A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods

A DANP-Based NDEA-MOP Approach to Evaluating the Patent Commercialization Performance of Industry–Academic Collaborations

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.