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

Woondong Yeo, Seonho Kim, Byoung Youl Coh, Jaewoo Kang

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)589-604
    Number of pages16
    JournalScientometrics
    Volume96
    Issue number2
    DOIs
    Publication statusPublished - 2013 Aug

    Keywords

    • Co-word analysis
    • Collaborative filtering
    • Emerging technology
    • Knowledge arbitrage
    • Promising technology
    • Small and medium-sized enterprises (SMEs)

    ASJC Scopus subject areas

    • Social Sciences(all)
    • Computer Science Applications
    • Library and Information Sciences

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