지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called “all possible DEAs”, for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : “R&D manpower” (χ₂) and “Sales revenue” (y₁). However, it should be pointed out that the input variable “R&D funds” (χ₁) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sponsored R&D project from a practical point of view a priori. In this context, if practitioners’ top priority is to see the efficiency between “R&D funds” (χ₁) and “Sales revenue” (y₁), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners’ expectations and DEA efficiency scores.
목차
1. Introduction
2. Background and Theory
3. Design of Experiments
4. Analysis of Experiments
5. Practical Implications and Conclusions
References
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UCI(KEPA) : I410-ECN-0101-2015-500-001128258