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

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  • Wang, Yanhong
  • Gong, Chen
  • Ji, Xiaodong
  • Yuan, Qi

Abstract

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.

Suggested Citation

  • Wang, Yanhong & Gong, Chen & Ji, Xiaodong & Yuan, Qi, 2025. "Text classification for evaluating digital technology adoption maturity based on BERT: An evidence of Industrial AI from China," Technological Forecasting and Social Change, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:tefoso:v:211:y:2025:i:c:s0040162524007017
    DOI: 10.1016/j.techfore.2024.123903
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