北京
社会化媒体
目的地图像
事件(粒子物理)
图像(数学)
透视图(图形)
多样性(控制论)
韩流
计算机科学
卷积神经网络
数据科学
广告
旅游
社会学
人工智能
地理
万维网
中国
业务
目的地
考古
物理
量子力学
作者
Lingxue Zhan,Mingming Cheng,Jingjie Zhu,Xiaowei Wang
标识
DOI:10.1177/00472875231210817
摘要
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.
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