首页|Text classification for evaluating digital technology adoption maturity based on BERT: An evidence of Industrial AI from China

Text classification for evaluating digital technology adoption maturity based on BERT: An evidence of Industrial AI from China

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier Inc.Natural language processing (NLP) models, such as GPT and BERT, are predictable to subvert the research paradigm of technology foresight and innovation management, for their good objectivity, robustness, and efficiency. This paper aims to apply an NLP model based on deep learning to realize the digital technology adoption maturity evaluation. Firstly, a 3-layer evaluation system, with a hierarchy of domain-indicator-level, is proposed. Meanwhile, a dataset on the deployment of Industrial AI in China is collected and provided to our evaluation system. After data annotation by experts with reference to domains and indicators, a BERT model is introduced to execute the multi-label text classification task. The experiment results prove that our high-performance BERT model has the ability to learn from human experts, and then benefits to mitigate biases and reduce cost in evaluation. In the area of Industrial AI deployments, our research points out the digital technologies adoption maturity trends over time, trickle-down effect across regions, and the flying geese pattern between industries.

BERTDeep learningDigital technology adoption maturityIndustrial AIMulti-label text classification

Wang Y.、Gong C.、Ji X.、Yuan Q.

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School of Management Studies Shanghai University of Engineering Science

Technology research & development department

2025

10.1016/j.techfore.2024.123903

Technological forecasting and social change

Technological forecasting and social change

ISSN：0040-1625
年,卷(期)：2025.211(Feb.)
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