Name
Abstract | ||
|---|---|---|
Technological substitutions play a major role in the research and development efforts of most modern industries. If timed and provisioned well, successful technology substitutions can provide significant market advantages to firms that have anticipated the demand correctly for emergent technologies. Conversely, failure to commit to new technologies at the right time can have catastrophic consequences, making determining the likely substitution mode of critical strategic importance. With little available data, being able to identify at an early stage whether new technologies are appearing in response to the perceived stagnation in existing technical developments, or as a result of pioneering leaps of scientific foresight, poses a significant challenge. This paper combines bibliometric, pattern recognition, and statistical approaches to develop a technology classification model from historical datasets where literature evidence supports mode labeling. The resulting functional linear regression model demonstrates robust predictive capabilities for the technologies considered, supporting the literature-based substitution framework applied and providing evidence suggesting that substitution modes can be recognized through automated processing of patent data. Furthermore, preliminary evidence suggests that classification can be achieved based on partial time series, implying that future extensions to real-time classifications may be possible for decision-making in the early stages of research and development. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2913615 | IEEE ACCESS |
Keywords | Field | DocType |
Adner's classification scheme,emergence,patent bibliometrics,pattern recognition,technological substitutions,technology life cycle | Computer science,Mode (statistics),Technology life cycle,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ian P. Marr | 1 | 0 | 0.34 |
| Chris McMahon | 2 | 9 | 1.52 |
| Mark H. Lowenberg | 3 | 7 | 3.63 |
| Sanjay Sharma | 4 | 13 | 1.72 |