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
- Dataset
Government Attention to Tourism Data
This web-based application is designed to provide researchers and policymakers with an interactive platform for querying and analyzing data regarding Government Attention to Tourism (GAT) in China.
Based on the research findings of Yang, Yang, Huang & Yan (2026), this tool visually demonstrates the spatiotemporal relationships and statistical correlations between the GAT index and various tourism economic indicators. This tool supports a bilingual interface in both Chinese and English. You can switch between the two language modes in real-time by clicking the language toggle button (中文/EN) located in the top-right corner of the page.
Key Features:
Multidimensional Data Coverage: Includes GAT indices and 11 key tourism economic indicators at both the provincial and prefectural (city) levels.
Interactive Visualization: Provides trend analysis, spatial distribution maps, and statistical correlation scatter plots.
Data Resources: Supports viewing summaries of raw data and downloading data.
本网页应用旨在为研究人员和政策制定者提供关于中国政府旅游关注度 (Government Attention to Tourism, GAT) 的交互式数据查询与分析平台。
该工具基于 Yang, Yang, Huang & Yan (2026) 的研究成果，通过可视化手段展示了 GAT 指数与各类旅游经济指标之间的时空关系和统计关联。本工具支持中英文双语界面。点击页面右上角的语言切换按钮（中文/EN），即可在两种语言模式间实时切换。
核心功能：
- 多维数据覆盖： 包含省级和地级层面的 GAT 指数及 11 项关键旅游经济指标。
- 交互式可视化： 提供趋势分析、空间分布地图和统计关联散点图。
- 数据资源： 支持查看原始数据摘要及下载。
- Tool
Extended MASEM Analysis from SO, YANG AND LI (2025)
This web-based application is designed to conduct Meta-Analytic Structural Equation Modeling (MASEM). It is built upon the foundational research of So, Yang, and Li (2025), which synthesized 15 years of research to determine the interrelationships between Customer Loyalty, Customer Satisfaction, Perceived Value, and Perceived Service Quality.
Key Features:
- Replication: Users can replicate the findings of So, Yang, and Li (2025) using pre-loaded, industry-specific data.
- Extension: The tool allows researchers to expand the original model by adding new variables (e.g., Place Attachment, Trust, Brand Image) to test novel theoretical frameworks.
- Methodology: It utilizes the Harmonic Mean or Minimal N methods to aggregate sample sizes.
- Dataset
Pulse of American Domestic Tourism
“The ‘Pulse of American Domestic Tourism’ project serves as a digital monitor for the nation’s internal mobility. By mining transportation-derived mobility data, we develop a comprehensive matrix of tourism flows connecting American MSAs. This data-driven approach unveils the rhythmic shifts in visitor demand and regional connectivity. Crucially, we ground these digital insights through extensive cross-validation with household survey data, creating a verified, high-resolution framework for understanding the evolving landscape of domestic travel.”
Key Vocabulary Used (Why it works):
- Inter-MSA travel flows: Specific and accurate to your methodology.
- Arterial circulation / Rhythmic shifts: Reinforces the “Pulse” metaphor without being cheesy.
- High-granularity / Spatiotemporal precision: Highlights the “Big Data” advantage.
- Rigorously cross-validated: Emphasizes the reliability of your model (crucial for academic trust).
- Ground-truth metrics: A professional way to refer to the survey data as the standard of truth.
- Dataset
COVID19tourism Index
The COVID19tourism index was developed to monitor the pandemic’s multifaceted impact on the global tourism industry. This index comprises five distinct sub-indices designed to track the specific effects of COVID-19 across various aspects of tourism activities. By utilizing this tool, destinations are enabled to assess their recovery status, generate rigorous forecasts, and benchmark their performance against potential competitors. Sub-indices The COVID19tourism index is comprised of five distinct sub-indices. These sub-indices were designed to track the specific effects of the pandemic across different aspects of tourism activities.
Dashboard Utility The index functions as a tool that enables destinations to perform three primary functions:
• Evaluate Recovery: Destinations can use the tool to assess their current recovery status.
• Forecast: The tool allows users to produce rigorous forecasts regarding tourism trends.
• Benchmark: Destinations can use the index to benchmark their performance against potential competitors
Link to the COVID19tourism Index Dashboard
Link to download the data
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