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• W3108875105 endingPage "103356" @default.
• W3108875105 startingPage "103356" @default.
• W3108875105 abstract "Our framework aims to support the business diversification of firms. In a dynamic business environment, tracing business trends and uncovering business diversifiable opportunities are crucial and difficult tasks for commercial firms. Although the identification of the firms’ opportunities has been attempted in several prior studies, most of them focused on opportunities from a technological perspective but not the business (or market). Particularly, these technology opportunity approaches have a fundamental limitation in that they are unable to apply service business which is hard to patent. Therefore, a trademark-based framework to uncover business opportunities using deep link prediction and competitive intelligence analysis is proposed in this study. The overall procedure of the proposed framework is as follows: 1) constructing a deep link prediction model using co-occurrences of designated goods and services of trademarks, 2) discovering diversifiable businesses, and 3) establishing business diversification strategies using competitive intelligence. Regardless of the type of business, the deep link prediction model learns business dynamics and identifies diversifiable businesses for the target firm. In particular, the proposed framework has the advantage that it can support the establishment of a business diversification strategy using competitive intelligence analysis. We expect that the proposed framework will contribute a systematic approach to identifying business opportunities based on objective data and will be used as a monitoring tool for entire business trends." @default.
• W3108875105 created "2020-12-07" @default.
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• W3108875105 date "2021-01-01" @default.
• W3108875105 modified "2023-09-29" @default.
• W3108875105 title "Trademark-based framework to uncover business diversification opportunities: Application of deep link prediction and competitive intelligence analysis" @default.
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• W3108875105 doi "https://doi.org/10.1016/j.compind.2020.103356" @default.
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