Abstract:

Measuring the digitalization level of enterprises systematically and comprehensively is crucial for an in-depth exploration of the effectiveness of digital transformation. It has received wide attention from academic community by statistically analyzing the frequency of digitalization-related terms in annual reports of companies. However， previous studies have overlooked the importance of terminology dictionaries， and usually adopt small-scale， narrow coverage and poor scalability， which leads to improper measurement of enterprise digitalization levels. To address this issue， two term extraction models are proposed， namely BERT-GlobalPointer and BERT-GlobalPointer-Mask， for efficient identification of digital terms. Furthermore， utilizing the large-scale terminology dictionary built by the proposed BERT-GlobalPointer-Mask， a Digital Transformation Index （DTI） for Chinese listed companies is developed and their evolution patterns over the past two decades are analyzed. The results indicate that the proposed models significantly outperform the benchmark models in identifying digital terms， new terms and long terms. Based on BERT-GlobalPointer-Mask model， a digital terminology dictionary containing 273，634 terms is constructed， which covers more comprehensive and diverse digital terms than previous studies. With this terminology dictionary， a more long-term and comprehensive measurement of the digitalization of listed companies in China over the past two decades can be conducted. The fundamental data support is provided for quantitative measurement and effectiveness analysis of enterprise digital transformation， and there is significant methodological and practical values.

Key words: digital transformation, term extraction, text analysis, deep learning, evolution analysis