Identification of DEA Determinant Input-Output Variables : an Illustration for Evaluating the Efficiency of Government-Sponsored R&D Projects

Abstract

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” ( ) and “Sales revenue” ( ). However, it should be pointed out that the input variable “R&D funds” ( ) 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” ( ) and “Sales revenue” ( ), 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.