Postvisit Destination Loyalty Judgments :  Developing and Testing a Comprehensive Congruity Model

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Abstract

Previous research has established the effect of self-congruity on both pre- and postvisit constructs, but its predictive power has appeared minimal. Departing from both classical and contemporary approaches to human needs and values, this study proposes a comprehensive model explaining more variance in postvisit destination loyalty judgments. The model comprises six explanatory variables, in addition to self-congruity: functional, hedonic, leisure, economic, safety, and moral congruity. Based on a large-scale web survey among tourists (N = 973), the results provide good support for the proposed model (64% explained variance). Each of the seven congruity components exerts a significant influence on postvisit loyalty, but their relative contributions differ considerably. Other than self-congruity, functional, hedonic, leisure, and safety congruity exert the greatest influence on postvisit loyalty judgments; in contrast, economic and moral congruity have lesser influences. The authors discuss the results in light of their theoretical and practical implications for destination marketing.

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A power primer.

Jacob Cohen (1992)

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.