Author Bios (50 Words for each Author)

Ms. Yuan Wang is a PhD student at School of Tourism and Hospitality Management and Fox School of Business, Temple University. Yuan is interested in topics related to destination image and tourist attitude.

Dr. Xiang (Robert) Li is a professor and Washburn Senior Research Fellow at the School of Tourism and Hospitality Management, Temple University. Robert's research mainly focuses on destination marketing and tourist behavior, with special emphasis on international destination branding, customer loyalty, and tourism in Asia.

Dr. Kun Lai is an associate professor at the School of Tourism Management, Sun Yat-sen University. His research focuses on tourism geographies, event management/tourism, tourism psychology and tourism philosophy.

Abstract (150 Words)

The core-periphery structure (C/PS) of destination image, recently proposed as an alternative image model, has scarcely been tested. Presumably, the lack of appropriate techniques plays an important role in the limited theory testing efforts. Besides, it remains unclear how people retrieve destination image from memory, when we place destination image into a C-P model. Understanding the structure of destination image could reveal insights into image retrieval. This research aims to introduce social network analysis as a new approach to test the C/PS of destination image, and identify the retrieving paths of destination image adopted by potential tourists. Image descriptions about the Shanghai Disney Resort (SHDR) were collected from 1,000 respondents, and the data was analyzed via social network techniques. Results show that the image of SHDR has both single and multiple C/PSs, and image retrieval of respondents either follows a core-to-periphery path or fluctuates between two neighboring levels of core/periphery.

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The Core-Periphery Structure of Destination Image: An Exploration via Social Network Analysis

The core-periphery structure (C/PS) of destination image, recently proposed as an alternative image model, has scarcely been tested. Presumably, the lack of appropriate techniques plays an important role in the limited theory testing efforts. Besides, it remains unclear how people retrieve destination image from memory, when we place destination image into a C-P model. Understanding the structure of destination image could reveal insights into image retrieval. This research aims to introduce social network analysis as a new approach to test the C/PS of destination image, and identify the retrieving paths of destination image adopted by potential tourists. Image descriptions about the Shanghai Disney Resort (SHDR) were collected from 1,000 respondents, and the data was analyzed via social network techniques. Results show that the image of SHDR has both single and multiple C/PSs, and image retrieval of respondents either follows a core-to-periphery path or fluctuates between two neighboring levels of core/periphery.