AI-Driven Responsible Supply Chain Management and Ethical Issue Detection in the Tourism Industry

Minjung Hong and JongMyoung Kim ()
Additional contact information
Minjung Hong: Department of Tourism and Leisure, Dangjin Campus, Sehan University, Dangjin 31746, Republic of Korea
JongMyoung Kim: Department of Artificial Intelligence and Big Data, Dangjin Campus, Sehan University, Dangjin 31746, Republic of Korea

Sustainability, 2025, vol. 17, issue 21, 1-17

Abstract: This study aims to develop and evaluate an AI- and big-data-based innovation system for proactively managing ESG (Environmental, Social, and Governance) risks within the tourism supply chain. Drawing on heterogeneous data sources including supply chain records, news articles, social media, and public databases, the research employs advanced methodologies such as network analysis, anomaly detection, natural language processing (including greenwashing detection), and predictive modeling. Through this comprehensive approach, the study demonstrates the feasibility and effectiveness of a dynamic AI-driven ESG risk management system that delivers reliable risk identification and quantitative performance evaluation. The theoretical contribution lies in bridging AI-driven ESG evaluation frameworks with sustainable tourism and hospitality literature, moving beyond static, indicator-based assessments toward a more systematic, replicable, and predictive methodology capable of capturing the dynamic, multiscalar, and networked nature of tourism supply chains. Ultimately, this research provides tourism and hospitality firms with a powerful tool to enhance transparency, mitigate ethical and reputational risks, and strengthen stakeholder trust, while offering actionable insights for managers and policymakers developing data-driven ESG integration strategies.

Keywords: tourism; supply chain management (SCM); ESG (environmental, social, governance); artificial intelligence (AI); big data analytics; greenwashing; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/21/9622/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/21/9622/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:21:p:9622-:d:1782451

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().