Title of Paper

Does Travel Distance Matter in Measuring Theme Park Demand and Theme Park Attractiveness?

Author Bios (50 Words for each Author)

Yingsha Zhang, Ph.D. (yingsha.zhang@waikato.ac.nz), is a senior lecturer at the Waikato Management School, The University of Waikato. Yingsha’s research mainly focuses on destination marketing, resort management, and consumer behavior, with special emphasis on theme parks, tourism geography, and big data analysis.

Xiang (Robert) Li, Ph.D. (robertli@temple.edu), is a professor and Washburn Senior Research Fellow at the Department 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.

David Cárdenas, Ph.D. (dcardenas@hrsm.sc.edu), is an associate dean at the School of Hotel, Restaurant, and Tourism Management, University of South Carolina. David’s research mainly focuses on sustainable development, resident attitudes, and tourism education. He has extensive experience working on community-based tourism planning projects in the United States and Ecuador.

Yu Liu, Ph.D. (liuyu@urban.pku.edu.cn), is a professor of GIScience and deputy director of the Institute of Remote Sensing and Geographical Information Systems, Peking University, China. Yu's research mainly focuses on spatial data models and analysis methods for big geo-data and covers applications in human and social science using big data.

Abstract (150 Words)

This study used visitation and travel distance to measure a tourist source market’s theme park demand and a theme park’s attractiveness. A tourist source market’s theme park demand was measured using theme park tourist visitation out and travel distance out; a theme park’s attractiveness was measured using theme park tourist visitation in and travel distance in. Data were obtained from TripAdvisor. Reviewers of each U.S. theme park were a proxy of the park’s visitors. A reverse gravity model was applied to explain the relationship between a tourist source market’s theme park demand, a theme park’s attractiveness, visitor flow, and travel distance. Particle swarm optimization (PSO) was employed to solve the reverse gravity model and estimate each tourist source market’s demand for theme parks and each theme park’s attractiveness in the U.S. The predictive power of the proposed measurement was found to be much higher than the traditional visitation-based measurement.

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Does Travel Distance Matter in Measuring Theme Park Demand and Theme Park Attractiveness?

This study used visitation and travel distance to measure a tourist source market’s theme park demand and a theme park’s attractiveness. A tourist source market’s theme park demand was measured using theme park tourist visitation out and travel distance out; a theme park’s attractiveness was measured using theme park tourist visitation in and travel distance in. Data were obtained from TripAdvisor. Reviewers of each U.S. theme park were a proxy of the park’s visitors. A reverse gravity model was applied to explain the relationship between a tourist source market’s theme park demand, a theme park’s attractiveness, visitor flow, and travel distance. Particle swarm optimization (PSO) was employed to solve the reverse gravity model and estimate each tourist source market’s demand for theme parks and each theme park’s attractiveness in the U.S. The predictive power of the proposed measurement was found to be much higher than the traditional visitation-based measurement.