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Paper Number
1366
Paper Type
short
Description
Artificial Intelligence (AI) has emerged as a crucial facet of contemporary technological innovation, influencing diverse domains. Consequently, understanding the diffusion and evolution of AI innovations is vital. Scholarly publications have commonly served as proxies for studying these AI innovations. However, previous studies on publication diffusion have largely overlooked the role of models, which is particularly integral for AI innovations as they bridge upstream datasets and downstream applications. Moreover, models form an interdependent network due to their combinational evolution. This paper addresses this gap, examining how the location, movement, and speed of model movement in that model network affect the dissemination of AI research. Using a four-layer network—author collaborations, paper citations, model dependencies, and keyword co-occurrences—we examine 345,383 AI papers from 2000 to 2022. This research aims to contribute to the diffusion of innovation literature and dynamic network analysis, offering several novel insights and advancements.
Recommended Citation
Gao, Kaige; Yoo, Youngjin; and Schecter, Aaron, "Models as Social Actors in the Diffusion of AI Innovations: A Multilayer, Heterogeneous, Dynamic Network Perspective" (2023). ICIS 2023 Proceedings. 6.
https://aisel.aisnet.org/icis2023/diginnoventren/diginnoventren/6
Models as Social Actors in the Diffusion of AI Innovations: A Multilayer, Heterogeneous, Dynamic Network Perspective
Artificial Intelligence (AI) has emerged as a crucial facet of contemporary technological innovation, influencing diverse domains. Consequently, understanding the diffusion and evolution of AI innovations is vital. Scholarly publications have commonly served as proxies for studying these AI innovations. However, previous studies on publication diffusion have largely overlooked the role of models, which is particularly integral for AI innovations as they bridge upstream datasets and downstream applications. Moreover, models form an interdependent network due to their combinational evolution. This paper addresses this gap, examining how the location, movement, and speed of model movement in that model network affect the dissemination of AI research. Using a four-layer network—author collaborations, paper citations, model dependencies, and keyword co-occurrences—we examine 345,383 AI papers from 2000 to 2022. This research aims to contribute to the diffusion of innovation literature and dynamic network analysis, offering several novel insights and advancements.
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