A framework for quantitative analysis and differentiated marketing of tourism destination image based on visual content of photos. (December 2022)

• Record Type: Journal Article
• Authors: Xiao, Xin; Fang, Chaoyang; Lin, Hui; Chen, Jingfu
• Is Part Of: Tourism management. Volume 93(2022)
• Publication Date: 2022-12
• Subjects: Tourism destination image -- Tourist-generated content -- Visual analysis -- Destination marketing -- Deep learning; TGC tourist-generated content -- OGC occupationally-generated content -- DMO destination marketing organization -- CNN convolutional neural network -- NIMA neural image assessment -- AHP analytical hierarchy process -- CRITIC criteria importance through intercriteria correlation -- TF-IDF term frequency-inverse document frequency -- DIM destination identification model; Tourism -- Periodicals; 338.4791
• DOI: 10.1016/j.tourman.2022.104585
• Languages: English
• ISSNs: 0261-5177

Abstract

Abstract: Photos shared by tourists are being generated at an unprecedented speed, creating new opportunities to study tourism destination images. Nevertheless, little research has focused on the tourist's perception of images from multiple perspectives and how to construct differentiated marketing strategies that link tourists with destinations. With the support of deep-learning technology, we propose herein a quantitative analysis strategy and differentiated marketing framework driven by photo big data which contains images of tourism destinations. We draw on photos of tourism destinations shared by tourists and analyse three aspects of perceived images – composition scene, visual aesthetic quality and visual uniqueness. We further develop a set of objective image-projection schemes that integrate multiple indicators of tourism destinations to improve destination marketing. Finally, we focus on an empirical case study of Wuyuan, China. The results of the study have methodological, theoretical and practical implications for the tourism industry.