Author Bios (50 Words for each Author)

Ning Deng, PhD, is an associate professor and the vice dean of the school of Tourism Sciences at Beijing International Studies University. His research mainly focuses on destination digital marketing and tourism big data, with emphases on UGC mining and particularly the visual content analysis.

Chun Liu, PhD, is an associate professor of Tourism Sciences at Beijing International Studies University. Her main research areas include technology adoption, customer experience, consumer behavior, social media and cultural industries.

Siqi Guo, Master, is a student at the School of Tourism Sciences, Beijing International Studies University. Her research interests include destination marketing and destination image.

Langlang Qv, Master, is a research assistant at the School of Tourism Sciences, Beijing International Studies University. His research interests include tourism destination marketing and big data.

Abstract (150 Words)

Abstract

In the formation of tourism destination image (TDI), understanding and being able to measure, analyze, compare, and contrast the images projected by user-generated content(UGC) and destination marketing organizations(DMOs) is crucial in tourism management and destination marketing. This study aims to propose a specific way to measure and compare UGC and DMO projected images within 249 short videos. Using machine learning algorithms, four indicators (the number of short videos, the number of likes, comments and shares) were identified to measure the influences of DMO and UGC short videos, and extracted 7 dimensions representing the destination image extracted through video content analysis, namely, nature environment, infrastructure, culture and art, people, food and beverage, specific activities, and transportation. The data analysis further revealed statistical differences in several dimensions of these images at different destination’s life cycle. Theoretical and practical implications were also discussed.

Keywords

tourism destination image, destination’s life cycle, short video, video captaining,brand hijack

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Who Dominate TDI? A Big Data Evidence from DMO and UGC Short Videos

Abstract

In the formation of tourism destination image (TDI), understanding and being able to measure, analyze, compare, and contrast the images projected by user-generated content(UGC) and destination marketing organizations(DMOs) is crucial in tourism management and destination marketing. This study aims to propose a specific way to measure and compare UGC and DMO projected images within 249 short videos. Using machine learning algorithms, four indicators (the number of short videos, the number of likes, comments and shares) were identified to measure the influences of DMO and UGC short videos, and extracted 7 dimensions representing the destination image extracted through video content analysis, namely, nature environment, infrastructure, culture and art, people, food and beverage, specific activities, and transportation. The data analysis further revealed statistical differences in several dimensions of these images at different destination’s life cycle. Theoretical and practical implications were also discussed.

Keywords

tourism destination image, destination’s life cycle, short video, video captaining,brand hijack