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The problem of overdispersion in multivariate count data is a challenging issue. Nowadays, it covers a central role mainly due to the relevance of modern technologies data, such as Next Generation Sequencing and textual data from the web or digital collections. This work presents a comprehensive analysis of the likelihood-based models for extra-variation data proposed in the scientific literature. Particular attention will be paid to the models feasible for high-dimensional data. A new approach together with its parametric-estimation procedure is proposed. It is a deeper version of the Dirichlet-Multinomial distribution and it leads to important results allowing to get a better approximation of the observed variability. A significative comparison of these models is made through two different simulation studies that both confirm that the new model considered in this work allows to achieve the best results.

Individuals’ participation in tourism recreation events can be constrained by a number of situational factors and can also be bolstered by key influences such as a desire for a particular social identity. This study extends the current body of research by investigating the effects of social identity, motivation, and perceived constraints on desire, and thus on the intent to participate in organized motorcycle tourism events. This research was carried out on a sample of participants at an internationally acclaimed motorbike event: the Transitalia Marathon. The results of the structural model indicate that social identity has a positive influence on motivation, the desire to participate, and the intent to participate. In addition, social identity has a negative, direct effect on perceived constraints, which in turn have a negative effect on motivation. This motivation has a positive, direct influence on the desire to participate. The practical and research implications of this study are presented herein.