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| Content Provider | Springer Nature Link |
|---|---|
| Author | You, Hanlin Li, Mengjun Hipel, Keith W. Jiang, Jiang Ge, Bingfeng Duan, Hante |
| Copyright Year | 2017 |
| Abstract | A forecasting methodology for technology development trends is proposed based on a two-level network model consisting of knowledge-transfer among patents and patent subclasses, with the aim to confront the increasing complex challenge in technology investment and management. More specifically, the patents of the “coherent light generators” classification, granted from 1976 to 2014 by examiners of the United States Patent and Trademark Office, are collected and with which the first-level citation network is constructed first. Then, a new approach to assess patent importance from the perspective of topological structure is provided and the second-level citation network, which consists of patent subclasses, is produced with the evaluation results. Moreover, three assessment indices of the subclass citation network are abstracted as impact parameters for technology development trends. Finally, two typical time series models, the Bass and ARIMA model, are utilized and compared for development trend forecasting. Based on the results of evolution prediction and network analysis, the highlighted patent subclasses with more development potential are identified, and the correlation between technology development opportunity and topological structure of the patent citation network is discussed. |
| Starting Page | 297 |
| Ending Page | 315 |
| Page Count | 19 |
| File Format | |
| ISSN | 01389130 |
| Journal | Scientometrics |
| Volume Number | 111 |
| Issue Number | 1 |
| e-ISSN | 15882861 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2017-02-02 |
| Publisher Place | Dordrecht |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Knowledge-transfer Patent citation network Time series model Coherent light generator technology Technology forecasting Information Storage and Retrieval Library Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Library and Information Sciences Computer Science Applications |
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