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THE ROLE OF ESG REPORTING IN STRENGTHENING CORPORATE REPUTATION AND INVESTOR TRUST. THE CASES IN THE CAR INDUSTRY

ESG報告が企業の評判と投資家の信頼を強化する役割：自動車産業の事例 (AI 翻訳)

MOLDIR SHAIMERDENOVA, OLGA UZHEGOVA

プレプリント2025-10-31#ESGOrigin: Global
DOI: 10.5281/zenodo.17664647
原典: https://doi.org/10.5281/zenodo.17664647

🤖 gxceed AI 要約

日本語

本論文は、自動車産業におけるESG報告が企業の評判と投資家の信頼に与える影響を理論的・実証的に分析する。正当性理論とシグナリング理論に基づき、「評判-信頼ネクサス」モデルを提案し、開示の透明性（マテリアリティ、比較可能性、保証）がステークホルダーの認識と信頼に与える影響を検証する。自動車産業特有のサプライチェーンの複雑性と電化などの技術移行が重要な調整要因であることを示し、業界固有のESG-KPI指標やデジタルツイン活用などの実践的提言を行う。

English

This paper theoretically and empirically analyzes how ESG reporting influences corporate reputation and investor trust in the automotive industry. Drawing on legitimacy and signaling theories, it proposes a 'Reputation-Trust Nexus' model, emphasizing disclosure transparency (materiality, comparability, assurance) and its impact on stakeholder perceptions and trust. Supply-chain complexity and technological transition (e.g., electrification) are identified as critical moderators. Practical recommendations include an automotive-specific ESG-KPI index, digital twin tools for disclosure simulation, and enhanced traceability and assurance mechanisms.

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

本論文は自動車産業に特化したESG報告の枠組みを提案しており、日本の自動車メーカー（トヨタ、ホンダなど）がSSBJ基準や有報でのESG開示を強化する際に参考となる。特にサプライチェーン全体のScope 3排出量開示や電動化移行におけるESG-KPIの設計は、日本企業の投資家対応や統合報告書の質向上に寄与する可能性がある。

In the global GX context

This paper offers a sector-specific ESG reporting model for the automotive industry, relevant to global disclosure frameworks like ISSB and CSRD. Its focus on supply-chain complexity and electrification transition provides insights for multinational automakers facing diverse regulatory demands. The proposed ESG-KPI index and digital twin tools could enhance comparability and assurance in sustainability reporting, addressing investor trust issues in the mobility sector.

👥 読者別の含意

🔬研究者:Provides a theoretical model (Reputation-Trust Nexus) linking ESG disclosure to corporate reputation and investor trust, with automotive-specific moderators like supply-chain complexity and electrification.

🏢実務担当者:Offers actionable recommendations such as an automotive ESG-KPI index and digital twin tools for disclosure simulation, useful for corporate sustainability teams in the car industry.

🏛政策担当者:Suggests regulatory enhancements like sector-specific ESG-KPI indices and assurance mechanisms, relevant for policymakers shaping automotive sustainability disclosure standards.

📄 Abstract（原文）

In the automotive sector, ESG (Environmental-Social-Governance) reporting has evolved into a strategic instrument for reconciling corporate sustainability performance with external perceptions of legitimacy and risk. This article develops a theoretical and empirical inquiry into how robust ESG disclosures influence corporate reputation and investor trust within the car industry. Drawing on legitimacy and signalling theories, and integrating evidence from leading automotive manufacturers, the paper proposes a “Reputation-Trust Nexus” model specific to the mobility domain, emphasising disclosure transparency (materiality, comparability, assurance), stakeholder perceptions, and trust-driven outcomes. Empirical indicators are analysed, and the supply-chain complexity and technological transition (e.g., electrification) of the automotive industry are shown as critical moderators and mediators. Managerial, investor and regulatory recommendations are provided, including the creation of an automotive-specific ESG-KPI index, deployment of digital twin tools for disclosure simulation, and richer traceability & assurance mechanisms. By positioning ESG reporting as a dynamic strategic asset rather than static compliance, the article advances both academic understanding and practical guidance for automotive firms seeking reputational capital and investor trust in an era of rapid mobility transformation.

🔗 Provenance — このレコードを発見したソース

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。