Rare Substitution, Rarer Convergence: A Novel Approach to Measuring Knowledge Coupling between Emerging and Traditional Industries
Author(s)
Wang, Jiajie
Li, Tianyi
Liu, Yijia
Cheng, Xiaoling
Issue Date
2024-03-20
Keyword(s)
Knowledge Coupling
Emerging Industry
Traditional Industry
Convergence
Substitution
Abstract
This study embarks on a critical task of quantifying knowledge coupling between emerging and traditional industries, a cornerstone for understanding synergistic innovation mechanisms. While prior research has largely concentrated on modernizing traditional industries and fostering emerging industries, the dynamics of knowledge interaction through innovation diffusion have remained underexplored. Our research presents a novel approach for measuring this coupling at the knowledge network level, leveraging the Hidden Markov Model (HMM) to identify two distinct patterns: substitution and convergence. We underscore that while substitution is commendable, convergence is rarer yet demonstrates superior knowledge interactive performance. This finding suggests convergence as a less common but more impactful form of industry interaction. The empirical examination involving the new-generation IT industry and traditional manufacturing sector validates our method's efficacy, revealing nuanced interactions and contributing significantly to the existing methodologies in interindustry knowledge network analysis. Our findings provide not just theoretical insights but also practical guidance for fostering meaningful and effective collaborations between diverse industrial sectors.
Publisher
iSchools
Series/Report Name or Number
iConference 2024 Proceedings
Type of Resource
Other
Language
eng
Handle URL
https://hdl.handle.net/2142/122783
Copyright and License Information
Copyright 2024 is held by Jiajie Wang, Tianyi Li, Yijia Liu, Xiaoling Cheng, Li Zhang. Copyright permissions, when appropriate, must be obtained directly from the authors.
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