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.

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.

Competitive intelligence (CI) is a business tool within strategic management, and it is gaining significance as a process that enables companies to achieve sustainable competitive advantage. This study explores the current state of CI in the Spanish hotel industry. For this purpose, a path model has been developed which empirically investigates the relationship between CI use and its backgrounds. The results obtained suggest that environmental and organizational characteristics affect CI effort, and in turn, CI effort affects CI use. This study provides theoretical and practical implications to help managers develop sustainable competitive advantages through the potential that CI offers within the hotel industry.

The current literature on sustainable practices shows that social missions and economic achievement can be compatible. However, very few studies have offered a quantitative perspective on this relationship. This study presents the first empirical evidence of the effects of the creating shared value (CSV) principle on the performance of companies. Using a sample of Spanish hotels, the results suggest that companies that apply CSV achieve higher financial performance. These findings represent a substantial contribution to the literature and shed light on how to efficiently implement CSV practices to simultaneously achieve economic and social benefits.