Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model

Hyejin Jang, Suyeong Lee and Byungun Yoon

Technovation, 2023, vol. 126, issue C

Abstract: Recently, as the short life cycle of technology entails the introduction of various new technologies, the importance of technological evolution has been emphasized. Although many researchers have tried to study technological evolution, their works have some limitations because they must consider not only technology evolution but also social evolution in a social system, such as social institutions, markets, and customer acceptance. In addition, previous techno-socio co-evolution studies mostly focused on case analyses based on a theoretical research framework. Thus, in this study, we propose a framework for analyzing co-evolution in terms of the technological-social aspect using topic modeling to analyze the evolutionary patterns of social and technological topics and performing time-series analysis of co-evolution using the HMM model based on the contents of technology and society. In this study, we collected technology-specialized news articles for 20 years for autonomous vehicles, which have been the biggest issue in technology and society in recent years. Using the HMM-based on the derived technological-social anchored topic, we derived the techno-socio co-evolution sequence and analyzed the evolutionary pattern of technological development after the emergence of new technology and the techno-socio co-evolution pattern leading to policy establishment. The techno-socio co-evolution pattern derived from this study will contribute to technology planning and policy establishment.

Keywords: Technology intelligence; Patent analysis; Natural language processing; Deep learning; Data-driven; Techno-socio co-evolution (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:techno:v:126:y:2023:i:c:s0166497223001244

DOI: 10.1016/j.technovation.2023.102813

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