Using a probabilistic neural network and a set of financial and nonfinancial variables, this study seeks to improve the ability of the existing bankruptcy prediction models in the hotel industry. Our aim is to construct a hotel bankruptcy prediction model that provides high accuracy, using information sufficiently distant from the bankruptcy situation, and which is able to determine the sensitivity of the explanatory variables. Based on a sample of Spanish hotels that went bankrupt between 2005 and 2012, empirical results indicate that using information nearer to bankruptcy (one and two years prior), the most relevant variable is EBITDA to current liabilities, but using information further from bankruptcy (three years prior), return on assets is the best predictor of bankruptcy.

Recent tourism studies have shown that cruise passengers´ intention can be used as a tool to evaluate the sustainability of port of call destination. However, studies on this topic remain scarce and only offer an initial conceptualization of this issue. Hoping to help fill this void, the present research proposes a robust model for the analysis of the cruise passengers´ intention as assessed by Partial Least Squares. Data was collected in the port of Malaga (Spain), between January and December 2018. The results showed that reputation and familiarity are the best explanatory factors of the cruise passengers´ intention with a port of call destination. Also, cognitive perception and affective evaluation are the antecedents of reputation and familiarity.

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