Foundations for a Network Model of Destination Value Creation

Abstract

Previous research has demonstrated that a network model of destination value creation (i.e. the Destination Value System model) based on the flows of travelers within a destination can be used to estimate and predict individual attractions’ marginal contributions to total visitor expenditures. While development to date of the Destination Value System (DVS) has focused on the value created from dyadic relationships within the destination network, previous research supports the proposition that system-level network structures significantly influence the total value created within a destination. This study, therefore, builds upon previous DVS research in order to determine the relationships between system-level network structures and total value creation within a destination. To answer this question econometric analysis of panel data covering 43 Florida destinations over the period from 2007 to 2015 was conducted. The panel data was created utilizing volunteered geographic information (VGI) obtained from 4.6 million photographs shared on Flickr. Results of econometric analysis indicate that both seasonal effects and DVS network structures have statistically significant relationships with total tourism-related sales within a destination. Specifically, network density, network out-degree centralization, and network global clustering coefficient are found to have negative and statistically significant effects on destination value creation, while network in-degree centralization, network betweenness centralization, and network subcommunity count are found to have positive and statistically significant effects. Quarterly seasonality is also found to have dynamic and statistically significant effects on total tourism-related sales within a destination. Based on the network structures of destinations and total tourism related sales within destinations, this study also uses k-means cluster analysis to classify tourism destinations into a taxonomy of six different system types (Exploration, Involvement, Development I, Development II, Consolidation, and Stars). This taxonomy of DVS types is found to correspond to Butler’s (1980) conceptualization of the destination life cycle, and additional data visualization and exploration based on the DVS taxonomy finds distinct characteristics in destination structure, dynamics, evolution, and performance that may be useful for benchmarking. Additionally, this study assesses the quality of VGI data for tourism related research by comparing DVS network structures based on Flickr data and visitor intercept survey data. Support for the use of VGI data is found, provided that thousands of observations are available for analysis. When fewer observations are available, aggregation techniques are recommended in order to improve the quality of overall destination network system quantification. This research makes important contributions to both the academic literature and the practical management of destinations by demonstrating that DVS network structures significantly influence the economic value created within the destination, and thus suggests that a strategic network management approach is needed for the governance of competitive destinations. As a result, this study provides a strong foundation for the DVS model and future research in the areas of destination resiliency, “smarter” destination management, and tourism experience design.