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Abstract
Previous literature has shown that important variations exist over time regarding the extent to which visitors make return trips to tourist destinations. This paper provides an analytical framework to account for time discrepancies in behavior and helps predict repeat visits to a destination at different time points. The authors carried out an empirically validated three-parameter Weibull distribution and a 50-period numerical simulation exercise to illustrate ways in which the pattern of repeat visits affects total visitors through time. Several scenarios representing different distribution parameters and different intensities of initial trips are illustrated. Implications for destination managers and future research extensions are suggested.
Keywords: Repeat visits, tourist destinations, recency-frequency theorem, simulation.
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Introduction
Repeat visits are an important element of tourism for both the economy as a whole and the individual tourist attraction. According to a 2005 report by the English Tourist Board (ETB), 79% of all overseas visitors to the United Kingdom made a repeat visit over the period 1994-2004. For leisure visitors only, the percentage for the same period dropped to 60%. Individuals visiting friends and family (VFRs), had the highest percentage of repeat visits (84%), and the percentage across all visitor types increases with the visitor's age: in the 16- to 24-year-old category, 12% made repeat visits, while the percentage rises to 39% for those older than 45. A later customer survey by the ETB (2007) stated that 28% of overseas visitors had visited London between two and five times in the preceding five years. Visitor surveys at individual tourist attractions have emphasized the significance of repeat visits; at the British Museum for example, a visitor survey (Caygill & Leese, 1994) for 1992-1993 showed that in June 1993, 51% of visitors had made an earlier visit, and 22% had made six or more visits in the previous 12 months.
The importance of repeat visits has been further identified in the findings of econometric studies of both international tourism flows (e.g., Akal, 2004; Cho, 2003; Johnson & Ashworth, 1990) and the demand for individual attractions (e.g., Darnell, Johnson, & Thomas, 1990, 1998). These studies illustrate the important explanatory role that lagged dependent variables play in predicting visits, such that a visit in a current period affects the...