Quantitative Evaluation of China’s CSR Policies Based on the PMC-Index Model
Along with the deep comprehension and accumulated practice of corporate social responsibility (CSR), people are increasingly aware of the positive role of the government in the development of CSR. Chinese governments at all levels have issued many policies to guide and regulate CSR behavior in Chine...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI
2023
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Subjects: | |
Online Access: | View Fulltext in Publisher View in Scopus |
Summary: | Along with the deep comprehension and accumulated practice of corporate social responsibility (CSR), people are increasingly aware of the positive role of the government in the development of CSR. Chinese governments at all levels have issued many policies to guide and regulate CSR behavior in Chinese enterprises. However, there has been little research on the evaluation of CSR policy’s effectiveness. In this paper, we conducted a text analysis of 76 corporate social responsibility policies (CSRPs) and statistics of high-frequency words. Based on the existing policy evaluation index system, combined with the characteristics of CSRPs, we constructed a CSRPs content evaluation index system based on the policy modeling consistency index (PMC-index) model. Additionally, we conducted content analysis and quantitative evaluation of six CSRPs selected from different levels and regions of government agencies. The results show that the evaluation levels of the six policies were all good, which could play a positive role in the CSR development of their policy objectives. However, policies in different regions show obvious differences in the design of implementation suggestions and incentive and constraint measures, and there is a large space for further optimization. This study not only provides specific policy optimization suggestions for the government and enterprises based on case studies but also provides methods for evaluating the content of CSRPs, filling the research gap in this field. © 2023 by the authors. |
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ISBN: | 20711050 (ISSN) |
DOI: | 10.3390/su15097194 |