Lingxue Zhan, Mingming Cheng, Jingjie Zhu, Xiaowei Wang
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Projected Destination Image of Beijing on Instagram: A Sequential Research Design
Using a sequential research design combining image analytics and Qualitative Comparative Analysis (QCA), this research examines Beijing’s projected destination image and its impacts on social media engagement on Instagram. Deep learning algorithms and convolutional neural networks were used to analyze the images. The image analytic findings show that Beijing’s projected destination image includes: (1) multiple urban and country landscapes; (2) a mixture of modernity and tradition; (3) a range of activities in a dynamic city and (4) cuisine—a variety of traditional Chinese food. QCA identified three paths that lead to high engagement, including “building” and “sky,” “building” and “event,” “sky,” and “event.” This research advances the destination image literature by empirically establishing the relationship between destination image labels and social media engagement. Further, it offers a new configurational perspective for constructing projected destination image by delineating how DMOs effectively increase social media engagement through image semantic content configurations.
期刊介绍:
The Journal of Travel Research (JTR) stands as the preeminent, peer-reviewed research journal dedicated to exploring the intricacies of the travel and tourism industry, encompassing development, management, marketing, economics, and behavior. Offering a wealth of up-to-date, meticulously curated research, JTR serves as an invaluable resource for researchers, educators, and industry professionals alike, shedding light on behavioral trends and management theories within one of the most influential and dynamic sectors. Established in 1961, JTR holds the distinction of being the longest-standing among the world’s top-ranked scholarly journals singularly focused on travel and tourism, underscoring the global significance of this multifaceted industry, both economically and socially.