This study investigates the impact of digital transformation on corporate sustainability reporting, analysing how emerging technologies (e.g., AI, Blockchain, IoT, Big Data) are fundamentally reconfiguring the ESG process and data lifecycle. It aims to resolve the disconnect between theoretical technological potential and operational reality by evaluating an ecosystem of technologies, rather than a single digital tool. Adopting a Systematic Mapping Study (SMS) compliant with PRISMA 2020 guidelines, this research analyses 110 peer-reviewed articles published between 2020 and 2026. Moving beyond purely bibliometric approaches, the literature was systematically coded to construct a functional taxonomy by cross-referencing technological families with ESG reporting phases, strategic functions, and implementation barriers. Results indicate a structural polarization in the literature, characterized by a functional dichotomy. While AI and Blockchain are extensively applied in the downstream phases for fraud detection and trust enhancement, the upstream Data Collection phase exhibits a significant lack of automated solutions, leaving the reliability of input data unresolved. Furthermore, despite technology's potential to reduce information asymmetry, high capital expenditures and organizational complexity deter firms from adopting transparent digital frameworks, favouring cosmetic digitalization over substantive process re-engineering. Providing a holistic, disaggregated framework, the study outlines a strategic roadmap to navigate the complexities of the Twin Transition. It demonstrates that effective ESG digitalization requires shifting focus from output validation to source data integrity, advocating for a human-centric approach to mitigate the risks of automated greenwashing. Conclusively, the study proposes a research agenda targeting current critical gaps, such as social dimension marginalization and SME adoption barriers.
Lo studio indaga l'impatto della trasformazione digitale sulla rendicontazione di sostenibilità aziendale (CSR), analizzando come le tecnologie emergenti (es. AI, Blockchain, IoT, Big Data) stiano radicalmente riconfigurando il processo di reporting ESG e il ciclo di vita dei relativi dati. La ricerca si propone di colmare il divario tra il potenziale tecnologico teorico e la realtà operativa, valutando un ecosistema tecnologico anziché un singolo strumento digitale. Adottando un Systematic Mapping Study (SMS) conforme alle linee guida PRISMA 2020, la ricerca analizza 110 articoli peer-reviewed pubblicati tra il 2020 e il 2026. Superando approcci puramente bibliometrici, la letteratura è stata codificata per costruire una tassonomia funzionale, incrociando le famiglie tecnologiche con le fasi di reporting ESG, le funzioni strategiche e le barriere implementative. Le evidenze mostrano una polarizzazione strutturale all'interno della letteratura, caratterizzata da una dicotomia funzionale. Mentre l'IA e la Blockchain trovano ampia applicazione nelle fasi a valle per il rilevamento delle frodi e il rafforzamento della fiducia, la fase a monte di raccolta dei dati sconta una carenza di soluzioni automatizzate, lasciando irrisolto il problema dell'attendibilità degli input. Inoltre, sebbene la tecnologia possa ridurre l'asimmetria informativa, alti investimenti e complessità organizzativa frenano l'adozione di architetture trasparenti, favorendo una digitalizzazione di facciata a discapito di una reale reingegnerizzazione dei processi. Offrendo un framework olistico e disaggregato, lo studio traccia una roadmap strategica per orientarsi nella complessità della Twin Transition. Si dimostra che un'efficace digitalizzazione delle logiche ESG richiede di spostare il focus dalla validazione degli output all'integrità dei dati alla fonte, promuovendo un approccio human-centric per mitigare i rischi di greenwashing automatizzato. Infine, la ricerca formula un'agenda per gli studi futuri mirata a colmare specifiche lacune critiche, come la marginalizzazione della dimensione sociale e gli ostacoli all'adozione da parte delle PMI.
Digital technologies for ESG reporting: a systematic mapping study
Tangari, Annachiara
2024/2025
Abstract
This study investigates the impact of digital transformation on corporate sustainability reporting, analysing how emerging technologies (e.g., AI, Blockchain, IoT, Big Data) are fundamentally reconfiguring the ESG process and data lifecycle. It aims to resolve the disconnect between theoretical technological potential and operational reality by evaluating an ecosystem of technologies, rather than a single digital tool. Adopting a Systematic Mapping Study (SMS) compliant with PRISMA 2020 guidelines, this research analyses 110 peer-reviewed articles published between 2020 and 2026. Moving beyond purely bibliometric approaches, the literature was systematically coded to construct a functional taxonomy by cross-referencing technological families with ESG reporting phases, strategic functions, and implementation barriers. Results indicate a structural polarization in the literature, characterized by a functional dichotomy. While AI and Blockchain are extensively applied in the downstream phases for fraud detection and trust enhancement, the upstream Data Collection phase exhibits a significant lack of automated solutions, leaving the reliability of input data unresolved. Furthermore, despite technology's potential to reduce information asymmetry, high capital expenditures and organizational complexity deter firms from adopting transparent digital frameworks, favouring cosmetic digitalization over substantive process re-engineering. Providing a holistic, disaggregated framework, the study outlines a strategic roadmap to navigate the complexities of the Twin Transition. It demonstrates that effective ESG digitalization requires shifting focus from output validation to source data integrity, advocating for a human-centric approach to mitigate the risks of automated greenwashing. Conclusively, the study proposes a research agenda targeting current critical gaps, such as social dimension marginalization and SME adoption barriers.| File | Dimensione | Formato | |
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2026_03_Tangari.pdf
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Descrizione: Testo Tesi di Laurea
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https://hdl.handle.net/10589/252480