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

PurposeThis study aims 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/approachContent and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons. FindingsTwenty-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. Practical implicationsDestinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations' social life atmosphere. Originality/valueThis 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.

Automated summary

This study aims to derive destination image attributes and establish a classification system for destinations based on online consumer narratives. Content and social network analyses were used to explore the consumer image structure, and cluster analysis and ANOVA were used to group destinations and compare them. The study identified 22 attributes and grouped destinations into three categories based on their network centralities. Landscape, traffic, food and beverages, and resource-based attractions were the most mentioned attributes. Social life was found to be meaningful in consumer narratives but often overlooked by researchers.