본 연구는 K연구원의 상향식 R&D과제기획 차원의 신규 연구기획과제 선정 평가를 위한 평가도구 개발에 목적을 두고 진행하였다. 이를 위해 CIPP모형과 연구기획평가를 위한 선정평가 및 평가지표에 관한 선행연구를 중심으로 R&D과제기획 선정평가 항목과 문항을 개발 한 후, 2회에 걸친 델파이 조사를 실시하였다. 개발된 평가도구는 13명의 전문가를 대상으로 설문조사를 실시하여 내용타당도, 합의도 및 수렴도를 검증하였다. 최종 선정된 R&D과제기획 선정평가 도구는 8개 항목에 총 21개 문항으로, 상황평가 5 문항, 투입평가 2문항, 과정평가 8문항, 산출평가 6문항으로 구성되었다. 개발된 평가 도구는 상향식 기획 과정상의 문제점을 해소하고 연구자들의 기획역량을 제고하는 데 기여할 것이다. 또한, 선정 평가 시 평가에 대한 일관성과 효율성 제고에 기여할 것이다.

In this study, the factors affecting the efficiency of 48 projects of private R&D institutes were analyzed using the Tobit model. Influencing factors were selected as open R&D network size, IT industry, interaction between R&D network size and IT industry, and type of R&D network cooperation. As a result of Tobit analysis, the R&D network size, the IT industry, and the type of R&D network cooperation were found to be significant. The larger the open R&D network size, the lower the efficiency, and the IT industry showed lower R&D efficiency than other industries. In addition, cooperation with universities and research institutes showed lower R&D efficiency than cooperation with companies. As a result of these studies, companies will be able to select and focus on cooperation with the outside in relations and investment allocation.

The current environment of technological and competitive changes influences not only the business R&D environment but also government driven national R&D strategies. Open innovation has now become an important paradigm that is replacing the outdated paradigm of closed innovation. Many companies and nations have been increasing R&D investment because R&D has been considered a driving force for national and corporate competitive advantage. The purpose of this paper is to evaluate and compare the performance of R&D focused on open innovation according to scientific and technological outputs which is based on paper publications, patents and etc. Comparisons should not be only based on the quantity but also on the quality of the output. This paper shows that it is possible to develop DEA models that utilize the Analytical Hierarchical Process in order to transform the qualitative index into a quantitative index. Hence, the relative efficiency for R&D organizations is obtained based on both quantity and quality outputs and subsequently provides comprehensive and realistic methods for decision makers to identify levels of project efficiency.