down national R&D investment allocation requires a more quantitative decision-making system that maximizes R&D performance and efficient budget allocation. Sound decision-making is necessary at both the selection stage and the pursuit stage in order to maximize limited national R&D resources. We study Korean smart farms as an example to examine national R&D investment from the various R&D actors (academia, industry, and research institutes) perspectives. The objective of our research is to evaluate the theoretical efficiency of R&D investment on specific technologies in smart farms and compare the results with expert opinions where the reality is reflected. To be specific, our study is to provide the quantitative approach in making decision among policymakers by reflecting the field experiences and opinions. We use a data envelopment analysis with an assurance region model, which integrates an analytic hierarchy process and a data envelopment analysis. The weights of output in DEA model by the R&D actors are similar to the overall weight by all actors, implying that investment allocation decisions in the smart farm sector are not significantly affected by the R&D actors. We realized that the relative efficiency of some R&D technologies increases after reflecting qualitative ideas of experts. In reality, it is necessary to invest in these technology groups, but they excluded from top-down decision-making. This also shows that a government’s top-down decision-making can distort its investment allocation. This study proposes a new approach to compensate for the difference between theoretical virtual prices and actual prices in data envelopment analysis. In particular, when comparing the only quantitative results on investment priorities with analysis results by additionally reflecting the opinions of experts in each sector, we found that the Korean government’s investment priorities in the smart farm field are considerably distorted. Therefore, this study is expected to be used as an alternative for policy makers to compensate for the quantitative distortion might be caused by top-down national R&D investment decisions.

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

Precision medicine has received a lot of attention in recent years and we have not yet found any research cases that apply Data Envelopment Analysis (DEA) to investment decision making in this area. The purpose of this study is to analyze the relative efficiency of candidate technology sectors in order to determine priorities for government investment in precision medicine. The results of the efficiency analysis can be used as an important reference for government policy makers to determine the amount of government investment in the next year for each candidate technology sector. The candidate technology for government investment in precision medicine was decided for 23 sectors based on the data analysis and the opinions of expert committees. This study applies the input-oriented DEA in regard to 23 technology sectors, which is widely used to analyze relative efficiency in terms of inputs versus outputs and to enhance efficiency through the propositional reduction of inputs. The input variables include the government’s research and development (R&D) investment and forward and backward industry linkage effects. The output variables are the employment creation effect, value-added effect, number of Korean patents, and number of Korean papers. Our analysis results show that the 23 technology sectors in precision medicine overall have a high efficiency, with the exception of the biobank technology sector. Therefore, since the Biobank technology sector has strong infrastructure characteristics, it seems to require continuous investment. The efficiency of DEA is high in most precision medicine sectors; therefore, overall, investing in these technologies is expected to yield good benefits.