Manuel Angel Fernández Gámez

Multiple variables determine holiday rentals’ price composition in cultural tourism destinations. This study sought, first, to test a model including the variables with the greatest impact on tourism accommodations’ prices in these destinations and, second, to demonstrate the proposed model’s applicability to cultural city destinations by identifying the adaptations needed to apply it to different contexts. Two cities were selected for the model application—Seville in Spain and Porto in Portugal—both of which are located in different countries and are well-known cultural tourism destinations. The data were extracted from Booking.com because this accommodations platform has adapted its offer to the sharing economy, becoming one of the most important players in the market, and because research on holiday rentals using data from Booking.com is scarce. The results show that the variables used are relevant and highlight the adaptations necessary for specific cultural tourism destinations, thereby indicating that the model can be applied to all cultural tourism destinations. The proposed approach can help holiday rental managers select the correct tools for determining their accommodation units’ daily rates according to their product and marketing context’s characteristics.

The literature on the inclusion of students with disabilities in the university setting highlights the great importance of the faculty's willingness to provide quality accommodations. However, current studies offer only an initial analysis of the factors explaining these accommodations. Current research requires the analysis of new actions that will improve the accommodations provided by the university faculty. The objective of this study is to research the effect that innovative programmes and interventions have on the provision of accommodations to university students with disabilities. Using structured questionnaire, sample of 182 university professors, and structural equation model, the results indicate that innovative programs and interventions have a significant influence on the provision of accommodations by improving both personal beliefs regarding students with disabilities and the professors' perception of institutional support. Suggestions are proposed for implementing strategies to implant intervention programs that will improve the faculty's willingness to provide accommodations and support to students with disabilities.

Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.

Over the past decade, there has been a growing interest in studying the impact of sustainable tourism development on economic growth. However, despite its recent scope, the scientific literature published so far has not evaluated the performance of the scientific activity of this relationship. Consequently, this study analyzes the 668 articles published to date in the Web of Science database on the effect that sustainable tourism development has on the overall long-term progress of the economy. To this end, we carry out an analysis of the most recognized authors, regions with the highest percentage of scientific production, most influential organizations, the co-occurrence of keywords, most prominent citations, publications, and co-authorship among the most recognized authors. The results obtained show the trend and impact of the literature published to date and the established and emerging research groups. Also, they identify key research topics in a way that provides a planning framework for further research in this field.

Studies on financial behavior indicate that emotional intelligence (EI) and personality traits (PTs) explain much of the bias in financial activity. This study aims to identify in which dimensions of theEI and PTs of university students in finance further training is needed to avoid financial behavior bias. To this end, the EI and PT levels of a sample of university finance students and financial industry professionals were compared using the Trait Emotional Intelligence Questionnaire (TEIQue) and Big Five Inventory questionnaire. Subsequently, the dimensions of EI and PTs in which students have a deficit compared to professionals were identified, and the impacts that this deficit causes on the financial behavior of students were determined. The results indicate that students are deficient in the EI competencies related to empathy, emotion regulation, self-motivation, stress management, optimism, and self-esteem. Furthermore, PTs are related to kindness, awareness, openness, and extraversion. This deficit makes students more likely to have financial behavior biases such as risk tolerance, endowment, optimism, self-control, and loss aversion. These findings suggest that universities should be aware of providing financial students with full training in EI and PTs to help them successfully address their professional future.

In recent years, the number of sharing economy accommodations has grown exponentially due to the Internet and peer-to-peer networks, which has made researchers increasingly interested in analysing this new type of lodging. This study sought to develop models that determine the significant variables for the daily price of staying in holiday rentals based on data extracted from Booking.com and other sources. The hedonic pricing method (HPM) was selected to conduct the research as this methodology has been widely used in real estate valuation and hotel daily rate determination; however, the HPM is still rarely used for holiday rentals. The study focused on the city of Seville, where a notable increase in holiday rentals has been observed in recent years. Variables related to the accommodation typology, including location, size and equipment, as well as seasonality, are the most influential factors in the proposed models. These results are of interest to both owners and users of holiday rentals and can help these individuals to determine if the price of a stay is what would commonly be offered in the market under normal circumstances.

Corporate reputation enhancement in the restaurant industry has recently been increasingly driven by the central importance of consumer review websites and customers’ greater awareness of sustainable practices regarding health and the environment. In this context, the research question of the present study was if there is a relationship between health and environmental conditions, and restaurants’ corporate reputation on a country level. Trying to answer this question, the present study sought to analyze the effects of countries’ health and environmental conditions on their restaurants’ corporate reputation, thereby contributing to the existing knowledge about how sustainable environments influence the industry’s competitiveness. The research design included different methodological approaches, and was divided into three main phases: restaurant corporation identification, reputation database design, and results. To this end, reputation data from a consumer review website were gathered for a sample of restaurant corporations and establishments connected to the European countries on the Healthiest Country Index.The methods were based on regression analysis. The results indicate that restaurant reputation improves in healthy, sustainable environments, specifically in countries ranked as the healthiest. These findings provide a better understanding of how aspects related to health and environmental sustainability influence corporate reputation.

The financial performance of football clubs has become an essential element to ensure the solvency and viability of the club over time. For this, both the theory and the practical and regulatory evidence show the need to study financial factors, as well as sports and corporate factors to analyze the possible flow of income and for good management of the club’s accounts, respectively. Through these factors, the present study analyzes the financial performance of European football clubs using neural networks as a methodology, where the popular multilayer perceptron and the novel quantum neural network are applied. The results show the financial performance of the club is determined by liquidity, leverage, and sporting performance. Additionally, the quantum network as the most accurate variant. These conclusions can be useful for football clubs and interest groups, as well as for regulatory bodies that try to make the best recommendations and conditions for the football industry.

A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.

Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008–2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity.