DOI: 10.57760/sciencedb.j00289.00063
title: A Dataset on Corporate Digital Responsibility Indicators for Automotive Manufacturers Listed on China's A-Share Market from 2018 to 2023
title: 中国汽车制造企业2018-2023年企业数字责任指标数据集

description: Corporate digital responsibility consists of a social responsibility surface and a digital core, where the digital core includes digital elements, digital technologies, digital management, and digital value. Firstly, construct an enterprise digital responsibility feature lexicon based on the five dimensions mentioned above. Then, use natural language processing methods to calculate word frequency and measure enterprise digital responsibility data. Finally, introduce multiple anti-interference measures to embellish and correct the data for the enterprise. Specifically, data from A-share "automobile manufacturing" sector enterprises from 2018 to 2023 will be collected. The annual report texts of these enterprises are sourced from Guotai An database, Wind database, Tonghuashun Finance, and Julang Information website. Excluding ST and * ST companies as well as companies with less than 6 years of listing, 89 automobile manufacturing companies have obtained annual report texts for 6 consecutive years; Obtain initial data of enterprise digital responsibility indicators through data processing and calculation; Introduce methods such as negative word screening, industry index comparison, and semantic accompaniment sampling to embellish and remove three invalid disclosure companies. The final dataset includes 516 enterprise digital responsibility data values from 86 companies, as well as 516 word frequency statistics for each feature word (a total of 100 feature words). This dataset and measurement scheme can be replicated in other industries and effectively support empirical research on corporate digital responsibility as well as third-party evaluation work. 
description: Corporate digital responsibility consists of a social responsibility surface and a digital core, where the digital core includes digital elements, digital technologies, digital management, and digital value. Firstly, construct an enterprise digital responsibility feature lexicon based on the five dimensions mentioned above. Then, use natural language processing methods to calculate word frequency and measure enterprise digital responsibility data. Finally, introduce multiple anti-interference measures to embellish and correct the data for the enterprise. Specifically, data from A-share "automobile manufacturing" sector enterprises from 2018 to 2023 will be collected. The annual report texts of these enterprises are sourced from Guotai An database, Wind database, Tonghuashun Finance, and Julang Information website. Excluding ST and * ST companies as well as companies with less than 6 years of listing, 89 automobile manufacturing companies have obtained annual report texts for 6 consecutive years; Obtain initial data of enterprise digital responsibility indicators through data processing and calculation; Introduce methods such as negative word screening, industry index comparison, and semantic accompaniment sampling to embellish and remove three invalid disclosure companies. The final dataset includes 516 enterprise digital responsibility data values from 86 companies, as well as 516 word frequency statistics for each feature word (a total of 100 feature words). This dataset and measurement scheme can be replicated in other industries and effectively support empirical research on corporate digital responsibility as well as third-party evaluation work.