# Efficiency of Public R&D Management Agency and Its Improvement toward Open Innovation
## 2 Research Background and Methodology
### Research Methods  
This study intends to present possible ways to improve the operational efficiency of planning and evaluation costs of R&D management agencies.  
To this end, first, it intends to analyze the structural problems by examining the regulatory system such as the current status of planning and evaluation cost of 17 R&D management agencies and related regulations from 2016 to 2020. For data collection, we carried out a survey from 1 April 2020 until 30 May 2020. In addition, this study referred to the results of an unstandardized interview held during the above survey period. Second, DEA analysis is used to measure the causes of relative efficiency and inefficiency of R&D management agencies, and to identify factors that appear to be over-input through the existence of congestion.  
Meanwhile, input and output variables of R&D management agencies were selected for DEA analysis. In the case of R&D management agencies, it is difficult to set general R&D performance indicators, considering that most of the research outputs are the results of unsigned management rather than the results of their own research. In consideration of these characteristics, [PERSON] et al. (2011) [23] also selected variables in a real unit related to the operation of R&D management agencies. As input factors, manpower and planning and evaluation costs were selected, and as output factors, project cost and number of projects were selected. In this study, variables were selected based on the operational characteristics of R&D management agencies and prior research. As shown in Table 2 below, as input variables, manpower dedicated to planning, evaluation, and management were selected as labor input, and planning and evaluation cost as capital input, while project cost and number of projects are chosen as yields for output variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Type**} & \multicolumn{3}{c}{**Input Variables**} & \multicolumn{2}{c}{**Output Variables**} \\ \hline \multirow{2}{*}{**Ministry**} & \multirow{2}{*}{\begin{tabular}{c} **R\&D** \\ **Management Agency** \\ \end{tabular} } & \begin{tabular}{c} **Manpower** \\ **(FTE)** \\ \end{tabular} & \begin{tabular}{c} **Planning and** \\ **Evaluation Cost** \\ \end{tabular} & \multirow{2}{*}{**Project Cost**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Projects** \\ \end{tabular} } \\ \hline \begin{tabular}{c} Ministry of \\ Science and \\ ICT(MSIT) \\ \end{tabular} & \begin{tabular}{c} NRF \\ \end{tabular} & 252 & 88,895 & 10,864,546 & 521 \\ \cline{2-6}  & \begin{tabular}{c} IITP \\ \end{tabular} & 229 & 48,070 & 1,087,592 & 124 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Ministry of Trade, \\ Industry, and Energy (MOTIE) \\ \end{tabular} } & \begin{tabular}{c} KEIT \\ \end{tabular} & 257 & 86,090 & 2,038,812 & 190 \\ \cline{2-6}  & \begin{tabular}{c} KIAT \\ \end{tabular} & 182 & 85,860 & 1,840,144 & 310 \\ \cline{2-6}  & \begin{tabular}{c} KETEP \\ \end{tabular} & 123 & 28,594 & 772,757 & 196 \\ \hline \
\begin{tabular}{c} Ministry of SMEs and \\ Startups (MSS) \\ \end{tabular} &
\begin{tabular}{c} TIPA \\ \end{tabular} & 138 & 48,814 & 1,142,590 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics by input and output variables of R&D management agencies (2020). (Unit: person, KRW million).  
In addition, this study intends to conduct an input-based efficiency analysis considering that the output variable is the result of the previous year's performance and is confirmed at the beginning of the year due to the nature of a R&D management agency. Both the CCR model and the BCC model were used to infer inefficiency as well as efficiency values, and to analyze the profitability of scale.