Article
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Feature Distributions of Technologies
Version 1
: Received: 18 June 2024 / Approved: 18 June 2024 / Online: 18 June 2024 (16:15:59 CEST)
How to cite: Zhu, J.; Deng, C.; Pan, J.; Gu, F.; Guo, J. Feature Distributions of Technologies. Preprints 2024, 2024061235. https://doi.org/10.20944/preprints202406.1235.v1 Zhu, J.; Deng, C.; Pan, J.; Gu, F.; Guo, J. Feature Distributions of Technologies. Preprints 2024, 2024061235. https://doi.org/10.20944/preprints202406.1235.v1
Abstract
The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.
Keywords
2D analytical space; commerciality; disruptiveness; representative keyword; search engine; significance; technological assessment; versatility
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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