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

Yingwen Wu and Yangjian Ji

Journal of Informetrics, 2023, vol. 17, issue 2

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.

Keywords: Technology opportunity discovery; Patent mining; Association rule mining; Technology opportunity evaluation; Polynomial ridge regression (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:17:y:2023:i:2:s1751157723000238

DOI: 10.1016/j.joi.2023.101398

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