{"title":"User-generated photos in hotel demand forecasting","authors":"Jian Xu , Wei Zhang , Hengyun Li , Xiang (Kevin) Zheng , Jing Zhang","doi":"10.1016/j.annals.2024.103820","DOIUrl":null,"url":null,"abstract":"<div><p>User-generated content has become an invaluable resource for researchers in hospitality and tourism, especially regarding sales and demand forecasting. Some scholars have analyzed textual data and sentiment information; however, few studies have addressed roles of user-generated photos in hotel demand prediction. This study fills this void by examining the effectiveness of various photo features (i.e., topics and sentiments) for hotel demand forecasting. Results demonstrate the superiority of photo topic features over sentiment features in enhancing demand prediction. Forecasting accuracy is further improved after integrating a combination of photo topic and sentiment features. Moreover, user-generated photos elevate the accuracy of daily demand forecasting for different hotels. This study contributes to the literature on hotel demand forecasting using Internet multimodal data.</p></div>","PeriodicalId":48452,"journal":{"name":"Annals of Tourism Research","volume":null,"pages":null},"PeriodicalIF":10.4000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Tourism Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160738324000975","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
引用次数: 0
Abstract
User-generated content has become an invaluable resource for researchers in hospitality and tourism, especially regarding sales and demand forecasting. Some scholars have analyzed textual data and sentiment information; however, few studies have addressed roles of user-generated photos in hotel demand prediction. This study fills this void by examining the effectiveness of various photo features (i.e., topics and sentiments) for hotel demand forecasting. Results demonstrate the superiority of photo topic features over sentiment features in enhancing demand prediction. Forecasting accuracy is further improved after integrating a combination of photo topic and sentiment features. Moreover, user-generated photos elevate the accuracy of daily demand forecasting for different hotels. This study contributes to the literature on hotel demand forecasting using Internet multimodal data.
期刊介绍：
The Annals of Tourism Research is a scholarly journal that focuses on academic perspectives related to tourism. The journal defines tourism as a global economic activity that involves travel behavior, management and marketing activities of service industries catering to consumer demand, the effects of tourism on communities, and policy and governance at local, national, and international levels. While the journal aims to strike a balance between theory and application, its primary focus is on developing theoretical constructs that bridge the gap between business and the social and behavioral sciences. The disciplinary areas covered in the journal include, but are not limited to, service industries management, marketing science, consumer marketing, decision-making and behavior, business ethics, economics and forecasting, environment, geography and development, education and knowledge development, political science and administration, consumer-focused psychology, and anthropology and sociology.
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