A Trajectory Based Collaborative Filtering Approach to Support Firm Technological Development Forecasting
| Autor: | Wen-Heng Qiu, 丘文恒 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Technological rivalry is recognized as a key dimension of competition and innovation strategies in the digital era. One of the most important tasks in the technological development is determining the R&D directions, taking into consideration the firms’ present constraints, such as the costs of R&D. Forecasting a firm’s own or competitors’ technological development, can improve a firm’s R&D practicality and performance while reducing the level of R&D investment risk. Moreover, combining original scientific effort and technological development forecasting, a firm can try the best to avoid blind spot of research works. A firm can also take the advantage of technological development forecasting to merge or acquire other companies that are active in complementary technological fields. In addition, it can also block the commercial endeavors of rivals and by preempting substitute inventions (fence strategy), to avoid the risk of hold-up by other technology owners, or as a bargaining chip in litigation and cross-licensing (play strategy). In this paper, we present a technological trajectory-based collaborative filtering algorithm to support firm technological development forecasting, which outperforms the performance benchmarks. We take a firm’s research order into account and attempt to explore different values of Firm-IPC table and its corresponding quality of recommendations. This approach can give a simple and easy-to-implement way to forecast a firm’s own or competitor’s research orientation by only using publicly available patent data. It is also an efficient, low-cost way for complementing the traditional text mining-based approach. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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