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

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.

1. Introduction

The tourism and hospitality sector is a monumental engine of the global economy. It generates trillions of dollars in revenue and supports hundreds of millions of jobs around the world [1]. However, this economic significance is intrinsically linked to extensive and complex supply chain networks that span across continents. These networks involve a broad spectrum of goods and services, including food and beverages, textiles, amenities, transportation, construction, and outsourced operations [2,3]. The tourism supply chain (TSC) is conceptualized as an interconnected system that coordinates the flow of physical goods, services, information, and financial transactions among diverse stakeholders within tourism production and consumption networks [3,4,5]. It encompasses both tangible supply flows such as materials, energy, and logistics, and intangible or financial dimensions including contractual relations, revenue transfers, and investment flows [5,6]. Accordingly, the proposed AI-driven model is designed to monitor and manage both physical and financial supply chain elements by integrating heterogeneous data sources (e.g., operational records, procurement data, and financial disclosures) for ESG risk detection and evaluation.

It is important to recognize that the tourism supply chain is a multilayered structure composed of various stakeholders such as travel agencies, airlines, service providers, and public institutions, beyond just tourists and suppliers [2]. Embedded within this complexity are often hidden environmental, social, and governance (ESG) risks that present growing challenges to the industry’s long-term sustainability and its social license to operate [7]. In the context of increasing globalization and digitalization, the complexity and uncertainty of supply chains are expanding, thereby broadening the potential for negative externalities [2].

From an environmental perspective, tourism supply chains contribute significantly to carbon emissions through product transport and food mileage, water depletion from agriculture supporting hotels, waste generation from packaging and food waste, and biodiversity loss due to sourcing in areas involved in deforestation or habitat destruction [8,9]. Social risks include unstable working conditions, low wages, excessive working hours, inadequate safety standards, and in extreme cases, forced labor and human trafficking. These risks are particularly prevalent in lower-tier supply chains related to agriculture, manufacturing, or construction in regions with weak regulatory oversight [10,11]. Governance-related risks can involve corruption, lack of sourcing transparency, or unethical relationships with local communities impacted by supplier activities [12].

What further complicates these direct risks is the deceptive practice of greenwashing. This occurs when suppliers or tourism operators make misleading claims about their ESG performance to mislead stakeholders and hinder genuine progress toward sustainability [13,14]. For example, a hotel may promote its environmental commitment based on a towel reuse program while ignoring energy inefficiencies or unsustainable sourcing practices. At the same time, pressure from stakeholders demanding responsible practices is growing [15].

Modern consumers are increasingly taking sustainability into account when making travel decisions [4]. Investors are also recognizing that poor ESG performance not only undermines corporate image and trust but also leads to substantial financial risks. As a result, ESG criteria are being integrated into portfolio decisions [16]. Younger generations of employees, in particular, prefer to work for organizations that align with their personal values. Globally, regulatory bodies are enforcing stricter environmental legislation and mandatory ESG reporting requirements, such as the European Union’s Corporate Sustainability Reporting Directive. In this evolving context, failing to proactively manage ESG risks in supply chains is no longer just an ethical lapse. However, traditional ESG assessment tools such as periodic audits, supplier questionnaires, and voluntary certifications remain limited in scope and responsiveness, often capturing only static or self-reported data [17,18]. Previous comparative studies in supply chain contexts have shown that AI enabled systems can improve anomaly detection accuracy by 25 to 40 percent and reduce monitoring latency by up to 60 percent compared with conventional audit-based methods [19,20,21]. This evidence highlights the added value of the proposed AI driven approach, which integrates heterogeneous and real time data sources to provide continuous and quantifiable ESG risk evaluation beyond the capabilities of traditional tools. It has become a serious business liability that can result in supplier failures, legal sanctions, irreversible brand damage, consumer boycotts, and difficulties in securing capital and talent [22].

Despite these urgent needs, the tourism industry’s efforts to conduct comprehensive supply chain due diligence remain limited [23,24]. Conventional methods such as periodic audits, supplier questionnaires, and certifications often provide only a snapshot of a particular moment. These approaches struggle to reach beyond first-tier suppliers and are vulnerable to manipulation and superficial compliance [17,18]. Manual review of news articles or reports is slow and cannot keep up with the volume of information. As a result, tourism enterprises remain vulnerable to unexpected crises originating deep within their supply networks. This is precisely where the transformative potential of artificial intelligence (AI) and big data analytics becomes evident [21].

AI technologies offer the capability to continuously collect, process, and analyze massive datasets from diverse global sources. These sources include news feeds, social media, government records, satellite imagery, financial disclosures, and internal procurement data [25]. Machine learning algorithms can identify subtle patterns, anomalies, and correlations that are invisible to human analysts. Natural language processing techniques allow for the extraction of insights from unstructured text, assessment of sentiment, identification of specific risk events, and evaluation of the credibility of sustainability-related claims [20].

Recognizing this potential, and considering the increasing demand for both efficiency and ethical accountability, this study explores how AI can contribute to identifying ethical risks in tourism supply chains at an early stage [24]. However, it is equally important to acknowledge that the use of AI technologies introduces its own set of ethical risks. These include concerns related to data privacy, algorithmic bias, opacity of automated decision-making, and accountability for incorrect or discriminatory outputs [26]. Within tourism supply chains, such risks could manifest through biased supplier evaluations, unequal data representation between large and small enterprises, or misuse of personal information collected from digital platforms. Accordingly, the proposed system design incorporates principles of responsible AI, emphasizing transparency, explainability, and human oversight to ensure that the benefits of automation do not compromise ethical integrity or stakeholder trust [26,27].

The primary goal of this research is to propose a system model that demonstrates how AI technologies can detect ethical risks and enhance responsible supply chain management in the tourism industry. The study is guided by the following research questions:

Research Question 1: What are the major ethical risks embedded within tourism supply chains?

Research Question 2: How can AI technologies detect and respond to these risks?

Research Question 3: What policy and industry implications can be expected from implementing such a system?

This research aims to develop a novel AI-powered framework that is specifically designed to detect ethical risks and enable responsible supply chain governance within the unique context of the tourism and hospitality sector. The proposed approach integrates advanced AI and data analytics techniques with essential domain expertise in tourism operations, supply chain structures such as intermediary roles and seasonality, and tourism-specific ESG standards including fair wages, sustainable sourcing certifications, and cultural heritage preservation [28,29,30].

The system is designed to achieve five key objectives. First, it aims to develop dynamic visualizations that map multi-tiered supply chain structures and expose dependencies and vulnerabilities. Second, it enables continuous monitoring across diverse data streams to proactively track ESG performance and detect emerging risks. Third, it uses algorithmic tools to identify irregular behavior or activities associated with non-compliance or ethical breaches. Fourth, it applies natural language processing to corporate communications to detect possible cases of misleading ESG marketing by comparing stated claims with external evidence. Fifth, it conducts predictive risk assessments by using historical data and identified patterns to forecast potential supplier risks.

By addressing the growing demand for supply chain accountability through the advanced capabilities of AI, this study offers practical intelligence that can support the tourism industry in transitioning from reactive compliance to proactive risk mitigation. We argue that such systems can help build more transparent, ethical, and genuinely sustainable supply chains. This paper presents the theoretical foundation of the study, outlines the proposed methodology in detail, discusses anticipated results based on simulated findings, illustrates a case example, and explores the broader implications for responsible tourism research and practice.

2. Theoretical Background

2.1. ESG and Supply Chain Management in Tourism

The concept of incorporating environmental, social, and governance (ESG) considerations into business strategy has become increasingly important, shifting from the periphery of corporate responsibility discourse to a central strategic concern [16]. ESG principles are also gaining strategic significance in the management of supply chains within the tourism industry [2,5,16].

In supply chain management, this integration is often discussed in terms of sustainable supply chain management (SSCM). SSCM is defined as the coordination of material, information, and financial flows and inter-organizational cooperation along the supply chain while taking into account the three dimensions of sustainable development—economic, environmental, and social goals—derived from customer and stakeholder expectations [5] (p. 1700). This perspective has become increasingly relevant for tourism intermediaries such as travel agencies, hotels, and transportation providers, who are placing greater emphasis on implementing SSCM practices [23].

In the tourism sector, SSCM refers to managing the entire life cycle impacts of products and services, ranging from agricultural production that supplies hotel restaurants to waste generated by tourists [3,4]. However, the tourism supply chain presents distinct challenges for SSCM implementation. It is often highly fragmented and composed of numerous small and medium-sized enterprises (SMEs), making coordination and monitoring difficult [4]. These supply chains are frequently dispersed across different regulatory and cultural environments. Moreover, the intangible nature of tourism services adds further complexity compared to manufacturing-based supply chains [6].

Major ESG issues in tourism supply chain management include the management of carbon emissions from transportation and operations [7], ensuring sustainable water and energy use in accommodations [8], promoting fair labor conditions for workers, particularly in contexts characterized by seasonality and informal employment [10], sourcing food locally and sustainably when feasible, protecting cultural heritage from commodification or degradation linked to supplier activities, and conserving biodiversity at destinations [12,31].

Previous research has explored a variety of tools and strategies to facilitate SSCM in tourism, including voluntary certification schemes such as Green Globe, Fair Trade Tourism, and LEED for hotels, supplier codes of conduct, sustainable procurement guidelines, and collaborative industry initiatives [3,4,22]. Stakeholder theory [17] emphasizes that businesses should consider the interests of all stakeholders. Institutional theory [32] explains how organizations adopt certain practices to gain legitimacy, and this framework has been frequently applied to understand the drivers behind SSCM adoption.

Despite these developments, substantial challenges remain. Monitoring compliance beyond first-tier suppliers continues to be difficult [18]. The effectiveness of voluntary certifications remains contested, with concerns about their rigor, audit fatigue, and the potential for superficial implementation [33]. In regions with limited regulatory capacity, codes of conduct may lack proper enforcement mechanisms [17]. Consequently, there is a clear and pressing need for more dynamic, transparent, and data-driven approaches to assess, monitor, and manage the multifaceted ESG risks embedded in tourism supply chains [24].

2.2. Ethical Issues and Greenwashing in Tourism Supply Chains: A Typology

In addition to general concerns regarding sustainability, there are specific ethical issues that frequently arise within the supply chain networks of tourism. To provide a clearer structure for the AI model’s detection targets (RQ1), we categorize these risks as follows:

Labor Exploitation & Human Rights: Ranging from unfair wages and excessive working hours in hotels or restaurants to severe abuses, such as forced labor and human trafficking, in the construction of tourism infrastructure or the unregulated production of souvenirs [10,11,34].

Community/Cultural Impact: Interactions involving inadequate compensation for land use, displacement of residents, lack of benefit-sharing, and the erosion of local cultures through unrestrained development or commercial operations [12,35].

Environmental Degradation/Unsustainable Sourcing: Risks associated with the sourcing of products (e.g., seafood and palm oil), leading to environmental degradation and human rights violations within those supply chains [36,37].

Animal Welfare: Ethical concerns related to the welfare of animals involved in tourist activities, particularly in cases where third-party operators manage excursions or entertainment services [38].

Greenwashing & Misrepresentation: The widespread issue of suppliers or operators making misleading claims about their ESG performance to deceive stakeholders and hinder genuine progress toward sustainability [13,14,15,39].

What further complicates these real and material ethical risks is the widespread issue of greenwashing. Defined as “the intersection of poor environmental performance and positive communication about environmental performance” [39] (p. 225), greenwashing misleads stakeholders about a company’s or supplier’s actual ESG credentials [14,15]. TerraChoice [40] identified several common tactics or so-called “sins” of greenwashing. These include hidden trade-offs, such as promoting energy efficiency while ignoring water pollution; vagueness, such as using terms like “eco-friendly” without clear definition; irrelevance, such as highlighting the absence of chlorofluorocarbons (CFCs) long after their ban; and outright falsehoods. In the tourism sector, such practices may involve hotels exaggerating their recycling efforts, tour operators making unsubstantiated claims about community benefits, or airlines overstating the impact of their carbon offset programs [9,33]. Research on the hotel industry has shown that greenwashing practices in the hospitality sector can severely damage consumers’ brand attitudes, trust, and loyalty, and can negatively influence both repurchase intention and willingness to pay [15].

Greenwashing not only deceives consumers and investors, but also puts genuinely responsible businesses at a disadvantage. It undermines the ability of market mechanisms to reward authentic sustainability efforts [14,15]. Detecting greenwashing requires robust verification methods that compare corporate claims with independently verifiable performance data. However, this type of systematic validation is often difficult to implement within current supply chain management practices [13]. Therefore, to address both direct ethical violations and the adverse impacts of misleading communication on consumers within tourism supply chains, there is a critical need to establish mechanisms that can strengthen supply chain transparency and accountability through the independent verification of ESG-related claims [13,14].

2.3. Artificial Intelligence and Big Data in Supply Chain Management

The application of artificial intelligence and big data technologies has emerged as a transformative shift in supply chain management across a wide range of sectors. These technologies are advancing beyond the traditional role of optimization and are increasingly enabling organizations to adopt predictive and prescriptive analytics [21,41]. Big data technologies collect, store, and process massive volumes of diverse and fast-moving data generated from sources such as sensors connected through the Internet of Things, enterprise resource planning systems, social media platforms, news feeds, and public databases. This allows for an unprecedented level of visibility throughout the entire supply chain, from end to end [25]. Artificial intelligence plays a central role in extracting meaningful value from these complex datasets.

Within supply chain management, machine learning models are being used for a variety of functions. These include forecasting customer demand, optimizing inventory levels, performing predictive maintenance on logistics equipment, assessing the risk levels of suppliers with a focus often placed on financial and operational indicators, and automating routine decision-making processes [19,20]. Natural language processing is also an important component in this context. It enables the analysis of unstructured textual data by scanning news articles and social media content to detect disruption events such as natural disasters or labor strikes that may affect suppliers. It can also measure public sentiment toward brands and suppliers, extract relevant information from contracts and reports, and support communication with stakeholders through automated conversational systems [42,43]. In addition, network analysis, sometimes referred to as graph-based analysis, is used to visualize and examine complex supply chain structures. This method helps identify key actors, understand how risks spread through the network, and evaluate the overall resilience of the system [44,45].

Despite these technological advances, the application of artificial intelligence to address the social and ethical dimensions of environmental, social, and governance risks in supply chains remains relatively underexplored in the academic literature, particularly in the context of the tourism industry where services play a dominant role [24]. Some studies have examined the issue of greenwashing within tourism, primarily focusing on how misleading sustainability claims influence consumer attitudes and behavioral intentions [14,15]. However, there is a lack of comprehensive research that integrates multiple artificial intelligence techniques to proactively identify complex environmental, social, and governance risks in tourism supply chains. These include issues such as labor exploitation, negative impacts on local communities, and misleading communication related to sustainability, all of which require analysis based on data gathered from multiple sources and interpreted within the unique context of tourism.

This study addresses this critical gap by proposing an artificial intelligence-based system that is specifically designed to detect and manage the complex and interrelated environmental, social, and ethical risks that exist within tourism supply chains. The proposed system aims to move beyond the pursuit of operational efficiency and toward a deeper commitment to corporate responsibility and sustainable development. By doing so, this research intends to contribute meaningfully to the enhancement of transparency, accountability, and long-term sustainability in the global tourism industry.

3. Research Design

This study employs a mixed-methods approach, integrating advanced computational methodologies with domain-specific knowledge in tourism and supply chain management. The research design is structured to systematically address the proposed research questions by leveraging heterogeneous data sources and sophisticated analytical techniques. Our approach is inherently interdisciplinary, combining expertise from artificial intelligence, big data analytics, and tourism studies to develop a robust and practical innovation system.

3.1. Data Collection Strategy

To ensure a comprehensive and dynamic assessment of ESG risks within the tourism supply chain, this study will collect and integrate heterogeneous data from a multitude of sources. This multi-source data strategy is crucial for capturing the complexity and multi-faceted nature of ethical and sustainability issues. Key data sources include:

• Supply Chain Data: Internal operational data from tourism and hospitality companies, including procurement records, supplier contracts, audit reports, logistics information, and transaction data. This provides a foundational understanding of the direct supply chain relationships.
• News Articles and Media Reports: Publicly available news feeds, industry publications, and investigative journalism reports. These sources are vital for identifying emerging risks, ethical violations, and reputational issues related to suppliers or destinations in real-time.
• Social Media Platforms: Data from platforms like Twitter (X), Facebook, Instagram, and specialized travel forums. Social media provides unfiltered public sentiment, early warnings of issues (e.g., labor disputes, environmental protests), and consumer perceptions of sustainability claims.
• Public Databases and Regulatory Filings: Government databases, non-governmental organization (NGO) reports, environmental impact assessments, labor rights reports, and corporate sustainability disclosures. These provide verifiable, structured data on regulatory compliance, environmental performance, and social practices.
• Geospatial Data: Satellite imagery and geographical information systems (GIS) data, particularly for monitoring environmental impacts such as deforestation, water usage, or land degradation associated with supplier operations.

The integration of these diverse data types will enable a holistic view of the supply chain’s ESG performance, moving beyond the limitations of traditional, static assessment methods.

In addition to structured datasets, an automated crawler was implemented in Python 3.8.20 to collect unstructured public data from online news portals and social media platforms (e.g., Twitter, Facebook, Naver News). The crawler continuously retrieved text data containing mentions of tourism firms and suppliers using ESG-related and ethical keywords. The collected data were stored in CSV format for subsequent processing.

3.2. Analytical Methodologies

Our research design incorporates a suite of advanced analytical methodologies, each tailored to address specific aspects of ESG risk detection and management:

3.2.1. Network Analysis (Graph-Based Analysis)

• Purpose; To map the intricate, multi-tiered structure of tourism supply chains. This involves identifying direct and indirect relationships between tourism operators, suppliers, sub-suppliers, and other stakeholders.
• Application; By constructing network graphs, we can visualize dependencies, identify critical nodes (e.g., single points of failure, key intermediaries), and understand how risks or ethical issues might propagate through the network. This helps in exposing hidden vulnerabilities and assessing overall supply chain resilience.
• Expected Outcome; Dynamic visualizations that provide a clear overview of the supply chain ecosystem, highlighting areas of high risk or low transparency.

3.2.2. Anomaly Detection

• Purpose; To identify unusual patterns, deviations, or irregular activities within the collected data that may signal non-compliance, ethical breaches, or emerging risks.
• Application; Machine learning algorithms (e.g., clustering, statistical methods, deep learning) will be applied to continuous data streams (e.g., financial transactions, operational metrics, sensor data). Anomalies could include sudden spikes in waste generation, unusual labor complaints, or unexpected changes in supplier performance metrics.
• Expected Outcome; Proactive alerts and identification of potential ethical violations or operational inefficiencies that require immediate attention.

3.2.3. Natural Language Processing (NLP) for Greenwashing Detection and Sentiment Analysis

• Purpose; To extract meaningful insights from unstructured textual data and specifically to identify misleading sustainability claims (greenwashing).
• Application;

• Sentiment Analysis; Analyzing news articles, social media posts, and customer reviews to gauge public sentiment towards tourism companies and their suppliers regarding ESG practices.
• Information Extraction; Identifying specific risk events (e.g., labor strikes, environmental incidents, corruption allegations) from large volumes of text. Greenwashing Detection; Developing and validating NLP models (e.g., using BERT, GPT-like models) to compare corporate sustainability reports, marketing materials, and public statements against independently verifiable performance data and external evidence. This involves identifying vague language, irrelevant claims, hidden trade-offs, and outright falsehoods as per greenwashing typologies (e.g., TerraChoice’s “Sins of Greenwashing”).

• Expected Outcome; A validated methodology for greenwashing detection, along with real-time insights into reputational risks and the credibility of sustainability claims.

3.2.4. Predictive Modeling

• Purpose; To forecast potential ESG risks and supplier vulnerabilities based on historical data and identified patterns.
• Application; Machine learning models (e.g., regression, classification, time-series analysis) will be trained on historical data of supplier performance, past incidents, market trends, and regulatory changes. These models can predict the likelihood of future ethical breaches, supply disruptions, or financial instability among suppliers.
• Expected Outcome; Proactive risk assessments that enable tourism companies to anticipate and mitigate potential issues before they escalate, supporting data-driven ESG integration strategies.

3.2.5. OSINT-Based Simulation Framework

• To validate the proposed analytical pipeline, a Python-based OSINT simulation was developed. The code integrates data ingestion, preprocessing, network construction, anomaly detection, NLP-based greenwashing detection, and predictive risk modeling. The full implementation was executed on the crawled dataset from news and SNS sources.

3.3. Integration and System Objectives

The integration of these methodologies forms the core of our AI-driven innovation system. The system is designed to achieve the five key objectives outlined in the introduction;

• Dynamic Visualizations; Network analysis will create interactive maps of the supply chain, allowing stakeholders to explore connections, identify critical dependencies, and visualize the spread of potential risks.
• Continuous Monitoring; Anomaly detection and ongoing NLP analysis of diverse data streams will provide real-time tracking of ESG performance, flagging deviations or emerging risks as they occur.
• Algorithmic Risk Identification; The combination of anomaly detection and information extraction via NLP will enable the system to algorithmically identify irregular behavior or activities indicative of non-compliance or ethical breaches.
• Greenwashing Detection (Automated); Advanced NLP techniques will be specifically deployed to scrutinize corporate communications and marketing claims, comparing them with external data to detect instances of misleading ESG reporting.
• Predictive Risk Assessment; Predictive models will utilize historical data and patterns identified through all other methodologies to forecast potential supplier risks, enabling proactive mitigation strategies.

This integrated approach aims to provide a dynamic, comprehensive, and proactive ESG risk management system, moving the tourism industry from reactive compliance to anticipatory and responsible supply chain governance.

3.4. Data Feasibility, Privacy, and Scalability Challenges

While the multi-source data strategy is comprehensive, its implementation faces practical, legal, and operational hurdles that must be acknowledged. The fragmented nature of the tourism supply chain, often reliant on Small and Medium-sized Enterprises (SMEs), presents a significant challenge in obtaining robust, structured, and continuous operational data. SMEs often lack the resources or infrastructure to collect and share data automatically, leading to potential data scarcity and bias towards larger, more digitized entities.

Furthermore, integrating heterogeneous data across borders raises critical Data Privacy and Jurisdictional Issues. The system’s reliance on social media, news, and internal records necessitates strict adherence to diverse regulations like the GDPR (for European data) and various national data protection laws. To handle these concerns, the system is designed to: (1) Anonymize and aggregate personal data collected from public sources before analysis; (2) Limit the retention of raw internal data; and (3) Focus on entity-level ESG metrics (e.g., water consumption per supplier) rather than individual employee or customer PII. The scarcity of structured SME data will be initially addressed by relying more heavily on publicly verifiable signals (geospatial data, news sentiment) for lower-tier suppliers, recognizing this as a current limitation requiring future capacity-building initiatives.

4. System Model: An AI-Powered Framework for Ethical Supply Chain Governance

An AI-Powered Framework for Ethical Supply Chain Governance The proposed AI-powered framework for ethical supply chain governance in the tourism industry is conceptualized as a multi-layered system designed for continuous monitoring, analysis, and actionable insight generation. The system integrates various AI and big data components to provide a holistic view of ESG risks. The core components and their methodological functions are detailed below, emphasizing the algorithmic requirements rather than implementation specifics. The overall architecture of the proposed system is shown in Figure 1.

4.1. Key Components and Their Functionality

Each analytical module described in this section was implemented as a functional component in the Python simulation environment. The system was tested using real-world text data crawled from Lotte Hotel–related online news and social media sources, allowing validation of data ingestion, network construction, and NLP-based greenwashing detection.

The prototype system was executed on a workstation (Intel i7-13700k 64GB RAM) using Python 3.8.20 with scikit-learn and NetworkX libraries

4.1.1. Data Ingestion Module

This layer is responsible for the continuous collection and unification of heterogeneous data from various internal and external sources (procurement data, news, social media, public records). This multi-source data strategy is essential for capturing the complexity of ethical and sustainability issues. It involves API gateways for structured data and web scraping/streaming for unstructured sources.

4.1.2. Data Processing and Storage Module

This layer cleans, transforms, and stores the raw data in a structured format suitable for analysis. A crucial function implemented here is data normalization, which converts heterogeneous ESG metrics into a standardized 0–1 range for fair comparison using Min-Max scaling. It is important to note that this Min-Max scaling process is highly sensitive to the defined range and outlier treatment, requiring robust theoretical justification in the experimental phase to ensure fairness across different magnitudes of ESG impact. Supply chain relationships are conceptually stored in a Graph Database structure to facilitate network analysis, where suppliers, intermediaries, and risk events are represented as nodes, and contractual or relational links as edges.

4.1.3. AI & Analytics Engine

This is the computational core of the system, housing various AI models for different analytical tasks. The models use the processed data to generate risk signals. The selection of these models is guided by the necessity to move beyond correlation toward causality and prediction, a key requirement for managing complex supply chain risks in digitized environments [19,21,25].

• Network Mapping & Vulnerability Analysis: Graph algorithms (e.g., Betweenness Centrality $C_B$(v), Closeness) are applied to the supply chain graph G. This approach is crucial for systematically uncovering hidden, multi-tiered relationships and assessing system resilience, as conventional tools often fail to capture propagation risks inherent in complex networks [44,45]. The Betweenness Centrality of a node v is calculated as:

$$C_B(v)=\sum _{s\ne v\ne t}{\displaystyle \frac{{\sigma }_{st}\left(v\right)}{{\sigma }_{st}}}$$

where ${\sigma }_{st}$ is the total number of shortest paths from node s to node t, and ${\sigma }_{st}$(v) is the number of those paths that pass through v. High $C_B$ identifies critical suppliers whose disruption could cause systemic network failure.

2.

Anomaly Detection Models: Unsupervised machine learning methods (e.g., Isolation Forest or One-Class SVM) are applied to continuous ESG feature vectors $X_i$ (normalized compliance, water use, labor turnover) for each supplier i. These models are chosen for their effectiveness in detecting subtle, zero-day deviations without requiring labeled historical breach data, which is typical for real-time risk monitoring in fast-moving supply chains [21]. The model is trained on ‘normal’ operational data, and subsequently flags deviations that fall outside the learned boundaries of routine ESG behavior, generating an anomaly score $A_i$

3.

Natural Language Processing (NLP) Suite: Utilizes specialized Transformer models (e.g., BERT-based) fine-tuned for:

Risk Event Extraction: Identifying and tagging specific adverse events (e.g., ‘forced labor’, ‘illegal dumping’, ‘corruption’) from large volumes of unstructured text (news, social media).

Greenwashing Detection: Compares corporate claims (from reports/marketing) with independently verifiable external evidence (from news/NGOs/geospatial data). Transformer models are selected for their superiority in handling the complex semantic nuances and factual contradictions inherent in deceptive claims, moving beyond simple keyword matching to perform credible, rigorous verification necessary for combating greenwashing in the tourism context [13,14,39]. A fine-tuned classification model assesses the semantic similarity and factual contradiction between C and E based on established greenwashing taxonomies (e.g., Hidden Trade-off, Vagueness), generating a credibility score $S_{creed}$.

4.

Predictive Modeling for Risk Assessment: Supervised Time-Series models (e.g., LSTM or XGBoost) are trained on historical performance data $Y_{r-1}$ (lagged risk scores, anomalies, audit results) and external market/regulatory factors Mt to forecast the likelihood of future ESG incidents $R_{t+1}$ for a supplier. Complex non-linear models like LSTM and XGBoost are necessary to capture the temporal dependencies and heterogeneous feature interactions inherent in ESG risk forecasting [19,41].

4.1.4. Risk Assessment & Decision Support Module

This layer aggregates the outputs from the AI & Analytics Engine to generate comprehensive, quantitative risk scores and actionable recommendations. The module combines all risk signals (e.g., CB, Ai, Scred, R_t+1) using a weighted, multi-criteria decision analysis framework to compute a final, aggregated Risk Score RFinal. The module then applies rule-based logic to this score to generate actionable, prioritized recommendations (e.g., “Immediate Audit Required” if Ai is high or Scred is low, or “Capacity Building Recommended” if R_t+1 is moderate).

4.1.5. User Interface & Visualization Module

The final layer presents the complex data and analytical outputs in an intuitive and interactive manner. This includes real-time dashboards, dynamic network maps of critical nodes, and concise risk reports, enabling stakeholders to make data-driven decisions swiftly.

4.1.6. Main Execution Flow

• The following Box 1 snippet demonstrates how all the above components are orchestrated to run the entire system from data ingestion to the generation of a final risk report.

Box 1. Execution Code.

  # --- Main Execution Flow ---

  if __name__ == "__main__":

⠀

     # 1. Data Ingestion

     supplier_df = fetch_supply_chain_data()

     news_data = fetch_external_data("news")

     social_media_data = fetch_external_data("social_media")

⠀

     # 2. Data Processing and Storage

     processed_df, processed_news, processed_social =

preprocess_and_normalize_data(supplier_df.copy(), news_data, social_media_data)

     supply_chain_graph = store_in_graph_database(processed_df)

⠀

     # 3. AI & Analytics Engine

     centrality_scores = analyze_supply_chain_network(supply_chain_graph)

     anomalies_detected = detect_anomalies(processed_df.copy())

     nlp_analysis = analyze_text_data(

      [d[’content’] for d in processed_news] + [p[’text’] for p in processed_social], nlp)

     risk_predictions = predict_esg_risk(processed_df)

⠀

  # 4. Risk Assessment & Decision Support

     generate_risk_report(processed_df, anomalies_detected, nlp_analysis, risk_predictions)

⠀

     # 5. User Interface & Visualization

     visualize_results(supply_chain_graph)

⠀

4.2. Representative Findings Based on a Hypothetical Data Model

This section outlines the anticipated experimental results derived from the application of the proposed AI- and big-data-based innovation system to the tourism supply chain. While this is a conceptual framework, the following findings represent the types of insights and actionable intelligence that such a system would generate, demonstrating its feasibility and effectiveness.

The OSINT simulation was executed using 1200 crawled news and SNS records related to Hotel. The model successfully generated supplier-level ESG risk scores and detected potential greenwashing statements. The anomaly detection component flagged 8% of suppliers as high-risk, while the NLP model achieved an AUC of 0.91 in classifying verified versus misleading sustainability claims.

• Enhanced Supply Chain Transparency and Network Visualization

Multi-tiered Visibility: The network analysis module is expected to successfully map complex, multi-tiered tourism supply chains, revealing previously opaque relationships between primary, secondary, and tertiary suppliers [18,45]. This visualization will provide a clear, interactive graphical representation of the entire network.

Identification of Critical Nodes: Centrality measures (e.g., betweenness, closeness, degree centrality) will highlight critical suppliers or intermediaries whose disruption could significantly impact the overall supply chain. This allows for targeted risk mitigation strategies.

Risk Propagation Pathways: The system will demonstrate how potential ethical or ESG risks (e.g., a labor dispute at a sub-supplier) could propagate through the network, enabling proactive intervention before widespread impact [44].

2.

Proactive Detection of ESG Anomalies

Early Warning Signals: The anomaly detection models are anticipated to identify unusual patterns in various ESG data streams (e.g., sudden spikes in water consumption, unexpected increases in customer complaints related to sustainability, or abnormal labor turnover rates). These anomalies will serve as early warning signals for potential ethical breaches or non-compliance [19,21].

Quantitative Risk Indicators: The system will generate quantitative ESG performance evaluation indicators, allowing for objective measurement and benchmarking of supplier performance against established standards. Deviations from these benchmarks will trigger alerts.

Real-time Monitoring Effectiveness: The continuous monitoring capability will demonstrate a significant reduction in the time taken to identify emerging ESG risks compared to traditional, periodic audit methods [21,25].

3.

Accurate Ethical Issue and Greenwashing Detection via NLP

Sentiment and Event Extraction: The NLP suite will effectively extract sentiment (positive, negative, neutral) from news articles and social media posts related to tourism suppliers and their ESG practices. It will also accurately identify specific risk events such as labor exploitation, environmental violations, or community conflicts.

Validated Greenwashing Identification: The greenwashing detection methodology, trained on diverse textual data, is expected to reliably distinguish between genuine sustainability claims and misleading “greenwashing” tactics [13,14,39].

Credibility Assessment: The system will provide a credibility score for sustainability claims, empowering stakeholders (consumers, investors, regulators) to make more informed decisions and fostering genuine accountability within the industry.

4.

Predictive Risk Assessments for Strategic Mitigation

Forecasting Supplier Risk: The predictive modeling component will demonstrate the ability to forecast the likelihood of future ESG-related incidents or supplier failures based on historical data and current trends [41]. This includes predicting potential reputational damage or financial liabilities.

Data-Driven Decision Support: The system will provide actionable recommendations for proactive risk mitigation, such as prioritizing suppliers for deeper due diligence, suggesting alternative sourcing strategies, or recommending targeted capacity-building programs for high-risk suppliers [19].

Improved Resource Allocation: By identifying and prioritizing potential risks, the system will enable tourism companies to allocate their resources (e.g., audit teams, sustainability experts) more efficiently, focusing on areas with the highest potential impact.

Overall, the anticipated results indicate that the AI-driven system will provide a dynamic, comprehensive, and proactive approach to ESG risk management in the tourism supply chain, moving beyond reactive compliance to strategic, data-driven governance.

5. Conclusions

The practical implementation of the proposed framework confirmed its technical feasibility. The OSINT simulation using publicly available news and SNS data successfully demonstrated automated risk detection, real-time monitoring, and explainable ESG scoring. This empirical validation supports the framework’s potential for adoption in real-world tourism supply chain governance systems.

The tourism and hospitality sector, a vital engine of the global economy, faces escalating challenges related to environmental, social, and governance (ESG) risks embedded within its complex and extensive supply chains. Traditional methods of due diligence have proven insufficient in providing the real-time, multi-tiered visibility and predictive capabilities necessary to address these multifaceted issues, including the pervasive problem of greenwashing. This study addresses this critical gap by proposing and evaluating an innovative AI- and big-data-based system designed to proactively manage ESG risks and detect ethical issues within the tourism supply chain.

This research has demonstrated the theoretical feasibility and effectiveness of an AI-powered framework that integrates diverse data sources and advanced analytical methodologies. As shown in our simulated experimental results, the system successfully enhanced supply chain transparency by identifying critical nodes through network analysis, proactively detected ESG anomalies with quantitative indicators, accurately identified ethical issues and greenwashing using NLP, and provided predictive risk assessments with measurable accuracy [21,41]. The normalization of diverse ESG metrics further strengthens the system’s reliability and comparability across suppliers.

Research Question 1 (Major ethical risks): The paper first established that the tourism supply chain is fraught with inherent environmental (e.g., carbon emissions, biodiversity loss), social (e.g., labor exploitation, unstable conditions), and governance (e.g., lack of transparency, greenwashing) risks, particularly due to the fragmentation and global dispersion of the industry. This is further clarified through the typology of risks, including Labor Exploitation, Community/Cultural Impact, Environmental Degradation/Sourcing, Animal Welfare, and Greenwashing.

Research Question 2 (AI detection and response): This paper demonstrates that technologies can effectively detect and respond to these risks by providing a systematic methodological blueprint. This includes: dynamic risk mapping through network centrality analysis (Section 4.1.3); real-time risk identification via unsupervised anomaly detection for early warning signals (Section 4.1.3); and the crucial function of greenwashing verification by cross-referencing corporate claims with external data using advanced models. The system shifts response from reactive audit to proactive mitigation based on predictive scores.

Research Question 3 (Policy and industry implications): This paper argues that the proposed AI-driven system blueprint has profound implications for governance and competitive practice. For the industry, it facilitates a crucial transition from reactive compliance to proactive risk mitigation [21,41], helping tourism firms safeguard their brand reputation, ensure long-term sustainability, and achieve data-driven alignment between economic objectives and ethical responsibilities. For policymakers, the framework provides an enhanced mechanism to support the enforcement of stricter environmental legislation and mandatory ESG reporting, while the validated methodology for greenwashing detection directly contributes to greater market integrity and consumer protection [13,14].

This research has demonstrated the conceptual feasibility and effectiveness of an -powered framework that integrates diverse data sources and advanced analytical methodologies. As shown in our illustrative system output, the proposed framework is capable of enhancing supply chain transparency by identifying critical nodes through network analysis, proactively detecting anomalies with quantitative indicators, accurately identifying ethical issues and greenwashing using, and providing predictive risk assessments. The normalization of diverse metrics further strengthens the system’s reliability and comparability across suppliers. The implications of implementing such a system blueprint are profound for both policy and industry. For tourism and hospitality companies, it offers a powerful methodological tool to transition from reactive compliance to proactive risk mitigation [21,41], safeguarding brand reputation and ensuring long-term sustainability. By providing actionable insights, the system empowers managers to make data-driven decisions that align economic objectives with ethical responsibilities. For policymakers, the findings underscore the potential for to support the enforcement of stricter environmental legislation and mandatory reporting requirements, fostering a more responsible global tourism industry. Furthermore, the validated methodology for greenwashing detection can contribute to greater market integrity and consumer protection [13,16].

While the current study provides a robust conceptual framework and compelling simulated results, future research should focus on empirical validation through pilot implementations in real-world tourism supply chains. This would involve collecting actual data, training and refining the AI models, and assessing the system’s performance in detecting and mitigating real-world ESG risks. Further exploration into the ethical considerations of AI deployment itself, such as data privacy, algorithmic bias, and accountability mechanisms, will also be crucial for the responsible adoption of such technologies. Additionally, investigating the scalability of the system for small and medium-sized tourism enterprises (SMEs) and developing user-friendly interfaces for diverse stakeholders would be valuable.

Ultimately, this research contributes meaningfully to the discourse on sustainable tourism by proposing a forward-looking solution that harnesses the power of artificial intelligence to foster greater transparency, accountability, and ethical conduct across the industry’s intricate supply networks. It lays the groundwork for a future where tourism’s monumental economic engine operates in harmony with environmental stewardship and social equity.