Abstract
Abstract. Identifying determinants of tourist destination choice is an important task in the study of nature-based tourism. Traditionally, the study of tourist behavior relies on survey data and travel logs, which are labor-intensive and time-consuming. Thanks to location-based social networks, more detailed data is available at a finer grained spatio-temporal scale. This allows for better insights into travel patterns and interactions between attractions, e.g., parks. Meanwhile, such data sources also bring along a novel social influence component that has not yet been widely studied in terms of travel decisions. For example, social influencers post about certain places, which tend to influence destination choices of tourists. Therefore, in this paper, we propose a socially aware Huff model to account for this social factor in the study of destination choice. Moreover, with fine-grained social media data, interactions between attractions (i.e., the neighboring effects) can be better quantified and thus integrated into models as another factor. In our experiment, we calibrate a model by using trip sequences extracted from geotagged Flickr photos within two national parks in the United States. Our results demonstrate that the socially aware Huff model better simulates tourist travel preferences. In addition, we explore the significance of each factor and summarize the spatial-temporal travel pattern for each attraction. The socially aware Huff model and the calibration method can be applied to other fields such as promotional marketing.
Highlights
Nature tourism, i.e., tourism that is based on the natural attractions of an area, has gone through rapid growth over the past two decades (Balmford et al, 2009), especially for national parks in the United States, according to visitation statistics by National Park Service.1 Identifying and evaluating relevant determinants of tourist flows is important
The emergence of locationbased social networks (LBSNs) and volunteered geographic information (VGI), such as Flickr, Instagram, Facebook etc., together with geotagging technology, provides more fine-grained spatial and temporal data, which equips us with a new lens to understand travel patterns as they relate to natural attractions
To explore how social factors and neighboring effects contribute to tourist destination choice in natural attractions, we propose a socially aware version of the well-known Huff model (Huff, 1964), which was originally used to calculate the probability of a customer shopping at each retail store
Summary
I.e., tourism that is based on the natural attractions of an area, has gone through rapid growth over the past two decades (Balmford et al, 2009), especially for national parks in the United States, according to visitation statistics by National Park Service. Identifying and evaluating relevant determinants of tourist flows is important. Geotagged photos posted by social media influencers (SMIs) can rapidly attract new visitors (Glover, 2009). These influencers are usually users with a large number of followers and have established credibility in certain fields that can shape attitudes of tourists and influencing their travel preferences (Freberg et al, 2011; Li, 2016). We argue that social factors brought by increasingly used social media need to be taken into account as a new norm to complement traditional destination choice models To justify such an argument, we explore this social effect in nature-based tourism destination choices, because tourists tend to share geotagged photos on social media platforms along their trips (Tasse et al, 2017)
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