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Destination image: a consumer-based, big data-enabled approach

Destination image: a consumer-based, big data-enabled approach

Zhong, Lina, Liu, Jiating, Morrison, Alastair ORCID: 0000-0002-0754-1083, Dong, Yingchao, Zhu, Mangyao and Li, Lei (2023) Destination image: a consumer-based, big data-enabled approach. Tourism Review. ISSN 1660-5373 (doi:

38401_MORRISON_Destination_image_A_consumer_based_big_data_enabled_approach.pdf - Accepted Version
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Purpose - This research aimed to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places.
Design/methodology/approach - Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then employed to group destinations by attributes, and ANOVA provided comparisons.
Findings - Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic, and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages, and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers.
Originality/value - This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.
Practical implications - Destinations should determine into which category they belong and then appeal to the real needs of tourists. DMOs should provide the essential attributes and pay attention to creating a unique social life atmosphere.

Item Type: Article
Additional Information: Publisher: Emerald Publishing Limited Copyright © 2022, Emerald Publishing Limited
Uncontrolled Keywords: COVID-19; postpandemic; prepandemic; peer-to-peer accommodations; perceptions, Experiences, Big data analysis
Subjects: B Philosophy. Psychology. Religion > BF Psychology
G Geography. Anthropology. Recreation > GV Recreation Leisure
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
Faculty / School / Research Centre / Research Group: Faculty of Business
Faculty of Business > Department of Marketing, Events & Tourism
Faculty of Business > Tourism Research Centre
Last Modified: 03 Apr 2023 10:27

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