AI Hospitality Alliance

Research Archive

2023

Archived AI Hospitality Alliance research published in 2023.

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Academic ResearchDecember 28, 2023Preprints.org

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Nadia Malik, Muhammad Bilal

Guest reviews on TripAdvisor, Google, and Booking.com are one of the most valuable — and most underused — data sources in hospitality. This paper surveys the AI methods available to analyze them at scale, from basic sentiment scoring (is this review positive or negative?) to advanced models that can identify exactly which service element a guest is praising or complaining about, detect sarcasm, and process reviews in multiple languages. The practical upshot: AI review analysis tools are now affordable and accessible for individual properties, not just chains, and hotels that use them systematically to spot operational issues early have a measurable reputation management advantage over those that don't.

Generative AIRevenue ManagementOperationsGuest ExperienceTourismReviews & Sentiment
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Academic ResearchAugust 15, 2023Journal of Sustainable Tourism

Intelligent Automation for Sustainable Tourism

Gilang Maulana Majid, Iis Tussyadiah, et al.

AI-driven automation and sustainability are more closely linked than most operators realize. This paper finds that energy management systems using AI to optimize heating, cooling, and lighting based on real-time occupancy consistently cut energy use by 15–30%, and AI-powered food purchasing tools can significantly reduce restaurant waste by forecasting demand more accurately. The paper frames these not as sustainability initiatives but as operational efficiency improvements that happen to have environmental benefits — meaning the business case is strong even for operators who don't prioritize ESG, and even stronger for those who do.

Revenue ManagementOperationsGuest ExperienceTourism
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Academic ResearchJuly 4, 2023Behavioral Sciences (Basel)

Research on the Frontier and Prospect of Service Robots in the Tourism and Hospitality Industry Based on International Core Journals: A Review

M. Chen, X. Wang, R. Law, M. Zhang

Analyzing 284 service robot studies, this paper maps the current state of evidence on robots in hotels and tourism into four clear categories: what we know about the technology itself, how guests respond, how staff are affected, and how the physical environment shapes outcomes. The COVID-19 period accelerated robot adoption significantly, generating enough real-world deployment data that operators no longer need to rely on theory or vendor promises. The most underdeveloped area in the research — and in most hotel deployment plans — is the employee side: how staff experience working alongside robots, and what organizational support they need to adapt, has received far less attention than guest experience.

RoboticsOperationsGuest ExperienceTourismReviews & Sentiment
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Academic ResearchJune 30, 2023Current Issues in Tourism

Artificial intelligence’s impact on hospitality and tourism marketing: exploring key themes and addressing challenges

Jacques Bulchand-Gidumal, Eduardo William Secin, Peter O’Connor, Dimitrios Buhalis

AI is turning hospitality marketing from segment-level targeting into genuine one-to-one personalization — tailoring offers, messages, and timing to individual guests based on their history and behavior. The paper introduces the concept of the 'augmented marketer': rather than replacing marketing staff, AI expands what they can do, letting smaller teams manage more channels, run more targeted campaigns, and respond faster. The practical warning is clear though: all of this depends on having unified, clean guest data. Without it, AI marketing tools underperform their potential significantly.

Revenue ManagementOperationsGuest ExperienceTourism
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Academic ResearchJune 7, 2023International Journal of Contemporary Hospitality Management

Leveraging ChatGPT and other generative artificial intelligence (AI)‑based applications in the hospitality and tourism industry: practices, challenges and research agenda

Y. Dwivedi et al.

This research by leading hospitality academics maps where generative AI (ChatGPT-style tools) is delivering real value now versus where it's still unproven. The clearest wins today are multilingual guest communications, first-draft content creation, and helping staff access information faster during service interactions. The paper is equally direct about what's not ready: governance frameworks for AI-generated guest communications don't yet exist, most hospitality teams haven't been trained to work alongside AI, and the regulatory environment around automated customer service is still evolving. Use it aggressively in low-stakes workflows; build your oversight processes before scaling to anything guest-facing or revenue-critical.

Generative AIRevenue ManagementOperationsGuest ExperienceTourismReviews & Sentiment
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Academic ResearchApril 4, 2023Tourism Review

ChatGPT for tourism: applications, benefits and risks

Ines Carvalho, Stanislav Ivanov

One of the first academic papers to map ChatGPT's real applications in hospitality, this study identifies the clearest wins as customer service automation (handling routine queries 24/7 in multiple languages), content creation (drafts for listings, emails, and social posts at a fraction of the usual time), and back-office productivity. It also issues an honest warning: language models sometimes produce plausible-sounding but factually wrong output, which in hospitality — where accuracy about pricing, amenities, and policies matters — requires human review before anything goes live. Start with low-risk, high-volume tasks and build review processes before scaling.

Generative AIRevenue ManagementGuest ExperienceTourismReviews & SentimentEthics
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Academic ResearchFebruary 3, 2023Administrative Sciences

Artificial Intelligence in the Tourism Industry: An Overview of Reviews

M. A. García‑Madurga, A. J. Grilló‑Méndez

This paper is helpful because it shows where AI already has strong evidence behind it and where more caution is still needed. The clearest benefits are in efficiency, personalization, forecasting, and customer communication. At the same time, the research highlights issues that operators cannot ignore, including privacy, guest trust, staff impact, and the challenge of adopting AI in smaller or less data-rich businesses. For hotel leaders, the practical message is to move ahead in areas where the value is well established, while taking a more deliberate approach to ethics, employee transition, and whether a tool genuinely fits the business.

RoboticsRevenue ManagementOperationsGuest ExperienceTourismReviews & SentimentEthics
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Academic ResearchJanuary 31, 2023Tourism and Hospitality Research

Robot‑delivered tourism and hospitality services: How to evaluate the impact of health and safety considerations on visitors’ satisfaction and loyalty?

M. Soliman, S. Gulvady, A. M. Elbaz, M. Mosbah, M. S. Wahba

This study shows that guest satisfaction with robot-delivered service depends on more than the robot itself. Guests respond more positively when they feel comfortable, understand what the robot is doing, and trust the environment around the interaction. In practical hotel terms, robots work better when the arrival experience is smooth, the technology feels safe and predictable, and staff communicate clearly about its purpose. The business takeaway is that robot service should be designed as part of the overall guest experience, with attention to trust, safety, and context, not treated as a standalone hardware purchase.

RoboticsOperationsGuest ExperienceTourismEthics
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Academic ResearchJanuary 1, 2023Journal of the Academy of Business and Emerging Markets

Chatbots in hospitality and tourism: a bibliometric synthesis of evidence

F. M. Khan, M. K. Azam

This paper finds that chatbots are most effective when they are used for clearly defined, high-volume tasks such as answering common questions, supporting booking inquiries, and handling routine service requests. Guest satisfaction stays much higher when the chatbot is reliable and its role is easy to understand, but it drops when the bot is expected to handle complex or sensitive issues. One of the clearest findings is that trust matters: guests respond better when they know they are speaking with a bot and can quickly reach a human when needed. For hotels, the practical lesson is to use chatbots to streamline routine service while keeping a clear handoff to staff for more nuanced situations.

Revenue ManagementGuest ExperienceTourismEthics
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Academic ResearchJanuary 1, 2023International Journal of Current Science (IJCSPUB)

Time series and machine learning for hotel revenue management: a review of recent advances and implications

G. Chopra, A. Kumar

The most effective hotel demand forecasting systems don't choose between traditional statistical methods and machine learning — they combine both. Traditional models handle stable seasonal patterns reliably; ML models excel at picking up irregular demand signals like competitor pricing moves, local events, and booking pace anomalies. Hotels using hybrid approaches are reporting RevPAR improvements of 3–8% over purely manual revenue management, with the biggest gains in volatile markets. The practical barriers are data quality (ML needs clean historical records) and team capability (revenue managers need training to interpret and act on model outputs), but the financial case for addressing both is clear.

Revenue ManagementReviews & Sentiment
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