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

Yanhong Wang, Chen Gong, Xiaodong Ji and Qi Yuan

Technological Forecasting and Social Change, 2025, vol. 211, issue C

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.

Keywords: Digital technology adoption maturity; Industrial AI; Multi-label text classification; Deep learning; BERT (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162524007017
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:211:y:2025:i:c:s0040162524007017

DOI: 10.1016/j.techfore.2024.123903

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().