Visitors’ strategic anticipation of crowding in scarce recreational resources
Introduction
Many recreational resources face limitations in allowing access to all visitors at the most preferred time slots (Francis, 1975, Joardar, 1989). For example, natural and cultural resources such as national parks, historical sites, major tourism attractions, and leisure shopping and dining facilities, typically are constrained in terms of how many individuals can visit at the most popular access times. In particular, strong increases in demand for scarce recreational resources may have unpleasant consequences for visitors (Jacobi and Manning, 1999), such as increased waiting times (Dawes and Rowley, 1996, Grewal et al., 2003), amplified stress (Loo, 1974), and less enjoyable experiences (Stokols, 1976, Hui and Bateson, 1991). In this article we refer to this phenomenon as crowding.
Most research on how to manage crowding in scarce recreational resources to date has investigated the effectiveness of different ways by which recreational resource planners can avoid crowding by redistributing visitors to obtain a more effective use of recreational resources, e.g., by using different booking and flexible pricing strategies such as yield management (Desiraju and Shugan, 1999) and dynamic pricing (Kannan and Kopalle, 2001) or providing visitors with more detailed visit information (MacLennan, 2000).
Other research has addressed individuals’ responses to crowding (Manning and Valliere, 2001). Coping with crowding in recreational resources may lead to both long- and short-term spatial and temporal behavioral adjustments of visitors (Kuentzel and Heberlein, 1992, Talen and Shah, 2007). The findings provided by Hall and Shelby (2000) show that temporal displacement (i.e., changes in visit timing) was the most common strategy of visitors to cope with crowding. Recent research has further shown that individuals’ choices of a specific response to crowding are strongly related to their general concern about the number of encounters with others (Fleishman et al., 2007), and their evaluations of crowding are associated with their crowding expectations and preferences (Robert et al., 1983).
This study adds to this latter stream of research and addresses the question how individuals – when making their timing choices – anticipate strategically what visit time slots are most popular. Understanding individuals’ anticipation of crowding is important because such anticipation may have a significant influence on individual visit timing decisions and thereby also on crowding distribution itself. This effect is particularly pronounced for recreational resources where it is likely that individuals share similar timing preferences, for example, because they face common constraints such as working hours or fixed holiday periods. If all individuals prefer the same visit timing but also dislike crowding, they need to take into account both their own timing and crowding preferences, and how others respond to potential crowding.
To address our research question, we rely on recent advances at the intersection of game theory and discrete choice models (Anderson et al., 2008, McKelvey and Palfrey, 1995). In studying individuals’ strategic anticipation of crowding in scarce recreational resources, our results from two experiments provide insights into how individuals incorporate strategic considerations regarding possible recreational resource crowding in their visit timing decisions.
Section snippets
Individuals’ strategic timing choices for recreation visits
To capture how individuals’ choices of recreational resource visit timing are affected by strategic considerations of other individuals’ timing decisions we build our model as a game of incomplete information. In this game individuals think ahead and devise a strategy based on expected timing decisions of other individuals. Individuals have expectations of the probability that other individuals will choose to use the facility at each time slot. Based on these expectations, the expected utility
Equilibrium in the visit timing game
To anticipate other individual’s visit timing choices, each individual needs to take into account not only the other individuals’ visit timing preferences, but also how these individuals respond to potential crowding and adjust their choices accordingly. These anticipated timing choices in turn affect each individual’s own timing decisions.
This structure represents a finite N-person game, where N is the total number of individuals visiting the resource across all relevant time slots. The
Experimental timing choice data
Testing the proposed model from real-world visit timing choices can be problematic because typically the anticipated inter-personal effects are confounded with (unobserved) personal constrains such as time budgets, scheduling obligations, and so on. Therefore the model estimation results typically are difficult to interpret. For this reason, we conducted two controlled experiments to test whether the proposed models’ underlying intuitions are valid.
Data for the study were collected over a
Tourist timing choice model
For both versions of the tourist timing choice task we estimate random coefficients logit models that allow for heterogeneity in individuals’ preferences for timing slots (see Eqs. (4), (5)). The model structure captures the repeated measures nature of our data in which each participant responded to multiple choice sets. We first define the utility model for the strategic dinner timing choice task:where is participant i
Tourist timing choice
Results for the tourist timing choice section of the experiment are reported in Table 1. The first column presents the parameter estimates for the version with strategic consideration. 61 individuals participated in this version. The overall fit of the model is good, with an adjusted pseudo rho-squared value of 0.43. One of the timing preference parameters and one of the capacity levels were significant at the 95% confidence level. Respondents showed a clear preference for a dinnertime at 7.30
Discussion
This study set out to address the question if and if so how, individuals anticipate strategically to what timing options are most popular with other individuals when making their own visit timing choices for scarce recreational resources. Understanding how individuals anticipate potential crowding can enhance researchers’ and managers’ abilities to capture the dynamics of individual visit timing choices in case demand exceeds supply for scarce recreational resources at popular visit times. We
Acknowledgements
The first author’s research was funded by SOBU: a collaboration between Tilburg University and Eindhoven University of Technology.
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