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A conceptual and methodological framework of leisure activity loyalty accommodating the travel context

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Abstract

As leisure travel continues to grow, it has become a critical subject for planners and decision-makers since it significantly impacts regional economic and social development as well as contributes to emission levels and congestion. Despite being a significant percentage of our travel, however, leisure travel behavior is still not very well understood. The goal of this article is to contribute to our understanding of leisure activity participation by considering leisure activity loyalty within the travel context. In particular, this study focuses on one specific dimension of travel context: travel extent (i.e., whether an individual participates in a leisure activity on a daily versus a long-distance basis). As such, this article first introduces a unified conceptual framework for measuring leisure activity loyalties within a travel context, based on two distinct dynamics of leisure loyalty behavior—destination attachment and activity involvement. Additionally, this article uses a unique 2001 NHTS dataset comprised of households’ daily and long-distance leisure activities to undertake a unique empirical analysis of five distinct leisure activities using the conceptual framework and a copula-based model methodology. The findings confirmed that households demonstrate significant loyalties to travel contexts across all leisure activities, especially resting and sightseeing.

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Notes

  1. This upper limit was based on the observation that 99.9% of all households participated in 15 or fewer long-distance episodes and 15 or fewer daily trips. The remaining 0.1% of households reported an unrealistic number of trips.

  2. Thus, in the context of the current analysis, F(.) may be the standard logistic cumulative distribution function or the standard normal distribution function. The same is the case with G(.). Note that, in the approach we use, it is not necessary that both F(.) and G(.) should be simultaneously logistic (logistic–logistic) or simultaneously normal (normal–normal). Rather, we can also test the normal–logistic and logistic–normal pairings.

  3. An important note here. Many of the Archimedean copulas (including the Clayton, Gumbel, and Joe copulas) can only accommodate positive dependencies (unlike the FGM, Gaussian, and Frank copulas). Thus, these copulas cannot even handle the situation of potential negative dependence (i.e., dedicated travel loyalty effects). However, to examine the appropriateness of these copulas for the potential presence of dedicated loyalty effects, one only has to re-formulate the model system in Eq. (1) by introducing the v q term in the first equation with a negative sign.

  4. Due to space considerations, we are unable to provide additional details on the structures of different copula types. Interested readers are referred to Bhat and Eluru (2009). Also, note that the independence copula, as should be self-explanatory, is a copula that assumes independence. In the notation of “Model structure” section, the independence copula corresponds to \( C_{\theta } \left( {u_{ 1} ,u_{ 2} } \right) = u_{ 1} u_{ 2} . \)

  5. The Frank’s copula allows a stronger central clustering of data points and lesser clustering at the edges relative to the Gaussian copula. In the current empirical context, this means that individuals are likely to be clustered around the medium-medium levels of the two-dimensional daily and long distance loyalty spectrum, and less so at the low–high end or the high-low end of the spectrum, given the negative dependence.

  6. Kendall’s measure of dependency (τ) transforms the dependency parameter (θ) into a number between −1 and 1 (see Bhat and Eluru 2009). For the Frank copula,

    $$ \tau = 1 - \frac{4}{\theta }\left[ {1 - \frac{1}{\theta }\int_{t = 0}^{\theta } {\frac{t}{{e^{t} - 1}}dt} } \right] $$

    and –1 < τ < 1. Independence is attained in Frank’s copula as θ → 0.

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Acknowledgments

The authors acknowledge the helpful comments of three anonymous reviewers on an earlier version of the article. This research was partially funded by a Southwest Region University Transportation Center grant. The authors are grateful to Lisa Macias for her help in formatting this document.

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Authors and Affiliations

  1. Department of Civil Engineering, Auburn University, 238 Harbert Engineering Center, Auburn, AL, 36849-5337, USA

    Jeffrey J. LaMondia

  2. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, 1 University Station C1761, Austin, TX, 78712-0278, USA

    Chandra R. Bhat

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Correspondence to Chandra R. Bhat.

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LaMondia, J.J., Bhat, C.R. A conceptual and methodological framework of leisure activity loyalty accommodating the travel context. Transportation 39, 321–349 (2012). https://doi.org/10.1007/s11116-011-9342-0

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