The Textual Representation of Double Materiality in ESG Reports: Developing a Content Analysis Codebook within a Business Model Perspective
Raili Lilo et al.
What the paper says
Purpose: This study aims to operationalise the analysis of textual patterns in ESG reports from the perspective of double materiality, enabling the distinction between value and impact statements across environmental, social, and governance initiatives. This aligns with the transformative nature of ESG reporting, which promotes more sustainable business models by integrating environmental and social impacts into core value creation, facilitating the management of risks and opportunities linked to the interests of various stakeholders. Design/Methodology/Approach: The paper develops guidelines for exploratory content analysis to examine ESG reports through a structured codebook approach, focusing on topic prevalence, tone, and integration levels. It combines signalling, stakeholder, legitimacy, institutional, and attribution theories as complementary elements. Findings: The research establishes a methodological framework for analysing double materiality in ESG reports facilitating the recognition, systematisation, and analysis of textual choices in ESG reporting. Practical implications: The developed codebook provides a structured approach to analyse and compose ESG reports, helping organisations balance standardisation requirements with reporting flexibility while ensuring transparent, decision-useful information for stakeholders. This benefits both academics and practitioners. Originality/Value: This paper presents a novel methodological approach to analyse ESG reports through the lens of double materiality, bridging theoretical understanding with practical application. This supports the use of ESG reporting as a tool for transformation towards more sustainable business models.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.