Concentric diversification based on technological capabilities: Link analysis of products and technologies

Hyunwoo Kim, Suckwon Hong, Ohjin Kwon and Changyong Lee

Technological Forecasting and Social Change, 2017, vol. 118, issue C, 246-257

Abstract: This research responds to the needs for concentric diversification by focusing on how firms can find new business opportunities based on their technological capabilities. We propose a systematic approach to identifying potential areas for concentric diversification at a product level via link analysis of products and technologies. For this, first, text mining is utilised to construct an integrated patent-product database from the US patent and trademark database. Second, association rule mining is employed to construct a product ecology network using directed technological relationships between products. Third, a link prediction analysis is conducted to identify potential areas for concentric diversification at a product level. Finally, three quantitative indicators are developed to assess the characteristics of the areas identified. Our case study employs a total of 850,676 patents and 328,288 products in the integrated patent-product database from 2010 to 2014 and shows that the proposed approach enables a wide-ranging search for potential areas for concentric diversification and the quick assessment of their characteristics, with statistically significant results. We believe that the proposed approach will be useful as a complementary tool for decision making for small and medium-sized high-tech companies that are considering entering new business areas, but which have little domain knowledge.

Keywords: Concentric diversification; Patent-product database; Text mining; Association rule mining; Link prediction (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:118:y:2017:i:c:p:246-257

DOI: 10.1016/j.techfore.2017.02.025

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