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Product opportunity identification based on internal capabilities using text mining and association rule mining

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Listed:
  • Seo, Wonchul
  • Yoon, Janghyeok
  • Park, Hyunseok
  • Coh, Byoung-youl
  • Lee, Jae-Min
  • Kwon, Oh-Jin

Abstract

Identifying new product opportunities must be a prerequisite for a firm's sustainable growth since it can help create new market segments. In this regard, a number of studies have attempted to suggest systematic methods to discover new technological opportunities. However, from these methods, it is difficult to figure out which products can come into the market as a result of the technological opportunities. Moreover, they have tried to measure generic potential values without considering a specific target firm so it is hard to judge whether the discovered opportunities are technically feasible to the target firm. These problems tend to reduce the practicality of the discovered technological opportunities. Therefore, this paper proposes a systematic approach to identify potential product opportunities by reflecting the target firm's internal capabilities. The capabilities are inherently unobservable so we need to figure out substitutes for the firm's capabilities. The existing products belonging to a firm can be generally a basis for developing new products. The firm is already good at dealing with the existing products so we consider the firm's existing product portfolios its internal capabilities. We first extract product information from patent database using text mining technique, and then generate product connection rules represented as directed pairs of products. Finally, we evaluate potential value of product opportunities taking into account a firm's internal capabilities. An empirical study is conducted to show the applicability of the presented approach using patents granted in the United States Patent and Trademark Office during 2009 and 2013. We expect that our approach can facilitate product-oriented R&D by presenting a front-end model for new product development and deriving feasible product opportunities according to the target firm's internal capabilities. Moreover, the presented systematic approach can be a basis for an R&D planning system that can help R&D planners in performing product-oriented technology planning activities.

Suggested Citation

  • Seo, Wonchul & Yoon, Janghyeok & Park, Hyunseok & Coh, Byoung-youl & Lee, Jae-Min & Kwon, Oh-Jin, 2016. "Product opportunity identification based on internal capabilities using text mining and association rule mining," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 94-104.
    • Handle: RePEc:eee:tefoso:v:105:y:2016:i:c:p:94-104
    DOI: 10.1016/j.techfore.2016.01.011
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    References listed on IDEAS

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    1. Sung-Seok Ko & Namuk Ko & Doyeon Kim & Hyunseok Park & Janghyeok Yoon, 2014. "Analyzing technology impact networks for R&D planning using patents: combined application of network approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 917-936, October.
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    6. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
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    Cited by:

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    2. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    3. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    4. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    5. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    6. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    7. Jinzhu Zhang & Wenqian Yu, 2020. "Early detection of technology opportunity based on analogy design and phrase semantic representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 551-576, October.
    8. Mun, Changbae & Kim, Yongmin & Yoo, Donghyun & Yoon, Sejun & Hyun, Heesu & Raghavan, Nagarajan & Park, Hyunseok, 2019. "Discovering business diversification opportunities using patent information and open innovation cases," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 144-154.
    9. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    10. Yuyao Guo & Lei Wang & Zelin Zhang & Jianhua Cao & Xuhui Xia, 2023. "Association Rule Mining-Based Generalized Growth Mode Selection: Maximizing the Value of Retired Mechanical Parts," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    11. Lee, Jiho & Ko, Namuk & Yoon, Janghyeok & Son, Changho, 2021. "An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    12. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    13. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    14. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    15. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    16. Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    17. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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