Date of Award
Open Access Dissertation
School of Hotel, Restaurant and Tourism Management
College of Hospitality, Retail, and Sport Management
Xiang (Robert) Li
This dissertation examines the relationship among theme park demand, theme park attractiveness, and visitors’ theme park choices. It consists of three studies reported through five chapters. Key data were obtained from the website of an online travel agency, TripAdvisor, using web scraping techniques. The first study developed a new approach to measure tourist source markets’ theme park demand and theme park attractiveness by solving a reverse gravity model via particle swarm optimization. Findings from this study should help theme parks to evaluate more accurately the demand market and their own competitiveness compared to other theme parks. The second study explored the spatial interaction between theme park demand and theme park attractiveness across different areas and within local areas, respectively. The tourism energy model was applied to explain spatial interaction. The size and spatial distribution patterns of theme park demand and theme park attractiveness were also explored. Findings of the second study provide significant resources to aid in tourism planning, tourism marketing, and infrastructure development. In the third study, three models were developed based on random utility theory. Agent-based modeling was used to test these models. The random percentage model was found to have the highest predictive power and was used to forecast attendance and visitor flows at new theme parks, which were assigned different attractiveness values and to different locations. A series of simulation experiments were conducted through agent-based modeling to examine the impacts of location and theme park attractiveness on new and existing theme park attendance and visitor flows. Findings from this study shed light on decisions related to theme park marketing and investment.
Zhang, Y.(2017). Theme Park Demand, Theme Park Attractiveness, and Visitors’ Theme Park Choices. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/4344
Available for download on Monday, December 18, 2023