JOURNAL ARTICLE
Exploring Technology Opportunities with a Deep Learning-Based Clustering Approach: The Case of Pyrolysis Technology for Recycling Plastic Wastes.
Published In: International Journal of Innovation & Technology Management, 2024, v. 21, n. 3. P. 1 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Wang, Ming-Yeu 3 of 3
Abstract
With the rapidly changing business environment, the dynamic capability of an enterprise begins with sensing and grasping technology opportunities. Considering that the knowledge gaps between science and technology hinder the exploration of technology opportunities, previous studies proposed methods to extract technology opportunities by identifying the knowledge gaps between science and technology literature. This study improves upon previous methods for identifying knowledge fields contained in the literature by introducing a deep learning-based clustering approach. This study also proposes a way to reduce experts' workloads in determining gaps in the field between science and technology literature. The key idea of the proposed method is to include technologies other than the technology in question. Additionally, pyrolysis technology can convert plastic wastes into useful materials and is considered a green technology. Therefore, this study applies our improved method to explore the technology opportunities of pyrolysis technology. The results show that our method can effectively identify technology opportunities. In the case of plastic wastes, the results show that recovering carbon fibers from plastic wastes and recycling metals from e-waste plastics are expected to be prevailing technological fields. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Innovation & Technology Management. 2024/05, Vol. 21, Issue 3, p1
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2024
- ISSN:0219-8770
- DOI:10.1142/S0219877024500238
- Accession Number:177678894
- Copyright Statement:Copyright of International Journal of Innovation & Technology Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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