An evaluation index system for prediction of technology commercialization of investment projects
Technology commercialization is a hot topic for governments, entrepreneurs, marketers and researchers due to the fact that its measurement is quite important for decision makers to know their possible growth potential in a dynamic and competitive environment. Having a high technology commercialization potential for invested technology is the engine of national development and growth as well. However, the measuring technology commercialization of any investment project is difficult because of the fact that there are some difficult questions, which should be answered exactly. For example, 'how will the technology be marketed in own sector?' 'Which factors are important for considered technology and affect technology commercialization?' 'Which scientific methods should be used for predication of technology commercialization potential properly?' and so on. The purpose of this study is to develop an evaluation index system for predicting the technology commercialization of the investment project. At the beginning of this study, a review of the technology commercialization literature is performed to address the factors which affect technology commercialization. Then, the evaluation index system proposed in this study can be used to prioritize investment projects in terms of their technology commercialization potential. It uses fuzzy multi-criteria decision making (FMCDM) methods and beta distribution to estimate the mean of each factor. Next, a solution approach to find an index value which shows technology commercialization potential is proposed. The proposed approach firstly finds interrelationships among the factors under fuzziness by using the fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL) method. Subsequently, it makes use of the fuzzy Analytic Network Process (Fuzzy ANP) method to determine the weights of factors. The values of all factors are then predicted by experts and assessed by beta distribution to estimate the mean of each factor with respect to the experts' opinion. Finally, a case study is presented to demonstrate the usefulness of the proposed approach. © 2012 - IOS Press and the authors. All rights reserved.
SourceJournal of Intelligent and Fuzzy Systems
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