AI Hospitality Alliance

Research

Academic articles and research signals

The Research Hub curates academic work on AI in hospitality for industry practitioners, operators, and technology leaders. It is designed to help bridge academia and day-to-day business decisions by translating published research into a more usable knowledge base for hotels and hospitality organizations. The collection spans operations, revenue strategy, guest experience, distribution, robotics, and other emerging applications of AI across the sector.

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Academic ResearchMarch 2, 2026Frontiers

Robots, ledgers, and RevPAR: a blockchain-enabled AI–robotics conceptual model for sustainable hotel revenue and asset management

Leonard A. Jackson

This article proposes a conceptual model integrating AI, robotics, and blockchain technologies to enhance hotel revenue management, sustainability, and asset management. It highlights how these technologies can jointly improve financial performance (e.g., RevPAR), operational efficiency, environmental impact, and long-term asset value, while addressing challenges related to governance, ethics, privacy, and organizational readiness.

AIRoboticsBlockchainHotel Revenue ManagementSustainabilityAsset ManagementHospitality TechnologyRevenue StrategyHotel Operations
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Academic ResearchJanuary 16, 2026Emerald Insight: Tourism Review

Generative AI in hospitality and tourism: a dual-stakeholder perspective on tourist and workforce experience dynamics

Maria Leonor Ferreira

This paper looks at how AI improves short-term rental management using Solarento as an example. AI helps optimize pricing, forecast demand, reduce costs, and improve occupancy and guest experience. However, challenges include reliance on good data, limited transparency, and ethical concerns. The study recommends combining AI with human oversight to ensure balanced, responsible, and effective use in hospitality operations.

Guest ExperienceReviews & SentimentGenerative AITourism
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Academic ResearchJanuary 1, 2026F1000Research

Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review

Pankaj Kumar Tyagi, Priyanka Aggarwal, Priyanka Tyagi, Asokan Vasudevan, Premendra Kumar Singh

This study systematically reviews 320 peer-reviewed articles from 2003 to 2025 on artificial intelligence (AI) applications in tourism and hospitality marketing. Using bibliometric and thematic analyses, it identifies key publication trends, influential journals and authors, and emerging research themes. The findings reveal a significant growth in AI-related tourism marketing research since 2017, highlighting four main thematic clusters: digital influence and tourist behavior analytics; AI-enabled smart tourism and commerce ecosystems; technology-driven hospitality and experience innovation; and data-driven decision making in predictive tourism modeling. The review underscores AI's transformative role in enhancing personalized marketing, customer engagement, and operational decision-making in the hospitality sector, while also noting challenges related to ethics, data privacy, and maintaining human touch in service.

Artificial IntelligenceTourismHospitalityMarketingBibliometric AnalysisThematic AnalysisCustomer ExperienceData-Driven Decision MakingSmart TourismPersonalization
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Academic ResearchNovember 1, 2025Journal of Tourism, Hospitality and Travel Management

Adoption of Artificial Intelligence (AI) Technology in Enhancing Tourist Experience: A Conceptual Model

Tomy Andrianto

This article proposes a conceptual model for adopting Artificial Intelligence (AI) technology to enhance tourist experiences. It highlights the potential of AI to transform tourism by personalizing services, improving operational efficiency, and enriching guest interactions, offering practical insights for hospitality professionals aiming to leverage AI in their strategies.

Artificial IntelligenceTourismHospitalityGuest ExperienceRevenue ManagementOperationsTechnology Adoption
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Academic ResearchOctober 1, 2025IBIMA Publishing

Smart Rentals, Smarter Profits: How Artificial Intelligence Improves Operational and Financial Efficiency in Short-Term Apartment Management

Maciej SIKORSKI

This study investigates how artificial intelligence (AI) enhances operational and financial performance in short-term apartment rentals, focusing on a Polish operator, Solarento. It finds that AI-driven revenue management and automation reduce costs and vacancy, improve guest experience, and increase owner satisfaction through transparent, adaptive pricing. However, challenges like data reliance and ethical concerns require balancing AI with human oversight.

short-term rentalartificial intelligencerevenue managementoperational efficiencyautomationguest experienceowner satisfactiondigital transformation
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Academic ResearchJuly 21, 2025Tourism Review

Gaining Ground: How Technology Fuels Hotel Competitiveness — A Systematic Review of the Literature

Saule Yolcu, Alperen Şahin, Taşkın Dirsehan

This paper reviews a large body of research and comes to a practical conclusion: technology strengthens hotel competitiveness when it is tied to real business performance, not treated as a side initiative. The strongest benefits appear in operational efficiency, better decision-making, stronger digital capability, sustainability, and more personalized guest experience. For hotel leaders, the key takeaway is that technology should be evaluated like any other strategic investment. If it helps the property run more efficiently, respond faster to demand, improve the guest journey, or strengthen long-term market position, it is directly contributing to competitive advantage.

Revenue ManagementOperationsGuest ExperienceReviews & Sentiment
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Academic ResearchJune 23, 2025ROBONOMICS: The Journal of the Automated Economy

Can robots substitute human receptionists? Results from a field experiment

Klaas Koerten

This study tested a robot receptionist in a hotel lobby to see if it could effectively replace human receptionists. While guests felt less social presence with the robot, the number of requests solved and overall hospitality experience were similar to human staff. However, some key reception tasks remain unautomated, so robots cannot yet fully replace human receptionists.

hospitality roboticsservice robotshuman-robot interactionhotel receptionguest experienceoperational efficiency
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Academic ResearchJune 23, 2025Hotel Yearbook

The Hospitality Paradox: Embracing Automation While Protecting Jobs

Klaas Koerten

Hospitality faces a paradox where labor shortages and low job satisfaction persist, especially in housekeeping, while automation technologies are advancing but not yet ready to replace complex hospitality tasks. Hotels should focus on using technology to improve employee well-being and job quality rather than assuming full automation will solve staffing issues.

hospitalityautomationlabor shortageshousekeepingemployee well-beingtechnologysustainable employmentoutsourcingrobotics
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Academic ResearchMay 19, 2025International Journal of Research and Innovation in Social Science (IJRISS)

AI-Driven Hyper-Personalization in Hospitality: Application, Present and Future Opportunities, Challenges, and Guest Trust Issues

Raihana Akter Nira

This article reviews how artificial intelligence (AI) enables hyper-personalization in hospitality, enhancing guest experiences and operational efficiency. It highlights AI applications like chatbots, virtual assistants, and AI-powered booking systems that tailor services to individual preferences. The paper also discusses challenges such as privacy concerns, technology anxiety, and trust issues, emphasizing the need for balancing personalization benefits with ethical considerations. Practical recommendations are offered for hospitality professionals to optimize AI use while maintaining guest trust.

Artificial IntelligenceHyper-PersonalizationHospitality TechnologyGuest ExperienceCustomer TrustPrivacyAI ApplicationsHotel Operations
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Academic ResearchDecember 10, 2024Current Issues in Tourism

AI and the Future of Talent Management

Georges El Hajal, Ian Yeoman

This paper argues that future hospitality performance will depend less on having more staff and more on having teams that can work effectively with AI. Hotels that begin building AI literacy now through hiring, training, and manager development are likely to be in a stronger position than those that wait. The research also points to practical uses in workforce planning, especially smarter scheduling and better use of labor data. The business implication is that AI should be used to improve decision-making, strengthen productivity, and support employee development, rather than being seen only as a tool for reducing labor cost.

OperationsGuest Experience
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Academic ResearchNovember 26, 2024Businesses (MDPI)

A Change Management View on Technology Adoption in Hotel Organizations

F. A. R. Somera, K. Petrova

Hotels consistently underperform on technology ROI not because they buy the wrong tools but because they underinvest in the organizational change required to use them well. This paper finds that staff 'digital readiness' — how prepared, involved, and confident employees feel about new technology — is a stronger predictor of AI deployment success than the technology's own capabilities. The practical prescription: budget for change management at roughly 20–30% of the technology spend, involve frontline staff in selection and piloting before launch, and communicate the 'why' explicitly and repeatedly. Teams that feel informed and equipped adopt faster and perform better than teams that are simply trained.

Guest ExperienceReviews & SentimentEthics
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Academic ResearchJune 5, 2024Journal of Modern Hospitality

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

A. Gatera

AI-based pricing tools improve hotel RevPAR by enabling continuous, real-time price adjustments that human revenue managers simply can't execute manually at the same speed or volume. The documented improvements across studies range from 3% to 12%, with the gains driven primarily by faster response to demand signals and better segmentation — capturing full-rate business travelers while staying competitive for leisure segments. The paper is equally clear about the three barriers: messy or fragmented data, technology integration complexity in legacy hotel stacks, and the skills gap on revenue management teams that still need to evolve from rate-setters to data analysts. The financial case for overcoming these barriers is strong; the competitive cost of not overcoming them is growing.

Revenue ManagementGuest ExperienceTourismReviews & SentimentEthics
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Academic ResearchJune 3, 2024British Food Journal

Machine Learning for short‑term property rental pricing based on seasonality and proximity to food establishments

D. de Jaureguizar Cervera, J. de Esteban Curiel, D. C. Pérez‑Bustamante Yábar

Analyzing 220 Airbnb listings in Madrid, this study uses machine learning to show that short-term rental properties don't compete in one single market — they cluster into distinct sub-markets, each with its own optimal pricing rhythm. Some properties should hold high base rates year-round; others need aggressive seasonal adjustment. Location matters in a specific way that's operationally useful: proximity to everyday amenities like supermarkets and restaurants is a stronger price driver than proximity to tourist attractions, reflecting the shift toward travelers wanting to 'live like a local.' For hotel revenue managers competing against short-term rentals, this research reinforces the value of hyper-local competitive set analysis over broad market comparisons.

Revenue ManagementOperationsGuest ExperienceTourism
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Academic ResearchMay 28, 2024Tourism and Management Studies

Hotel demand forecasting models and methods using artificial intelligence: a systematic literature review

H. Henriques

A systematic review of AI-based hotel demand forecasting finds that machine learning models consistently outperform traditional statistical methods — particularly during volatile periods, around irregular events, and when enriched with external data like competitor rates and local event calendars. The performance gap is largest for hotels in dynamic markets; in stable, predictable markets the advantage is smaller but still present. The most important practical finding is that data quality matters more than model choice: a well-implemented simple model trained on clean data beats a sophisticated model trained on messy data every time. Before investing in forecasting technology, audit and fix your data infrastructure.

Revenue ManagementGuest ExperienceReviews & Sentiment
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Academic ResearchJanuary 1, 2024International Journal of Hospitality Management

Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis

Daniele Contessi, Luciano Viverit, Luis Nobre Pereira, Cindy Yoonjoung Heo

Most AI forecasting models are 'black boxes' that revenue managers don't fully trust — and tools you don't trust don't get used consistently. This paper proposes a machine learning approach that groups historical booking patterns into recognizable clusters (early corporate, last-minute leisure, event-driven spike, etc.) before applying a forecasting model to each, making the prediction explainable: not just 'occupancy will be 87%' but 'because booking pace matches the early corporate pattern, which historically reaches 87%.' The result is both more accurate than standard methods and more operationally useful, because revenue managers can see the reasoning and make better decisions around it.

Revenue ManagementOperationsEthics
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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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Academic ResearchDecember 1, 2022Journal of Business Research‑Turk

Applications and Implications of Service Robots in Hospitality Sector: A Case Study

D. Bağıran Özşeker, E. Aktaş, O. A. Kurgun

A real-world case study of a hotel that deployed service robots found that robots work best when they're assigned specific roles — from handling routine deliveries to creating memorable guest moments — rather than being treated as generic automation. The biggest factor in success wasn't the technology itself but how well management prepared staff and redesigned workflows around it. Hotels that framed robots as tools to support employees (not replace them) saw faster adoption and better guest satisfaction scores.

RoboticsOperationsGuest Experience
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Academic ResearchDecember 1, 2022International Journal of Computer Trends and Technology

Future Service Robots: A Review of the Psychological Processes Involved in the Tourism and Hospitality Industries

T. C. Teo

Guests don't react to robots rationally — they react emotionally. This research shows that the same robot performing the same task can delight one guest and unsettle another, depending on factors like how human-like the robot feels, how predictable it behaves, and whether the guest was expecting it. For operators, the takeaway is clear: robot deployment requires as much attention to experience design and guest communication as to the technology itself. Giving guests an easy opt-out and making robots feel familiar rather than surprising leads to consistently better outcomes.

RoboticsOperationsGuest ExperienceTourismReviews & Sentiment
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Academic ResearchJune 24, 2022International Journal of Contemporary Hospitality Management

Deep learning in hospitality and tourism: a research framework agenda for future research

A. Essien, G. Chukwukelu

This paper reviews five years of AI research in hotels and travel and finds that deep learning — the technology behind modern AI — is already delivering real value in three areas: reading and categorizing thousands of guest reviews overnight, analyzing images for operational and marketing use, and forecasting hotel demand more accurately than traditional methods. The barrier to entry isn't the technology anymore; it's clean data and basic analytical capability. Hotels that start with review analysis get the fastest return for the least investment.

Revenue ManagementOperationsGuest ExperienceTourismReviews & Sentiment
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Academic ResearchMarch 1, 2022Journal of Business Research

Global Trends in Hospitality

Lerzan Aksoy, Tarik Dogru, et al.

A wide-ranging analysis of post-pandemic hospitality research found that digital transformation and AI are now the top strategic priorities across both academia and industry — not a future trend but a present competitive necessity. Hotels that had invested in flexible digital infrastructure before the pandemic recovered faster and more profitably. On the revenue side, AI-powered dynamic pricing and personalization are the clearest value drivers, and properties still relying on manual processes are falling behind measurably.

Revenue ManagementOperationsGuest ExperienceTourism
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