IDEAS home Printed from https://ideas.repec.org/a/bla/bstrat/v35y2026i3p4521-4537.html

Leveraging Artificial Intelligence for ESG Reporting: A Case Study in the European Fashion Industry

Author

Listed:
  • Serena Strazzullo
  • Lucia Acampora
  • Livio Cricelli
  • Sara Ianniello
  • Maria Elena Nenni
  • Narinder Singh

Abstract

The fashion industry in Europe has increasingly recognized the importance of Environmental, Social, and Governance (ESG) reporting as a key driver for sustainable development and transparency. As consumer awareness grows and regulatory frameworks evolve, companies are pressured to disclose their sustainability practices, ethical labor standards, and governance policies. Artificial intelligence (AI) is emerging as a powerful tool in this transformation, providing innovative solutions for data collection, analysis, and reporting. AI‐driven systems support fashion companies in monitoring environmental impacts, optimizing supply chains, and improving labor conditions while ensuring ESG compliance. Their predictive capabilities also enable early detection of ESG risks, supporting proactive sustainability strategies. However, the adoption of AI into ESG monitoring and reporting is still underdeveloped, particularly in sector‐specific contexts. This paper examines the intersection of ESG reporting and the role of AI in enhancing transparency and accountability, in the European fashion industry. Drawing on seven in‐depth interviews with sustainability managers from Italian companies, the study employs thematic analysis to identify key patterns in ESG reporting and AI adoption. The findings reveal heterogeneous ESG maturity levels, limited but growing AI integration, and strong managerial awareness of digital transformation needs in sustainability reporting. This study contributes to the growing body of research on sustainable business practices. Theoretically, it offers an empirical foundation to explore AI–ESG integration across industries. Practically, it drives both corporate responsibility and competitive advantage, emphasizing the need for digital transformation to meet the evolving demands of stakeholders in the European fashion sector.

Suggested Citation

  • Serena Strazzullo & Lucia Acampora & Livio Cricelli & Sara Ianniello & Maria Elena Nenni & Narinder Singh, 2026. "Leveraging Artificial Intelligence for ESG Reporting: A Case Study in the European Fashion Industry," Business Strategy and the Environment, Wiley Blackwell, vol. 35(3), pages 4521-4537, March.
  • Handle: RePEc:bla:bstrat:v:35:y:2026:i:3:p:4521-4537
    DOI: 10.1002/bse.70405
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/bse.70405
    Download Restriction: no

    File URL: https://libkey.io/10.1002/bse.70405?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Victoria Bogdan & Luminița Rus & Diana Elisabeta Matica, 2025. "The Interconnection of Double Materiality Assessment, Circularity Practices Disclosure and Business Development in the Fast Fashion Industry," Sustainability, MDPI, vol. 17(4), pages 1-23, February.
    2. Dumrose, Maurice & Rink, Sebastian & Eckert, Julia, 2022. "Disaggregating confusion? The EU Taxonomy and its relation to ESG rating," Finance Research Letters, Elsevier, vol. 48(C).
    3. Kenneth David Strang & Narasimha Rao Vajjhala, 2024. "Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms," Sustainability, MDPI, vol. 16(23), pages 1-27, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Weeren, Michelle & Bluntz, Clarence, 2025. "What makes a rating useable? Shifting epistemic practices in the ESG rating field," Accounting, Organizations and Society, Elsevier, vol. 114(C).
    2. Muck, Matthias & Schmidl, Thomas, 2024. "Comparing ESG score weighting approaches and stock performance differentiation," Finance Research Letters, Elsevier, vol. 67(PB).
    3. Kurbus, Barbara & Rant, Vasja, 2025. "A legal origins perspective on ESG rating disagreement," Research in International Business and Finance, Elsevier, vol. 74(C).
    4. Federica Tonnarello & Carlo Vermiglio & Carlo Migliardo & Valeria Naciti, 2025. "The Impact of EU Taxonomy for Sustainable Activities on European Utilities' Performance," Business Strategy and the Environment, Wiley Blackwell, vol. 34(3), pages 2848-2862, March.
    5. Guo, Chenhao & Zhong, Rui, 2025. "Is green revenue vanity or sanity? Evidence from corporate cash holdings," International Review of Financial Analysis, Elsevier, vol. 101(C).
    6. Bassen, Alexander & Kordsachia, Othar & Lopatta, Kerstin & Tan, Weiqiang, 2025. "Revenue alignment with the EU taxonomy regulation in developed markets," Journal of Banking & Finance, Elsevier, vol. 170(C).
    7. Essossinam Beguedou & Satyanarayana Narra & Ekua Afrakoma Armoo & Komi Agboka & Mani Kongnine Damgou, 2023. "Alternative Fuels Substitution in Cement Industries for Improved Energy Efficiency and Sustainability," Energies, MDPI, vol. 16(8), pages 1-29, April.
    8. Jiaxin Zhuang & Yinglin Wang & Shengxu Shi, 2025. "ESG Enterprise Hybrid Risk Diversification Mechanism Based on Third‐Party Guarantee," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(7), pages 4032-4055, October.
    9. Cheng, Louis T.W. & Cheong, Tsun Se & Wojewodzki, Michal & Chui, David, 2025. "The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach," Research in International Business and Finance, Elsevier, vol. 73(PA).
    10. Lygnerud, Kristina & Romanchenko, Dmytro & Unluturk, Burcu & Popovic, Tobias & Schultze, Sebastian, 2025. "Analysis of the impact of the EU Taxonomy on investments in District Heating," Energy Policy, Elsevier, vol. 198(C).
    11. Hoepner Andreas G. F. & Schneider Fabiola I., 2022. "EU Green Taxonomy Data – A First Vendor Survey," The Economists' Voice, De Gruyter, vol. 19(2), pages 229-242, December.
    12. Andre Höck & Tobias Bauckloh & Maurice Dumrose & Christian Klein, 2023. "ESG criteria and the credit risk of corporate bond portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 24(7), pages 572-580, December.
    13. Jarosław Brożek & Anna Kożuch & Marek Wieruszewski & Roman Jaszczak & Krzysztof Adamowicz, 2024. "Taxonomy Regulation as a New Instrument for the Sustainable Management of the Forest Environment in Europe," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    14. Kong, Linghui & Chen, Rongquan & Huang, Xinyu & Wang, Fan, 2025. "Can Industry-Specific Information Disclosure Guidelines Alleviate Corporate ESG Divergence? Evidence from Chinese List Companies," International Review of Financial Analysis, Elsevier, vol. 106(C).
    15. Wu, Liangpeng & Tang, Yujing & Meng, Lei & Zhu, Qingyuan & Zhou, Dequn, 2025. "Navigating ESG rating divergence: Implications for labor investment efficiency and firm adaptation strategy," Global Finance Journal, Elsevier, vol. 67(C).
    16. Dina Lucia Todaro & Riccardo Torelli, 2024. "From greenwashing to ESG‐washing: A focus on the circular economy field," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(5), pages 4034-4046, September.
    17. Kasırga Yıldırak & Ömer Kayhan Seyhun, 2025. "Refining ESG models: embedding natural capital valuation beyond box-ticking compliance towards confronting planetary boundaries," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.
    18. Massimo Postiglione & Cristian Carini & Alberto Falini, 2024. "ESG and firm value: A hybrid literature review on cost of capital implications from Scopus database," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(6), pages 6457-6480, November.
    19. Zou, Jin & Yan, Jingzhou & Deng, Guoying, 2023. "ESG rating confusion and bond spreads," Economic Modelling, Elsevier, vol. 129(C).
    20. Wang, Wenjiao & Sun, Ziyuan & Wang, Lan, 2025. "Does ESG rating divergence exacerbate management tone manipulation? − Empirical evidence based on MD&A text," Journal of Business Research, Elsevier, vol. 197(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:bstrat:v:35:y:2026:i:3:p:4521-4537. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-0836 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.