A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell

Authors:
Woondong Yeo;Seonho Kim;Byoung-Youl Coh;Jaewoo Kang
Affiliations:
Department of Computer Science and Engineering, Korea University, Seoul, Korea 136-713 and Technology Opportunity Research Team, Korea Institute of Science and Technology Information, Seoul, Korea ...;Technology Opportunity Research Team, Korea Institute of Science and Technology Information, Seoul, Korea 130-741;Technology Opportunity Research Team, Korea Institute of Science and Technology Information, Seoul, Korea 130-741;Department of Computer Science and Engineering, Korea University, Seoul, Korea 136-713
Venue:
Scientometrics
Year:
2013

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Abstract

Small and medium-sized enterprises (SMEs) are more important today than in the past, due to their capabilities of creating jobs and boosting the economy. SMEs need continual innovation to survive in a competitive market and to continue growth. But SMEs suffer from the lack of information to generate innovative ideas. The objectives of this study are to suggest a new method to recommend promising technologies to SMEs that need "knowledge arbitrage" and to help SMEs come up with ideas on new R&D. To this end, this study used three analytic techniques: co-word analysis, collaborative filtering, and regression analysis. The suggested method is tested to assure its usefulness by the real case of knowledge arbitrage from LCD to Solar cell. The main contribution of this study is that it is the first to suggest the new method using recommendation algorithm (collaborative filtering) for SMEs' knowledge arbitrage.