A Critical Review of Non-Financial Disclosure Measurement Methods

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers can use this review as a comprehensive overview of NFD measurement methods and their limitations, providing a foundation for further methodological improvements.

🏢実務担当者:Practitioners can gain insights into the weaknesses of current measurement approaches and consider adopting AI-enhanced tools for more reliable disclosure assessment.

🏛政策担当者:Policymakers can use the findings to inform the design of standardized disclosure measurement frameworks that prioritize quality and comparability.

Purpose – The measurement of non-financial disclosure (NFD) remains a key challenge in corporate reporting due to inconsistencies, subjectivity, and methodological limitations. As companies increasingly disclose information on environmental, social, and governance (ESG) issues, corporate social responsibility (CSR), and sustainability, the need for robust, reliable, and comparable measurement frameworks has become critical. This study critically evaluates existing NFD measurement methods, highlighting their strengths, weaknesses, and future directions. Design/methodology/approach – A systematic literature review was conducted, focusing on five primary disclosure measurement techniques: content analysis, disclosure indices, market-based measures, regulatory compliance-based assessment, and disclosure surveys. The study evaluates these approaches based on their ability to assess the quality, relevance, and comparability of non-financial disclosures. Additionally, emerging methodologies such as AI-driven content analysis, machine learning applications, and sentiment analysis are explored as potential solutions to enhance disclosure assessment. Findings – Traditional NFD measurement methods suffer from bias, subjectivity, and excessive focus on disclosure quantity over quality. Furthermore, the voluntary nature of many non-financial disclosures complicates standardization and comparability across industries and jurisdictions. The study highlights the need for more adaptive, technology-driven measurement frameworks that integrate automation, contextual analysis, and qualitative evaluation to improve reliability and objectivity. Originality – This study contributes to the ongoing discourse on corporate transparency and sustainability reporting by advocating for a more holistic and technology-enhanced approach to NFD measurement. It underscores the importance of AI, natural language processing (NLP), and machine learning in improving accuracy, comparability, and scalability in corporate disclosure assessment.