Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain |
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| Institution: | 1. Department of Mathematics, College of Science, Arak Branch, Islamic Azad University, Arak, Iran;2. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141, USA;3. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, D-33098 Paderborn, Germany;4. Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada;5. Polo Tecnologico IISS G. Galilei, Via Cadorna 14, 39100, Bolzano, Italy;6. Faculty of Economics and Management, Free University of Bolzano, Bolzano, Italy;1. The George Washington University, Washington, DC 20052, USA;2. Technical University of Crete, School of Production Engineering and Management, Chania, GR73100, Greece;3. University of Ioannina, Department of Economics, GR45110, Ioannina, Greece |
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| Abstract: | The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency. |
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| Keywords: | Innovation value chain Shared resource High-tech industry innovation Data envelopment analysis |
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