Jingyi Duan , Xuefeng Liang , Jiangqun Liao , Ryoichi Nakashima , Hongyi Shi , Chenhao Hu , Takatsune Kumada , Kaiping Peng , Song Tong
{"title":"“大图景”预测目的地吸引力:物理广度和背景广度的作用","authors":"Jingyi Duan , Xuefeng Liang , Jiangqun Liao , Ryoichi Nakashima , Hongyi Shi , Chenhao Hu , Takatsune Kumada , Kaiping Peng , Song Tong","doi":"10.1016/j.tourman.2024.105114","DOIUrl":null,"url":null,"abstract":"<div><div>In global tourism, renowned attractions with diverse visual styles consistently yield positive experiences. This study introduced the ‘big picture’ metaphor as a universal visual code underlying their appeal. Drawing on the broaden-and-build theory, we proposed a two-dimensional visual breadth (2DVB) model, identifying physical breadth (expansiveness of visual fields) and contextual breadth (variety of visual contexts) as key predictors of destination ratings. A deep neural network with advanced feature recognition was developed to operationalize this model. Analyzing 588,821 photos from 120 global destinations, our analyses showed that both the physical and contextual visual breadth positively predicted destination ratings, validating the model. This approach surpassed traditional content-based methods, offering a new framework for cross-scene analysis in tourism management, guiding strategic planning and promotion.</div></div>","PeriodicalId":48469,"journal":{"name":"Tourism Management","volume":"108 ","pages":"Article 105114"},"PeriodicalIF":10.9000,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"“Big picture” predicts destination attractiveness: The role of physical breadth and contextual breadth\",\"authors\":\"Jingyi Duan , Xuefeng Liang , Jiangqun Liao , Ryoichi Nakashima , Hongyi Shi , Chenhao Hu , Takatsune Kumada , Kaiping Peng , Song Tong\",\"doi\":\"10.1016/j.tourman.2024.105114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In global tourism, renowned attractions with diverse visual styles consistently yield positive experiences. This study introduced the ‘big picture’ metaphor as a universal visual code underlying their appeal. Drawing on the broaden-and-build theory, we proposed a two-dimensional visual breadth (2DVB) model, identifying physical breadth (expansiveness of visual fields) and contextual breadth (variety of visual contexts) as key predictors of destination ratings. A deep neural network with advanced feature recognition was developed to operationalize this model. Analyzing 588,821 photos from 120 global destinations, our analyses showed that both the physical and contextual visual breadth positively predicted destination ratings, validating the model. This approach surpassed traditional content-based methods, offering a new framework for cross-scene analysis in tourism management, guiding strategic planning and promotion.</div></div>\",\"PeriodicalId\":48469,\"journal\":{\"name\":\"Tourism Management\",\"volume\":\"108 \",\"pages\":\"Article 105114\"},\"PeriodicalIF\":10.9000,\"publicationDate\":\"2024-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261517724002334\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261517724002334","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
“Big picture” predicts destination attractiveness: The role of physical breadth and contextual breadth
In global tourism, renowned attractions with diverse visual styles consistently yield positive experiences. This study introduced the ‘big picture’ metaphor as a universal visual code underlying their appeal. Drawing on the broaden-and-build theory, we proposed a two-dimensional visual breadth (2DVB) model, identifying physical breadth (expansiveness of visual fields) and contextual breadth (variety of visual contexts) as key predictors of destination ratings. A deep neural network with advanced feature recognition was developed to operationalize this model. Analyzing 588,821 photos from 120 global destinations, our analyses showed that both the physical and contextual visual breadth positively predicted destination ratings, validating the model. This approach surpassed traditional content-based methods, offering a new framework for cross-scene analysis in tourism management, guiding strategic planning and promotion.
期刊介绍:
Tourism Management, the preeminent scholarly journal, concentrates on the comprehensive management aspects, encompassing planning and policy, within the realm of travel and tourism. Adopting an interdisciplinary perspective, the journal delves into international, national, and regional tourism, addressing various management challenges. Its content mirrors this integrative approach, featuring primary research articles, progress in tourism research, case studies, research notes, discussions on current issues, and book reviews. Emphasizing scholarly rigor, all published papers are expected to contribute to theoretical and/or methodological advancements while offering specific insights relevant to tourism management and policy.