- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
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.
This study has proposed a systematic approach to identifying potential areas for concentric diversification. The proposed approach is based on the premise that significant technological relationships between products extracted from large-scale quantitative databases can provide valuable information on the feasibility of concentric diversification from one area to another. Our case study shows that the proposed approach enables a wide ranging search for potential areas for concentric diversi- fication and the quick assessment of their characteristics, with analysis results that are statistically significant.