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Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining

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  • Wu, Yingwen
  • Ji, Yangjian

Abstract

Technology opportunity discovery (TOD) is a starting point for firms to conduct research and development (R&D) activities. Although researchers have suggested diverse methods for studying firm-specific TOD, there is a lack of research that makes the best use of competitors’ roles to identify technology opportunities. In addition, the evaluation of candidate technology opportunities only focuses on the current potential of technologies. Therefore, this paper proposes a novel approach to identifying technology opportunities from the perspective of competitors using an improved association rule mining (ARM) algorithm named Two-Phase Frequent Item Mining-Customised Association Rule Mining (TPFIM-CARM). The proposed approach involves: 1) mining frequent itemsets of technologies from the patent dataset of the target firm's competitors, 2) discovering strong association rules of technologies and recommending candidate technology opportunities based on the technology portfolio of the target firm, and 3) evaluating technology opportunities by predicting indicators of technology importance and technology effects with polynomial ridge regression. The effectiveness of the proposed approach is validated by a case study of the General Motors Company (GM). The study extends the TOD literature by identifying technology opportunities from the perspective of competitors and provides a forward-looking evaluation idea, which can be used in both TOD and other studies on technology and innovation management.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:infome:v:17:y:2023:i:2:s1751157723000238
    DOI: 10.1016/j.joi.2023.101398
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