Tourism and Hospitality Forecasting With Big Data
A Systematic Review of the Literature
Bibliographic Data
| ID | 15031980 |
|---|---|
| Authors | Doris Chenguang Wu (0000-0002-0291-5971, Sun Yat-sen University), Shiteng Zhong (0000-0002-9098-0350, Sun Yat-sen University), Ji Wu (0000-0002-3417-635X, Sun Yat-sen University), Haiyan Song (0000-0001-6158-1222, Shenzhen Research Institute, School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong SAR) |
| Year | 2025 |
| Volume | 49 |
| Issue | 3 |
| Pages | 615-634 |
| Publication date | 2025-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Hospitality & Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1096-3480 • E-ISSN: 1557-7554 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/10963480231223151 |
| Language | EN |
| Citations received | 18 |
| References cited | 145 |
Empirical research has shown that incorporating big data into tourism and hospitality forecasting significantly improves prediction accuracy. This study presents a comprehensive review of big data forecasting in the tourism and hospitality industry, critically evaluating existing research and identifying five key research questions and trends that require further attention. These include the lack of theoretical foundation, the rise of high-frequency forecasting research, less attention to unstructured data, the necessity of dynamic data analysis in forecasting, and the construction of a tourism and hospitality demand information system based on cloud computing. Importantly, this study constructs a theoretical framework by combining relevant theories from psychology, communication, information processing, and other fields. Five types of big data used for tourism and hospitality forecasting are identified: web-based volume data, social media statistics, textual data, photo data, and video data. Additionally, more recent tactics such as mixed data sampling and machine learning methods are discussed
Interval forecast combination in tourism demand
Man vs machine
Forecasting daily visits in Shanghai with Model combination and Telco big data
Come and say G’day
A novel two-stage combination model for tourism demand forecasting
Navigating Data Limitations
Hospitality and Tourism Demand
Agricultural heritage as a cultural moderator of sustainable food tourism behavior
Special issue
Hybrid Deep-learning model for resilient tourism enterprises
Optimizing hotel demand forecasting through ensemble models
Tourism destination mobile applications
Post-pandemic tourism forecasting with ensemble RNN
Forecasting China's outbound travel recovery post-Covid-19
Forecasting tourism recovery with multifactor insights – A case of post-pandemic Chinese outbound tourism
Tourism demand forecasting using social media data
Tourism Demand Interval Forecasting With an Intelligence Optimization-Based Integration Method
Digital Economy and Regional Tourism Development
New developments in tourism and hotel demand modeling and forecasting
Dynamic topic models
Estimating the Helpfulness and Economic Impact of Product Reviews
The tourism forecasting competition
Emotional Contagion
Predicting the Present with Google Trends
Signaling Theory
Organizational Information Requirements, Media Richness and Structural Design
Long Short-Term Memory
Experimental evidence of massive-scale emotional contagion through social networks
A Novel Approach for Spatially Controllable High-Frequency Forecasts of Park Visitation Integrating Attention-Based Deep Learning Methods and Location-Based Services
Monitoring and forecasting Covid-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries
Identifying the role of media discourse in tourism demand forecasting
Forecasting tourism demand with KPCA-based web search indexes
A novel Bemd-based method for forecasting tourist volume with search engine data
Tourism demand forecasting
Scenario Forecasting for Global Tourism
Let Photos Speak
A tale of two databases
Funding information in Web of Science
Tourism Demand Modelling and Forecasting
Social media engagement
Social Learning Theory of Aggression
Big data in tourism research
Research on user-generated photos in tourism and hospitality
A review of research on tourism demand forecasting
A deep learning approach for daily tourist flow forecasting with consumer search data
A novel hybrid model for tourist volume forecasting incorporating search engine data
Tourism demand forecasting from the perspective of mobility
The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers
Tourism demand modelling and forecasting—A review of recent research
Tourism demand forecasting using tourist-generated online review data
Mining meaning from online ratings and reviews
Wisdom of crowds
Forecasting Chinese tourist volume with search engine data
The Baidu Index
Forecasting tourist arrivals with machine learning and internet search index
A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data
Forecasting international tourist flows to Macau
Photographs in tourism research
Support vector regression with genetic algorithms in forecasting tourism demand
Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach
Forecasting tourism demand with composite search index
Exploring spatio-temporal changes of city inbound tourism flow
Forecasting Chinese cruise tourism demand with big data
Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index
A practitioners guide to time-series methods for tourism demand forecasting — a case study of Durban, South Africa
Tourism forecasting competition in the time of Covid-19
Visitor arrivals forecasts amid Covid-19
Forecasting tourist arrivals using denoising and potential factors
Forecasting city arrivals with Google Analytics
The combination of interval forecasts in tourism
An Integrative Model of Tourists’ Information Search Behavior
Forecasting tourism demand with multisource big data
Visitor arrivals forecasts amid Covid-19
Daily tourism volume forecasting for tourist attractions
Tourism demand forecasting with online news data mining
Group pooling for deep tourism demand forecasting
| Unique citing works | 18 |
|---|---|
| Citations per year | 9 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 17 |