Abstract
The industry–academic collaboration (IAC) in developed and developing countries enables these economies to gain momentum in continuous innovation and, thus, economic growth. Patent commercialization is one major channel of knowledge flow in IAC. However, very few studies consider the flow of knowledge between industrial firms and universities. Moreover, ways that the patent commercialization performance of IACs can be evaluated are rarely discussed. Therefore, defining an analytic framework to evaluate the performance of IAC from the aspect of patent commercialization is critical. Traditionally, data envelopment analysis (DEA) models have widely been adopted in performance evaluation. However, traditional DEA models cannot accurately evaluate the performance of IACs with complex university–industry interconnections, the internal linkages, or linking activities of knowledge-flow within the decision-making units (DMUs), i.e., the IACs. In order to solve the abovementioned problems, this study defines a multiple objective programming (MOP)-based network DEA (NDEA), with weighting derived from the decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (ANP), or the DANP. The proposed analytic framework can evaluate the efficiency of decision-making units (DMUs) with a network structure (e.g., supply chains, strategic alliances, etc.) based on the weights that have been derived, based on experts’ opinions. An empirical study based on the performance of the patent commercialization of Taiwanese IACs was used to demonstrate the feasibility of the proposed framework. The results of the empirical research can serve as a basis for improving the performance of IAC.
Highlights
In the era of the knowledge-based economy, knowledge has become the major factor driving the economic growth of major and fast-catching-up minor economies
We cannot ignore the role of the intermediate data when we evaluate the relative efficiency of decision-making units (DMUs), in the case of multiple inputs and multiple outputs [107]
To provide a more precise performance evaluation of industry–academic collaboration (IAC), we suggest that decision-makers should use the network DEA (NDEA)-multiple objective programming (MOP) model
Summary
In the era of the knowledge-based economy, knowledge has become the major factor driving the economic growth of major and fast-catching-up minor economies. Over the past few decades, pharmaceutical, engineering, and software firms have increasingly enhanced their cooperation with universities [2]. In addition to their own research and development (R&D), firms need universities’ R&D of new technologies. The motivations of policymakers and higher-education institutes to develop “third missions” besides the two conventional basic missions of lecturing and academic research, and to commercialize academic knowledge through various approaches like patent applications and commercialization, technology licensing and transfer, incubation has strengthened the appropriateness of IACs [4,5,6]. IAC promotes technological innovation, and the transfer of scientific technology can be promoted through the acquisition and use of knowledge created by the IAC, innovative start-ups, and spin-offs [7]
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