Our research teams include multi-disciplinary collaborations including travel, tourism and hospitality, information search and retrieval, impact assessment, forecasting, social media and social inspiration, business information system and strategic management and planning, computer science and symbolic algebraic computation, environmental engineering, and AI applications. We provide knowledge management, population mobility, information-to-knowledge transformations, user decision modeling and usage analysis, and context-aware systems for the needed academia and industry sectors.
Selected Examples of Funded Projects:
Economic Impact analyses; Forecasting Modeling; Input-output models; Spatial Analyses; Data Mining; Social media content, customer locations analyses; Meta-analyses; Sustainable business modules; Price and revenue optimizations
Sponsors: local, city, state, federal agencies; public and private sectors including National Park Service, State Department of Transportation, Attractions, and others.
Academic Certificates
AI/Data Analytics in Tourism, Hospitality and Event Management Graduate Certificate
AI/Data Analytics in Tourism, Hospitality and Event Management Undergraduate Certificate
EFTI AFFILIATED AI SCHOLARS and EXPERTS
Hsing Kenneth Cheng, Ph.D. - Profile
Damon Woodard, Ph.D. - Profile
Olivia Paradis, Ph.D. - Profile
Daniel Capecci, Ph.D. candidate - Profile
Awards and Recognition
The Data Science and Analytics in THEM group is nationally and internationally recognized by its versatility and comprehensiveness of their work.
SELECTED MEDIA COVERAGE
[May 11, 2024]. Students Learn AI to Prepare for Hospitality Careers. https://learningenglish.voanews.com/a/students-learn-ai-to-prepare-for-hospitality-careers/7604858.html
[Feb. 23, 2024] Hospitality, Financial, and Technology Professionals. Colleges are using AI to prepare hospitality workers of the future | HFTP https://www.hftp.org/news/4120567/colleges-are-using-ai-to-prepare-hospitality-workers-of-the-future#:~:text=Will%20AI%20reduce%20the%20number,of%20jobs%20in%20hospitality
[February 22, 2024]. The Conversation. “Colleges are using AI to prepare hospitality workers of the future.” - https://theconversation.com/colleges-are-using-ai-to-prepare-hospitality-workers-of-the-future-222952 (Dr. Rachel J.C. Fu as an author)
[March 2, 2023]. Condé Nast Traveler. "AI Chatbots Want to Plan Your Future Trips—Should You Let Them?" https://www.cntraveler.com/story/ai-chatbots-future-of-travel
Featured Projects and Articles
Fu*, R.J.C. (2025). Chapter 1-16. “Artificial Intelligence, Machine Learning, and Robotic in General Businesses”. 1st Edition. [Book. Publisher: KendallHunt.]
Fu*, R.J.C. (2024). “Artificial Intelligence, Machine Learning, and Robot Applications in Hospitality Businesses”. 1st Edition. [Book. Publisher: KendallHunt.]
Fu*, R.J.C. (2025). Innovations Across Business, Health, Space, and Sustainability. AI, ML, and Robotics in Business. 1(1), 1-7. https://doi.org/10.32473/aimlrb.1.1.138291
Fu*, R.J.C. (2025). The Future of AI, ML, and Robotics in Business and Beyond. AI, ML, and Robotics in Business. 1(1), 8-22. https://doi.org/10.32473/aimlrb.1.1.138290
Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics (Link)
Seunghyun Brian Park, Jinwon Kim, Yong Kyu Lee, Chihyung Michael Ok
Employing the convergence of social media analytics and geospatial analytics, this paper visualized cohesive places where Disneyland visitors express distinct types of emotion in social media messages. Text mining analysis and GIS-based exploratory spatial data analysis showed that tweets reflecting each quadrant of emotions have considerable spatial variations and different topics related to visitor emotions. This study highlights methodological implications of visualizing spatial patterns of visitors' emotions and provides practitioners with a useful guide to develop routes evoking pleasant emotions.
Tourism clusters and peer-to-peer accommodation (Link)
Yong-Jin AlexLee, Seongsoo, Jang Jinwon Kim
This study examines the importance of tourism clusters in peer-to-peer accommodation. Based on a rich dataset of 112,748 Airbnb listings in Florida, one of the top U.S. tourism destinations, this study uses geographically weighted regression to explore the spatially heterogeneous effects of tourism clusters on Airbnb performance across individual counties (intraregional clusters) and neighboring counties (interregional clusters). The results indicate that overall tourism clusters, especially in the industries of accommodation and food services, lead to superior Airbnb performance, but the tourism clusters-Airbnb performance relationship varies across industry and region, confirming the existence of intraregional and interregional clusters. These findings can help Airbnb hosts and tourism policymakers in other regions implement localized tourism industry strategies for maximizing Airbnb performance.
Identification of tourist flows in Florida to support development of tourist travel module for FDOT Florida Transportation Model (Link)
Thomas Hill, Andrei Kirilenko, Jinwon Kim, David Ma, Eunjung Yang
Network approach to tourist segmentation via user generated content (Link)
Hernandez, J.M., Kirilenko, A., Stepchenkova, S. (2018). Annals of Travel Research, 73, 35-47.
Online reviews of destination attractions are considered as a proxy for visitation data reflective of tourists’ interests. The connectivity between attractions is represented with a network of links created by tourists visiting and reviewing multiple attractions. Attraction clusters are revealed by segmenting this network using network analysis tools.
Online public response to a service failure incident: Implications for crisis communications (Link) Su,L., Stepchenkova, S., Kirilenko, A. (2019). Tourism Management, 73, 1-12
The study examines the online public response to a high visibility incident of service failure from the crisis communication and image restoration perspectives. A specific incident in a hotel of a popular chain in Beijing, China, which was widely publicized on Chinese social network Weibo, is used as an example. Applying data mining and GIS to the collected data, the study analyzes temporal, spatial, topical, and gender dynamics of public discussions to investigate the usefulness of such data for hospitality providers in crisis. The study finds evidence that the effect of the incident on the general public is moderated by spatial and personal proximity of Weibo users. The discussion topics reflect the nature of the crisis and are affected by communications from the service provider and third parties. Implications for providers of tourism and hospitality services in a crisis of high visibility are discussed.
