Title of Paper

The Ideal Mix: Tourist Preferences for Geographically Distributed Attractions in a City

Author Bios (50 Words)

Harmen Oppewal is a Professor of Marketing at Monash University. His research focuses on consumer behaviour in retail, tourism and transport contexts. He published among others in the Journal of Travel Research and Tourism Management, the Journal of Consumer Research, Journal of Marketing Research, Journal of Retailing and Transportation Research.

Ari Pramono is a Research Fellow in the Department of Marketing at Monash University. His research interests are in spatial analysis and choice modelling for marketing, retail, transportation and tourism applications. He obtained his PhD from Monash University.

Abstract (150 Words)

This paper explores how tourists value the presence of certain types of attractions in a city that they visit as part of a longer vacation trip. In developing new attractions tourism managers need to decide which additional attractions will add most value to the tourist offer in a city, which may already have multiple attractions such as museums, parks, shopping, amusement and historical sites. They also need to decide where to locate these attractions. Our study uses a simulated Google maps environment to present respondents with a hypothetical city offering a mix of geographically distributed attractions. Respondents indicate their preference for the city as a place to visit. Using latent class regression methods we identify for different motivational segments which attributes of attractions (e.g. location or co-location next to other attractions, star rating, recommended visit duration) contribute most to the overall appeal of the city as a destination.

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The Ideal Mix: Tourist Preferences for Geographically Distributed Attractions in a City

This paper explores how tourists value the presence of certain types of attractions in a city that they visit as part of a longer vacation trip. In developing new attractions tourism managers need to decide which additional attractions will add most value to the tourist offer in a city, which may already have multiple attractions such as museums, parks, shopping, amusement and historical sites. They also need to decide where to locate these attractions. Our study uses a simulated Google maps environment to present respondents with a hypothetical city offering a mix of geographically distributed attractions. Respondents indicate their preference for the city as a place to visit. Using latent class regression methods we identify for different motivational segments which attributes of attractions (e.g. location or co-location next to other attractions, star rating, recommended visit duration) contribute most to the overall appeal of the city as a destination.