Abstract
In the current AI era, an increasing number of new technologies have been developed which promote disruptive innovation, making analysis of diffusion of innovation ever more important. Where previous studies have mainly focused on the direct influence of new technology adoption behaviors, this article proposes a new model (Adoption Behavior-based Graphical Model (ABGM)) which incorporates the effect of influencing factors (i.e., homophily and heterophily) on users’ behavior regarding the adoption of new AI technologies. This model simulates the process of innovation diffusion and connects the diffusion patterns in a unified framework. We evaluate the proposed model on a large-scale AI publication dataset from 2006 to 2015. Results show that ABGM outperforms start-of-the-art baselines and also demonstrates that the probability of individual users adopting an innovation is significantly influenced by the diffusion process through the correlation network.
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This work is partially supported by the Chinese National Social Science Major Project 17ZDA200 and Chinese National Nature Science Youth Project 61702564.
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Zhou, E. et al. (2019). Modeling Adoption Behavior for Innovation Diffusion. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_33
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DOI: https://doi.org/10.1007/978-3-030-15742-5_33
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