International Association for Management of Technology IAMOT 2015 Conference Proceedings Page 1 of 20 CROSS-DISCIPLINARY METHODOLOGY FOR DETECTION OF COLLABORATIVE AND COMPETITIVE CANDIDATES SHINO IWAMI The University of Tokyo, Graduate School of Engineering, Japan iwami@ipr-ctr.t.u-tokyo.ac.jp (Corresponding) KYOKO KOGO Dai Nippon Printing Co., Ltd., Advanced Business Center, Japan FRANCISCO TACOA The University of Tokyo, Graduate School of Engineering, Japan JUNICHIRO MORI The University of Tokyo, Graduate School of Engineering, Japan jmori@ipr-ctr.t.u-tokyo.ac.jp YUYA KAJIKAWA Tokyo Institute of Technology, Graduate School of Innovation Management, Japan kajikawa@mot.titech.ac.jp ICHIRO SAKATA The University of Tokyo, Graduate School of Engineering, Japan isakata@ipr-ctr.t.u-tokyo.ac.jp ABSTRACT To detect unknown chances, several previous researches using text-based similarity are analyzed between different datasets – academic papers and patents in the same domain, papers about a social issue and a technical solution, and holding patents and patents of targeted market. In these researches, text-based analyses by fields are performed, but text-based analyses by countries were unimplemented. The latter are in high demand because many policy-makers and business-planners think by country from our several meetings with them. The purpose of this research is to establish and evaluate our methodology to detect potential collaborative and competitive countries in science and technology. The proposed methodology is to compare three relations: text-based similarities, numbers of citations and numbers of international co-authors. Text-based similarities mean both of explicit and implicit connections. Numbers of citations and numbers of co-authors mean explicit connections. Thus, the gaps between text-based similarities, numbers of citations and numbers of co-authors will indicate implicit relations. Numbers of citations include both of affirmative and critical relations. Numbers of co-authors indicate collaborative relations. After we collected two datasets of academic papers from Web of Science, we performed the network analysis and divined into several clusters. And then, we calculated scores of three relations by countries or clusters. After clusters of two datasets are identified, we apply our methodology to overview our sciences and technologies, find chances for actual use, and detect potential collaborative and competitive fields and countries, along the scenario how the University of Tokyo will contribute to infectious diseases. For example, sciences and technologies about influenza in the University of Tokyo would