A comparative study of user- and organization-generated hotel imagery across diverse geographical environments
Visual imagery greatly affects impressions of hotels and their geographical context (i.e. the properties themselves and their surroundings). This study compares how hotel zones are depicted in different destinations. It specifically considers cognitive, affective, and overall aspects along with seve...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-05-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2510604 |
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| _version_ | 1849689862321471488 |
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| author | Zizhen Zheng Tianxiang Zheng Shiqi Liu Yilin Sun Shiyu Lu Yuanyuan Zhu Suqin Ge Yinfei Xiao Yanbing Zhou Qihang Qiu |
| author_facet | Zizhen Zheng Tianxiang Zheng Shiqi Liu Yilin Sun Shiyu Lu Yuanyuan Zhu Suqin Ge Yinfei Xiao Yanbing Zhou Qihang Qiu |
| author_sort | Zizhen Zheng |
| collection | DOAJ |
| description | Visual imagery greatly affects impressions of hotels and their geographical context (i.e. the properties themselves and their surroundings). This study compares how hotel zones are depicted in different destinations. It specifically considers cognitive, affective, and overall aspects along with several stakeholder groups, namely tourists, organizations, and professionals. Deep learning techniques were employed to analyze user-generated photos (UGPs), organization-generated photos (OGPs), and professional-generated photos (PGPs) in three popular American cities. Google Vision API, DeepSentiBank with VaderSentiment, k-means clustering, and aesthetics automation with analysis of variance were, respectively, used to detect the objects, sentiments, colors, and aesthetics contained in photos. A dataset was assembled, comprising 7,953 images from TripAdvisor, to examine the interplay between content creators’ visual representations of hotels across geographical settings. The results revealed substantial perception gaps between customers and sellers. Differences also emerged in how UGPs and OGPs/PGPs showcase geographical features. These findings add nuance to the academic discussion on brand image by exploring various content creators and hotel zones. Strategic recommendations are provided on using hotel images to boost destination appeal, guide hotel marketing campaigns, and optimize platform design. |
| format | Article |
| id | doaj-art-0c50e8e1ce11423baeae81f6ceb76e1d |
| institution | DOAJ |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-0c50e8e1ce11423baeae81f6ceb76e1d2025-08-20T03:21:28ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-05-010011810.1080/13467581.2025.25106042510604A comparative study of user- and organization-generated hotel imagery across diverse geographical environmentsZizhen Zheng0Tianxiang Zheng1Shiqi Liu2Yilin Sun3Shiyu Lu4Yuanyuan Zhu5Suqin Ge6Yinfei Xiao7Yanbing Zhou8Qihang Qiu9Jinan UniversityJinan University (Shenzhen Campus)Sun Yat-sen UniversityJinan University (Shenzhen Campus)Jinan University (Shenzhen Campus)Jinan UniversityJinan University (Shenzhen Campus)Jinan University (Shenzhen Campus)Jinan University (Shenzhen Campus)Kennesaw State UniversityVisual imagery greatly affects impressions of hotels and their geographical context (i.e. the properties themselves and their surroundings). This study compares how hotel zones are depicted in different destinations. It specifically considers cognitive, affective, and overall aspects along with several stakeholder groups, namely tourists, organizations, and professionals. Deep learning techniques were employed to analyze user-generated photos (UGPs), organization-generated photos (OGPs), and professional-generated photos (PGPs) in three popular American cities. Google Vision API, DeepSentiBank with VaderSentiment, k-means clustering, and aesthetics automation with analysis of variance were, respectively, used to detect the objects, sentiments, colors, and aesthetics contained in photos. A dataset was assembled, comprising 7,953 images from TripAdvisor, to examine the interplay between content creators’ visual representations of hotels across geographical settings. The results revealed substantial perception gaps between customers and sellers. Differences also emerged in how UGPs and OGPs/PGPs showcase geographical features. These findings add nuance to the academic discussion on brand image by exploring various content creators and hotel zones. Strategic recommendations are provided on using hotel images to boost destination appeal, guide hotel marketing campaigns, and optimize platform design.http://dx.doi.org/10.1080/13467581.2025.2510604image analysisdeep learninggeographical environmenthotel imagerybrand image |
| spellingShingle | Zizhen Zheng Tianxiang Zheng Shiqi Liu Yilin Sun Shiyu Lu Yuanyuan Zhu Suqin Ge Yinfei Xiao Yanbing Zhou Qihang Qiu A comparative study of user- and organization-generated hotel imagery across diverse geographical environments Journal of Asian Architecture and Building Engineering image analysis deep learning geographical environment hotel imagery brand image |
| title | A comparative study of user- and organization-generated hotel imagery across diverse geographical environments |
| title_full | A comparative study of user- and organization-generated hotel imagery across diverse geographical environments |
| title_fullStr | A comparative study of user- and organization-generated hotel imagery across diverse geographical environments |
| title_full_unstemmed | A comparative study of user- and organization-generated hotel imagery across diverse geographical environments |
| title_short | A comparative study of user- and organization-generated hotel imagery across diverse geographical environments |
| title_sort | comparative study of user and organization generated hotel imagery across diverse geographical environments |
| topic | image analysis deep learning geographical environment hotel imagery brand image |
| url | http://dx.doi.org/10.1080/13467581.2025.2510604 |
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