Research
Key Research Areas
Tourist Flows and Location
Leveraging geospatial tool to predict tourist spatial behaviors and optimize destination management strategies.
AI and Big Data in Tourism
Leveraging machine learning and LLMs to predict tourist behaviors and optimize destination management strategies.
Sustainability and Resilience
Analyzing sustainability and risk on tourism and hospitality businesses and developing resilience models.
Digital Platforms and Pricing
Investigating the online behavior of tourists and organizations in digital platforms and pricing strategy.
Methodological Expertise
Big Data
Processing terabytes of unstructured data to reveal hidden patterns.
Econometrics
Advanced causal inference and time-series analysis for robust analysis.
Spatial Modeling
Geo-spatial tools to understand the spatial dimension and decision support.
Meta Analysis
Synthesizing results from different studies and understanding heterogeneity
Research Outputs
2026
Past stays and future standards: How customer-mediated knowledge transfer enhances service quality
Zhang, XW; Huang, XY; Yang, Y; Liu, W
TOURISM MANAGEMENT
- Conference Paper
- 2026
Measuring inter-regional tourism in the United States: Integrating passenger tracking and household survey data through a gravity lens.
Yang, Y. and Xiong, C.
International Association for Tourism Economics [IATE] 2026 Conference.
Palermo, Italy
- Conference Paper
- 2026
Event desert: Identifying determinants of cultural and social inequality in community leisure.
Hwang, G. and Yang, Y.
2026 TALS Research and Teaching Conference.
Philadelphia, Pennsylvania
- Conference Paper
- 2026
Feast or famine: A big and deep data approach to event impacts on restaurant revenue.
Hwang, G. and Yang, Y.
The 31st Annual Graduate Education & Graduate Student Research Conference in Hospitality & Tourism.
Auburn, Alabama
- Keynote
- 07/26/2025
Tourism Experience and Human Well-being
2025 International Conference on Consumption Studies (ICCS)
Changsha, China (Online)
- Keynote
- 06/06/2025
Geo-located big data and sport tourism
3rd High-Level Forum on Sports Tourism and Symposium on the Development of Sports Tourism Management
Huangshan, China
- Presentation
- 09/16/2025
Meta-Analysis in Tourism Economics
Tourism Economics in Focus, The IATE Research Seminar Series
Online
- Tool
Restaurant Week Impact Explorer
Tool link: https://uflyy.github.io/restaurant-week/
Academic Reference:
Yang, Y., Yin, Q., Hwang, G. K., Liang, S., & Yang, D. (forthcoming). Restaurant week paradox: Asymmetric effects of event-based marketing on online engagement. International Journal of Contemporary Hospitality Management.
1. Geographic Map View
- Year Filtering: Use the dropdown in the top right corner to filter participation status by a specific year.
- Frequency Mapping: If “All Years” is selected, the size (radius) of the cherry-red markers expands dynamically based on the total number of years a restaurant has participated.
- Interaction: Click on any marker to open an information popup with historical baseline data and predictive metrics.
2. Cohort Comparison
- Purpose: Compares the structural and baseline performance differences between restaurants that participated in RW versus those that did not.
- Visual Variables: Recharts-based grouped bar charts are used to visualize the mean values of quantitative variables (Rating, Review Volume, Local/First-time mix) and the proportional makeup of categorical variables (Price Level, Fine Dining status).
- Frequency Histogram: When filtering by “All Years”, a unique frequency distribution chart activates, revealing the historical retention and loyalty of participating restaurants.
3. Single Unit Simulation
- Purpose: A sandbox environment detached from the map data. It allows you to model hypothetical scenarios based on econometric estimation formulas.
- Controls: Adjust the baseline consumer mix (Local Reviewers and First-Time Reviewers) and toggle the Fine Dining status to see real-time marginal treatment effects.
- Tool
Bike sharing and tourism impact tool
Tool link: https://uflyy.github.io/bike-sharing/
The tool is an interactive, data-driven dashboard designed to visualize the synergistic relationship between urban micro-mobility and tourism. Grounded in empirical econometric research, the tool uses Chicago as a case study to demonstrate how bike-sharing systems impact the demand and visitor experience of nearby tourist attractions. It features interactive spatial mapping and a predictive policy simulator, bridging the gap between academic research and smart city tourism management.
- Tool
Rating Adjustment Tool
Tool link: https://uflyy.github.io/rating-adjustment/
The Rating Adjustment Tool is an advanced analytical web application designed to standardize hotel online reviews by correcting for “scaling heterogeneity”—the phenomenon where different types of reviewers interpret and use rating scales differently. Powered by a Hierarchical Ordered Probit (HOPIT) model grounded in peer-reviewed academic research , the tool mathematically controls for systematic response biases tied to traveler demographics (such as age and gender) and trip characteristics (such as travel type and reviewer expertise). By offering individual review adjustments, hotel-level aggregate score calculations, and batch CSV processing, the tool effectively translates subjective, raw user ratings into objective, comparable latent scores and standardized 1–5 metrics, ensuring fairer and more accurate hotel evaluations.
This rating adjustment tool is built on the theoretical framework and empirical results from the following paper:
Leung, X. Y., & Yang, Y. (2020). Are all five points equal? Scaling heterogeneity in hotel online ratings. International Journal of Hospitality Management, 88, 102539.
- Tool
Green Hospitality Dashboard
Tool Address: https://uflyy.github.io/green-hotels/
How to Use This Dashboard
- Global Map Aggregation: At the global or country level, cities are represented by aggregated markers scaled by hotel volume. Click a city marker to seamlessly zoom in and load individual hotel properties without clutter.
- Dynamic Popups: Popups intelligently pan into the view to ensure long lists of Green Features or Hotel Styles are never clipped by the screen edges.
- Deep-Dive Graphing: Navigate to the Graph View to instantly evaluate structural differences across subgroups using Native Boxplots (min, Q1, median, Q3, max), Standardized Radar Profiling, and 100% Stacked Bar Charts.
- Top 10 Feature Slicing: When grouping by specific environmental practices (Green Features), the system automatically filters for the top 10 most adopted measures to preserve visual clarity.
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