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A multi-level visual representation dataset for large-scale non-financial information disclosure.

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A new dataset, the Non-Financial Information Disclosure Visual Representations Index (NFIVI), quantifies visual elements in corporate reports. This enables objective analysis of corporate impression management strategies.

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Area of Science:

  • Business and Finance
  • Data Science
  • Visual Analytics

Background:

  • Corporate sustainability reports increasingly use visual elements to shape stakeholder perceptions.
  • Objective, quantitative tools for analyzing these visual strategies are currently limited.

Purpose of the Study:

  • To introduce the Non-Financial Information Disclosure Visual Representations Index (NFIVI) dataset.
  • To provide a dynamic, annually updated resource for Chinese listed companies' reports (2006-2024).
  • To enable quantitative research on corporate impression management through visual rhetoric.

Main Methods:

  • Developed a pipeline integrating layout analysis and computer vision to process reports.
  • Decomposed reports into text, image, and color elements.
  • Introduced two key indices: Feature-Correlation Index (NFIVI_FC) for stylistic consistency and Information Entropy Index (NFIVI_EI) for visual complexity.
  • Defined 18 granular indicators across text, image, and color at page and document levels.

Main Results:

  • The NFIVI dataset offers objective metrics for visual composition and structure in corporate reports.
  • The dataset operationalizes abstract design concepts into computable measures.
  • Facilitates large-scale quantitative studies on visual impression management.

Conclusions:

  • The NFIVI dataset is a valuable resource for researchers and supports the development of automated auditing tools for non-financial disclosures.
  • Enables objective measurement of visual strategies in corporate reporting.
  • Advances the quantitative analysis of corporate communication and stakeholder engagement.