Paper Number
1219
Paper Type
Complete Research Paper
Abstract
Sustainability reporting has become an essential aspect of corporate transparency and stakeholder engagement. As the number of sustainability reports continues to grow, the need for efficient and accurate automatic analysis tools to determine sustainability efforts and companies’ risks becomes increasingly important. Through technological progress, Artificial Intelligence is being increasingly utilized for report analysis. This paper aims to characterize AI-based methods for the analysis of sustainability reports based on the purpose they provide for users and to identify archetypes for publicly available methods. Using a taxonomy-driven approach combined with cluster analysis, we examine the objectives of existing methods employed in the literature and practice. By considering sustainability’s unique characteristics, we contribute to a better understanding of the current state of the field, enable researchers and practitioners to position their work, and provide insights into potential future developments in sustainability report analysis.
Recommended Citation
Wilhelmi, Lars; Nickerson, Robert C.; and Schumann, Matthias, "Insights from the Alphabet Soup: Characterizing AI-Based Methods for the Analysis of Sustainability Reports" (2024). ECIS 2024 Proceedings. 32.
https://aisel.aisnet.org/ecis2024/track17_greenis/track17_greenis/32
Insights from the Alphabet Soup: Characterizing AI-Based Methods for the Analysis of Sustainability Reports
Sustainability reporting has become an essential aspect of corporate transparency and stakeholder engagement. As the number of sustainability reports continues to grow, the need for efficient and accurate automatic analysis tools to determine sustainability efforts and companies’ risks becomes increasingly important. Through technological progress, Artificial Intelligence is being increasingly utilized for report analysis. This paper aims to characterize AI-based methods for the analysis of sustainability reports based on the purpose they provide for users and to identify archetypes for publicly available methods. Using a taxonomy-driven approach combined with cluster analysis, we examine the objectives of existing methods employed in the literature and practice. By considering sustainability’s unique characteristics, we contribute to a better understanding of the current state of the field, enable researchers and practitioners to position their work, and provide insights into potential future developments in sustainability report analysis.
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