Science and technology (S&T) constitute significant forces in shaping innovation, yet empirical investigations of their interaction and convergence fostering technological advancement remain limited.
This study aims to delineate the multidimensional dynamics of S&T interactions guided by synergetics principles, including strength, time-lag, depth, and synchronization, and explore their influence on technological innovation. It also examines the moderating impact of technological topic divergence.
Utilizing a knowledge network representation framework, we conducted an examination of papers and patents within the artificial intelligence domain, spanning the period from 2000 to 2022. Our findings indicate that the strength, time-lag, and synchronization of S&T interactions positively correlate with technological innovation.
Strong interaction between technology with deep-level scientific knowledge facilitates technological innovation and vice versa inhibits it. Moreover, the impact of S&T interactions on innovation tends to be greater in domains with higher technological topic popularity and centrality. By elucidating these associations, this study contributes to the methodological understanding of S&T intrinsic interactions and furnishes valuable insights for R&D organizations in formulating strategic S&T decisions.
This study addresses the above issues and organizes the program as follows. First, we conduct a comprehensive review of the literature on S&T interaction and innovation, consolidating and analyzing their conflicting conclusions. Then, we propose four dimensions of indicators- interaction strength, time-lag, depth, and synchronization - to gauge variations in S&T interactions. Utilizing a knowledge network coupling algorithm, we derive yearly interaction dynamics of these dimensions. Subsequently, we introduce a hypothetical model to elucidate potential mechanisms and processes influencing innovation impact. Empirical testing of our hypotheses leverages a dataset of 788,136 papers and 141,902 patents sourced from the Web of Science and the US Patent and Trademark Office. Finally, we conclude and discuss the implications of technological innovation.
Our work contributes to two main areas. First, we introduce a novel multidimensional interaction measure for S&T integrating natural language processing and complex network modeling applied to S&T texts. This approach addresses linear association concerns regarding citation linkages, the International Patent Classification - Institute for Scientific Information (IPC-ISI) classification system, and lexical- or topic-based similarity. By discerning various S&T interaction features, we bridge the gap between literature on science convergence and technology convergence, shedding light on technology innovation impacts via synergies and slaving principles.
Through empirical tests across four S&T interaction dimensions, we identify factors influencing innovation and enhancing the understanding of S&T interactions. Second, our study highlights Piaget’s theory of genetics and environmental applicability in technological innovation. We validate divergence significance in subdomain topics within S&T interactions, particularly the influence of subdomain popularity on innovation. These findings inform strategic R&D organizations and government policymaking.
Vists : https://innovatorawards.org/
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