Abstract Diversifying the tourist product by tourist segment is a strategy that local governments can use to counter seasonal concentration in tourist arrivals. The effectiveness of such a strategy can be assessed by using a statistical tool for analysing the changes in seasonal concentration by segment. Using a decomposition technique separating the roles of different tourist segments in changing the pattern and magnitude of seasonal concentration, this paper examines the changes in seasonal concentration in three popular tourist destinations in the Veneto region in the 2019–2022 period. Results show that the role played by each tourist segment in changing seasonal concentration varies according to the type of tourism of the destination considered.

Abstract In this note, we recollect some formulas and facts about linear mixed models in relation to Cohen’s effect size measure $$f^2$$ f 2 . It is shown how an estimate of the variance-covariance matrix for the estimated fixed effects parameter vector may serve to compute $$f^2$$ f 2 in the presence of random effects. The advantage of the purported approach lies in the fact that any variance-covariance estimate already available may be applied. This also illuminates the circumstance that an actual computed effect size value necessarily depends on the employed linear mixed model estimation procedures. To demonstrate possible applications, it is shown how $$f^2$$ f 2 can be computed with the statistical software environment using . Based on an artificially generated data set, the possible impact of the inclusion of fixed and/or random effects is exemplarily discussed. In addition, the application to several random variables is demonstrated with a publicly available dat set.