Network data envelopment analysis: A review

R&D (Research and Development) activities represent the basic core of corporate science and technology activities, and play a crucial role in enhancing the ability of companies to achieve rapid and sustainable growth. In recent years, the total R&D investments in China have increased significantly and the proportion of the industrial investments in R&D activities relative to national R&D investments has increased rapidly. In order to investigate the effectiveness of these R&D investments, we utilize Data Envelopment Analysis (DEA) models to evaluate the relative efficiencies of 30 regional R&D investments using the First Official China Economic Census Data in 2004. Our investigation and study indicate the following: (1) Only six provinces are global technical efficient and the performance of regional R&D investments in China needs to improve dramatically. (2) Increasing returns to scale have not yet occurred in any province. Constant returns to scale have prevailed in most provinces in the Western region, and decreasing returns to scale have prevailed in most provinces in the Eastern and Central regions. (3) There are no direct relationships between global technical efficiency and the amount of R&D investment. The Western region has the highest average radial efficiency score, followed by the Eastern region, and then by the Central region. (4) The Eastern region has advantages in local technical efficiency, the Western region has advantages in scale efficiency, while the Central region has neither technical efficiency advantages nor scale efficiency advantages. Suggestions are proposed to improve efficiencies of regional R&D investments.

The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data

Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach

Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"

A unified classification of two-stage DEA models

Although prior research has addressed the influence of production activity and research and development (R&D) on productivity, it is not clear whether production and R&D affect the market value of a firm. This study proposes and verifies an R&D value chain framework to explore the relationship among productivity, R&D, and firm market values, as measured by Tobin's q theory. By doing so, we attempt to link new theoretical insights and empirical evidence on the effects of R&D efforts and basic production activities to the market valuations of high-technology firms. The value chain data envelopment analysis approach was proposed to estimate parallel-serial processes of basic operations and R&D efforts. This approach can be used to simultaneously estimate the profitability efficiency and marketability efficiency of high-technology firms. This area has rarely been studied, but it is particularly important for high-technology R&D policies and for further industrial development. Using the R&D value chain perspectives of model innovations and extensions proposed in several previous studies, we examined the appropriate levels of intermediate outputs. Production efficiency and R&D were combined to estimate the appropriate levels of intermediate outputs for high-technology firms. Based on the intermediate output analyses, we developed an R&D efforts decision matrix to explore and identify operational and R&D efficiency for high-technology firms. Our sample firms are displayed on a four-quadrant action grid that provides visual information on current short-term operational efficiency and decision making on long-term R&D strategic positions. The empirical findings from the R&D value chain model can provide information for policymakers and managers and suggest the adoption of various policies that place more emphasis on profitability and marketability strategies.

Yung-Hsiang Lu

Papers

R&D, productivity, and market value: An empirical study from high-technology firms