Academic ResearchJuly 24, 2026Tourism and Hospitality (MDPI)

A Sustainability Assessment of Artificial Intelligence Applications in Tourism Management Using an Entropy–TOPSIS Framework

This paper builds an Entropy–TOPSIS framework to rank six AI applications used in tourism management — Generative AI, AI chatbots, digital twin systems, smart destination management, smart governance, and open data tourism platforms — against ten economic, environmental, social, managerial, and ethical criteria. Smart destination management and digital twin systems come out on top, with personalization capability, privacy/ethical risk, and energy efficiency the most discriminating criteria.

Authors

Zeynep Bayramoğlu

Article content

What the paper studied

Existing research on AI in tourism tends to look at individual technologies in isolation, without a common way to compare their sustainability performance. This study evaluates and ranks six major AI applications in tourism management from a sustainability perspective using an integrated Entropy–TOPSIS multi-criteria decision-making framework. The six applications assessed are Generative AI Systems, AI Chatbots, Digital Twin Systems, Smart Destination Management, Smart Governance, and Open Data Tourism Platforms. Ten evaluation criteria spanning economic, environmental, social, managerial, and ethical dimensions were drawn from a literature review; Entropy fixed the objective weights and TOPSIS produced the rankings.

Key findings

Personalization capability (weight 0.2217), privacy and ethical risk (0.2190), and energy efficiency (0.2108) were the most discriminating sustainability criteria.
Smart Destination Management ranked highest overall (CC = 0.5968), followed by Digital Twin Systems (0.5950) and Open Data Tourism Platforms (0.4978).
AI applications that combine sustainability-oriented destination management, operational intelligence, and personalized visitor services outperform those focused on isolated technological functions.

Why it matters for hospitality and tourism

Destinations and tourism operators are being pushed to invest in AI while also meeting sustainability goals. This paper gives them a defensible way to compare candidate AI investments — not just by hype or vendor claims, but against a common set of economic, environmental, social, managerial, and ethical criteria. The finding that integrated destination-level solutions beat single-purpose tools has direct implications for how DMOs and tourism boards prioritize spend.

Practical takeaways

Use a multi-criteria framework (like Entropy–TOPSIS) rather than intuition when comparing AI vendors or applications for sustainability impact.
Weight personalization capability, privacy/ethical risk, and energy efficiency heavily — they discriminate performance most.
Favor AI investments that operate at the destination or ecosystem level (smart destination management, digital twins, open data platforms) over narrow point solutions.

Artificial Intelligence and Sustainability in Hospitality: A Systematic Review and Future Research Agenda

A systematic review of 54 studies from seven leading hospitality and sustainability journals finds that research linking AI to sustainability has surged, with 63% published in 2025–2026, and centers on social and environmental outcomes, service robots and conversational or generative AI. The evidence is thin on real behavior: 70% of studies rely on self-reported perceptions or intentions, only four use actual behavioral or operational data, and AI for energy and water management and ESG reporting is not directly addressed.

Academic ResearchAugust 20, 2026

An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction

A three-part workflow combining a chatbot-administered survey, machine-learning data processing, and LLM-based forecasting to predict weather-sensitive transportation mode choice. Across 454 student observations under five weather conditions, the best vision-augmented LLM configuration reached 71.5% five-class accuracy, showing how multimodal context can strengthen travel demand forecasting.

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