The concept of sustainability has gone far beyond the issues of the sustainable management of natural and environmental resources. Nowadays, sustainability is part of the social sciences in a different way. The aim of this research was dual. Firstly, we analyzed the different contexts and areas of knowledge where this concept is used in society by using social listening on Twitter, one of the most popular social networks today. The sentiments of these conversations were rated to assess whether the feelings and perceptions of these conversations on the social network were positive or negative regarding the use of the concept. Also, we tested if these perceptions about the topic were attuned to other more formal fields, such as scientific research, or strategies followed nationally or internationally by agencies and organizations related to sustainability. The method used on this first part of the research consisted of an analysis of 15,000 tweets collected from Twitter using natural language processing (NLP) for clustering the main areas of knowledge of topics where the concept of sustainability was used, and the sentiment of these conversations on the social network. Secondly, we mapped the social network of users who generated or spread content regarding sustainability on Twitter within the period of observation. Social network analysis (SNA) focuses on the taxonomy of the network and its dynamics and identifies the most relevant players in terms of generation of conversation and also their referrers who spread their messages worldwide. For this purpose, we used Gephi, an open source software used for network analysis and visualization, that allows for the exploration and visualization of large networks of any kind, in depth. The findings of this research are new, not only because of the mix of technology and methods used for extracting data from Twitter and analyzing them from different perspectives, but also because they show that social listening is a powerful method for analyzing relevant social phenomena. Listening on social networks can be used more effectively than other more traditional processes to gather data that are more costly and time consuming and lack the momentum and spontaneity of digital conversations.

The technological revolution we have been witnessing over the last 30 years is bringing about important changes in business management, specifically in the conception, development, and management of its financial aspects. How to face these challenges is a particularly critical question for individual entrepreneurs who have to assume a much greater responsibility for decisions than in other corporate environments. In this sense, it is very important to understand the financial capabilities and skills and their limitations of this type of entrepreneurship, which is so important for the renewal and extension of the business community in any developed economy. Based on an extensive official survey of this type of entrepreneurship, this research proposes a logistic regression modeling, in order to find out the level of financial knowledge of this business group. One of the findings of this research is that individual entrepreneurs have a higher level of financial knowledge than salaried workers, with a higher qualification in this respect in the case of individual entrepreneurs with employees. This study analyses the link between individual entrepreneurs or self-employed workers – with or without employees – and financial literacy. In addition, we investigated the relationship between this group with their socio-demographic characteristics, financial product holdings, financial planning, and financial fragility. Different binary logistic regression models with maximum likelihood were estimated using a sample of individual entrepreneurs and self-employed workers from the Survey of Financial Competences database conducted in Spain to achieve this. The results of the study show that individuals who choose to be individual entrepreneurs or self-employed, and in particular those with hired workers, had a higher level of financial literacy than salaried workers. Moreover, it was confirmed that financial literacy complements the explanatory power of the other variables analyzed with respect to sole proprietors without employees, although not to a significant degree. The limitations of this work are due to the use of data from the Financial Skills Survey, so some dichotomous variables offer very generic information. In addition, the database is cross-sectional and we cannot analyze variations between different periods. No research before had applied the relationships between the behavior of individual entrepreneurs, with and without employees, and financial competencies using logistic functions and on the basis of a rigorous and comprehensive formal questionnaire. The conclusions provide a valuable perspective for developing training programs for this type of human capital aimed at raising the financial skills of individual entrepreneurs to improve personal and business management.

COVID-19 has profoundly disrupted national education systems, affecting the future well-being of school-age children. Bacher-Hicks and colleagues showed that the intensity of the search for online learning resources in the United States doubled with respect to pre-COVID-19 levels. However, areas of the country with higher incomes, better internet access, and fewer rural schools experienced significantly greater increases in search intensity. Using a similar method to study the case of Spain, we analyze the evolution of search intensity for a selection of digital educational resources over the period 2015 to 2021. Special attention is paid to the period of COVID-19 pandemic, namely, March 2020 to June 2021. The findings include a widespread rise in the use of digital resources with respect to the use in the last 5 years, which varies by digital educational resource and region. However, the use of digital resources in Spain does not seem to vary according to family income, at least in relation to open access digital educational resources. In addition, there appears to be a steady decline in search intensity for digital educational resources and a certain degree of substitutability following the surge due to the pandemic and the school closures.

Objective: This study aims to empirically analyse the relationship between gender and financial education. In addition, the study examines whether certain socio-demographic characteristics, the financial product portfolio itself, financial planning and savings attitudes can explain behavioural differences between men and women. Our approach stands out for its originality in using a representative dataset and statistical learning techniques. Methodology: To carry out this research work, data from the Survey of Financial Competences conducted by the Bank of Spain and the National Securities Market Commission, on a representative sample of 8,554 individuals in Spain, is analysed. Statistical learning methods, based on classification trees and gradient boosting, are used to identify the variables that best explain the gender difference in this context, and to obtain a statistical classification of the observed population. Results: Financial competencies provide a partial explanation for the gender gap. Employment status stands out as an explanatory factor for said gap. Limitations: The limitations of this work are due to the use of the variables present in the Survey of Financial Competences, some of which are dichotomous and, therefore, offer very generic information. Moreover, the study is cross-sectional, and we cannot analyse changes over time. Practical implications: The findings provide valuable input for implementing differentiated programs and strategies targeting women to help mitigate gender inequalities.

This study examines how international agreements (especially the 1992 Rio Summit, the 1997 Kyoto Protocol, and the 2015 Paris Agreement) on climate change have fueled rampant economic literature worldwide. However, it has not been systematically classified or distinguished from the more traditional studies in this field. Hence, we use a scientometric analysis using four different approaches: natural language processing (NLP), citation analysis, co-citation network analysis, and content analysis. We conduct an ambitious Boolean search of 30 terms in Scopus and use NLP, along with unsupervised statistical learning techniques and content analysis to classify and analyze 2400 of the most relevant studies in this field. As such, independent results are complementary. We provide novel literature by mapping four major clusters: climate change corporate, climate finance, climate capitalism, and climate gateway belief.