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Linguistic Characteristics of CEO Messages in Sustainability Reports : Evidence from Korea

원문정보

초록

영어

This study explored the impact of various factors on the linguistic features of CEO messages in sustainability reports, based on data from 154 listed Korean companies and 339 firm-year observations. Utilizing correlation analysis, univariate analysis, and panel regression analysis, the results indicated that higher ESG performance is generally linked to improved readability and longer CEO messages. However, higher ESG performance did not significantly affect the use of positive or future-oriented language in the messages. The analysis further revealed that large enterprises typically produce more readable messages than small and medium-sized enterprises(SMEs). Notably, the positive effect of ESG performance on readability was stronger in SMEs than in large enterprises. Additionally, no significant differences in readability were found across different industries. This study provides important insights into how corporate social responsibility activities, reflected in ESG performance, are communicated to external stakeholders in listed Korean companies. The findings imply that higher ESG performance is associated with clearer and more informative CEO messages, which could enhance stakeholder perceptions of transparency. The fact that ESG improvements have a more pronounced effect on readability in SMEs indicates that these firms, often with limited resources, aim to maximize the impact of their ESG activities and enhance their public image.

목차

Abstract
I. Introduction
II. Theory and Hypotheses
III. Data Collection and Model
1. Text Data of CEO Messages
2. Research Model
IV. Results
1. Sample Selection
2. Descriptive Statistics and Correlation Analysis
3. Univariate Analysis
4. Panel Regression Analysis
V. Discussion and Conclusion
Reference

저자정보

  • Park, Young Min Student, Department of Mathematics & Big Data Science, Kumoh National Institute of Technology
  • Lee, Woo Tae Student, Department of Mathematics & Big Data Science, Kumoh National Institute of Technology
  • Lee, Gi Dong Student, Department of Mathematics & Big Data Science, Kumoh National Institute of Technology
  • Lee, Su Hyeong Student, Department of Mathematics & Big Data Science, Kumoh National Institute of Technology
  • Yang, Ji-Yeon Professor, Department of Mathematics & Big Data Science, Kumoh National Institute of Technology

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