Decisions Beyond Data: Narrative Reporting Practices in Decision-Making
データを超えた意思決定：意思決定におけるナラティブ報告の実践 (AI 翻訳)
Tamás Zelles, Bernadett Domokos, Sándor Remsei
🤖 gxceed AI 要約
日本語
本論文は、機械学習とナラティブ手法を組み合わせた意思決定支援を検討。特に会計・サステナビリティ分野でのナラティブ報告が、専門家の解釈を加えることでデータ可視化だけでは得られない文脈的洞察を提供することを示す。AIとナラティブの統合が報告の明確性と戦略的価値を高め、暗黙知とデータ駆動型知見の整合を促進する。
English
This paper examines how combining narrative techniques with machine learning can enhance decision-making, particularly in accounting and sustainability reporting. It finds that narrative-driven reporting with expert interpretation improves insight generation over pure data visualization. AI integration enhances report clarity and strategic relevance, aligning tacit knowledge with data-driven insights.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではSSBJ基準の導入が進み、定性的なナラティブ開示の質が重要視されています。本論文は機械学習とナラティブ手法の組み合わせが開示の戦略的価値を高める可能性を示しており、有報や統合報告書の作成に示唆を与えます。
In the global GX context
Global frameworks like TCFD and ISSB emphasize narrative, forward-looking information. This paper strengthens the case for AI-enhanced narrative reporting to improve decision-usefulness of climate and sustainability disclosures.
👥 読者別の含意
🔬研究者:Provides a framework for integrating ML with narrative reporting for sustainability disclosure, opening avenues for empirical work on disclosure tone and decision outcomes.
🏢実務担当者:Can improve the quality and strategic relevance of narrative sections in ESG reports, leveraging AI to better communicate context and expertise.
🏛政策担当者:Highlights the potential of AI to enhance decision-usefulness of qualitative disclosures in sustainability regulations.
📄 Abstract（原文）
Leaders and managers frequently face the need to make highly complex decisions with incomplete or fragmented information. Traditional decision support systems largely emphasize the visualization of data but often fall short in producing context-sensitive insights that can directly inform decision-making. This paper examines how narrative techniques combined with machine learning can strengthen communication across organizational hierarchies, particularly by improving the transfer of tacit expertise and contextual knowledge. To explore this, a transdisciplinary literature review was conducted using articles published within the last five years from databases such as Scopus, Web of Science, and ScienceDirect. The review highlights that narrative-driven reporting has been most commonly applied in fields such as accounting and sustainability, where expert interpretation replaces purely numerical summaries with more meaningful analytical explanations. Such approaches can also embed sentiment and personalization, commonly referred to as Narrative Disclosure Tone. Building on this foundation, the study investigates how Artificial Intelligence-driven decision support can formally integrate narrative elements to enhance report clarity, usability, and strategic relevance. Findings suggest that combining machine learning with expert-driven narrative reporting supports more innovative decision support systems and facilitates the alignment of tacit knowledge with data-driven insights.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/2076-3387/16/4/181/pdf?version=1775701477first seen 2026-07-18 07:23:00
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。