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

doi:10.1007/s11192-012-0935-y

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 journal › Article › peer-review

9 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 language	English
Pages (from-to)	589-604
Number of pages	16
Journal	Scientometrics
Volume	96
Issue number	2
DOIs	https://doi.org/10.1007/s11192-012-0935-y
Publication status	Published - 2013 Aug

Bibliographical note

Funding Information:
Acknowledgments This research was supported by the National Research Foundation of Korea (NRF) grant (2012R1A2A2A01014729) and the Converging Research Center Program grant through the NRF(2012M3C4A7033341) funded by the Ministry of Education, Science and Technology.

Keywords

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

ASJC Scopus subject areas

General Social Sciences
Computer Science Applications
Library and Information Sciences

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Cite this

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

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.",

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author = "Woondong Yeo and Seonho Kim and Coh, {Byoung Youl} and Jaewoo Kang",

note = "Funding Information: Acknowledgments This research was supported by the National Research Foundation of Korea (NRF) grant (2012R1A2A2A01014729) and the Converging Research Center Program grant through the NRF(2012M3C4A7033341) funded by the Ministry of Education, Science and Technology.",

year = "2013",

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doi = "10.1007/s11192-012-0935-y",

language = "English",

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AU - Kang, Jaewoo

N1 - Funding Information: Acknowledgments This research was supported by the National Research Foundation of Korea (NRF) grant (2012R1A2A2A01014729) and the Converging Research Center Program grant through the NRF(2012M3C4A7033341) funded by the Ministry of Education, Science and Technology.

PY - 2013/8

Y1 - 2013/8

N2 - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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