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This study aims to use a novel approach, focusing on the manager’s gender, to explore whether it acts as a differentiator in the following aspects of tourist accommodation companies in Extremadura (Spain): the level of information and communication technology (ICT) specialisation of employees, managers’ knowledge of ICTs and the social media and online tourism platform use intensity of managers.

A questionnaire was sent to 238 accommodation companies. The data collected from the questionnaire were analysed using statistical inference techniques and linear and logistic regression.

In general, ICT specialist profiles are more common amongst the employees of male-led companies. Male managers also use Booking and analyse online feedback more intensively. There appear to be no gender-based differences in terms of the ICT knowledge of managers.

These results highlight issues of major practical interest for the sector’s managers and decision makers, especially in Extremadura. They reveal the digital divide in certain aspects between men- and women-led firms in Extremadura. This finding has important consequences for the sector in terms of competitiveness. It highlights the need to continue working to eradicate gender gaps in digital settings.

The study shows the role of the manager’s gender as a differentiating factor in terms of the existence of specialist ICT profiles and ICT use intensity in tourism companies. To the best of the authors’ knowledge, this study provides the first evidence of such a finding for the tourism sector in general, as well as for the specific case of a rural destination such as Extremadura.

The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector.

The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies.

The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems.

This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.