This article provides a systematic literature review of recent research on money laundering in crypto-asset environments, focusing on the main operational challenges and technical solutions proposed. Following PRISMA 2020 guidelines, the review draws on searches in Web of Science, Scopus and Google Scholar, which identified 680 records and led, after screening and the application of inclusion and exclusion criteria, to a final sample of 58 academic studies published between 2020 and 2025. The review identifies four core challenges in blockchain-based anti-money laundering: pseudonymity, label scarcity and class imbalance, structural and computational complexity, and cross-blockchain data fragmentation. In response, the literature proposes several detection approaches, particularly feature-based and graph-based models, focusing on transactions, addresses, mixers and service providers. The findings show that, although these methods improve the detection of suspicious activity, important limitations remain regarding real-world identity attribution, reliable AML-specific ground-truth labels, scalability, operational validation and cross-chain flow reconstruction. The article contributes an analytical framework that links structural AML challenges with methodological responses, supporting clearer comparison of models and identifying priorities for future research. It also highlights the need for multidisciplinary collaboration across data science, finance, regulation, economics and forensic investigation.

La expansión de la inteligencia artificial generativa está transformando rápidamente los procesos de aprendizaje en la universidad. Herramientas capaces de generar textos, sintetizar información o resolver problemas pueden mejorar el rendimiento inmediato en diversas tareas académicas, pero también plantean interrogantes pedagógicos. El objetivo de este trabajo es analizar cómo el uso de inteligencia artificial generativa puede influir en los procesos cognitivos implicados en el aprendizaje profundo. A partir de la literatura sobre dificultades deseables, efecto generación, externalización cognitiva y pensamiento crítico, el artículo examina el riesgo de confundir fluidez con comprensión en entornos educativos mediados por inteligencia artificial. Asimismo, se propone el concepto de fricción cognitiva como marco interpretativo para analizar las condiciones que favorecen la implicación cognitiva del estudiante en el aprendizaje. Finalmente, se presentan principios pedagógicos para integrar estas tecnologías en la educación superior sin sustituir los procesos cognitivos que sostienen el aprendizaje significativo.

This study aims to identify the extent to which a country's level of governance implicitly determines and encourages the use of cryptocurrencies, and the main elements associated with the use of alternative currencies to traditional ones. The methodology used is a descriptive analysis of the variables, an econometric analysis through an ANOVA, and the application of a truncated regression model, which aims to bring the research closer to the possible correlation between governance indicators and the rate of adoption of cryptocurrencies. The study concludes that countries with low levels of governance are directly related to the greater adoption of cryptocurrencies. To the best of our knowledge, this study is the first to analyse the relationship between cryptocurrency adoption and institutional governance by comparing two regions with different levels of development. The research is limited by the existence of other factors that influence the analytical framework of cryptocurrency adoption, but the availability of data has allowed the present study to focus on governance aspects. Now, despite the fact that the governance indicators present a global analysis in terms of their measurement, the relevant aspects of each country are not specified. The adoption of cryptocurrencies in some countries may not be strongly related to governance aspects but rather to the friendly regulations that have been implemented.

Purpose: This paper aims to identify the factors associated with the forecasting errors in Spain's budget balance during the period 1999–2024. To this end, the annual fiscal projections reported in Stability Programmes (SPs) following the implementation of the Stability and Growth Pact (SGP) have been used. Design/methodology/approach: To achieve this purpose, we use an analysis of cointegration in time series. The dependent variable is the budget balance (% GDP), and the independent variables are divided into three categories: economic, political and institutional. Real GDP forecast errors, the provisional fiscal outcome and the persistence of deviations in fiscal forecasts are included as economic variables. The construction of these three variables is based on information extracted from SPs, as well as official datasets provided by the Instituto Nacional de Estadística (INE) and the Intervención General de la Administración del Estado (IGAE). Political variables on general elections and the ideological orientation of successive governments were obtained from the Comparative Political Dataset (CPDS), and the institutional variable used is the Autoridad Independiente de Responsabilidad Fiscal (AIReF). Findings: Economic and political factors are relevant to explain the fiscal deviations, but the influence of the AIReF is not significant. Originality/value: There are few studies focused on the analysis of forecast errors for fiscal variables that analyse a single country individually. This research will allow establishing recommendations for the implementation of Spanish fiscal policy in the context of European budgetary governance given the evidence that deviations affect the sustainability of public finances.

Purpose: This study aims to explore the motivations that drive entrepreneurs to adopt sustainable business practices, with a particular focus on how the desire to make a difference in the world and awareness of the Sustainable Development Goals (SDGs) influence sustainable entrepreneurship. Design/methodology/approach: Using the theory of planned behaviour as a theoretical framework and 2022 data from the Global Entrepreneurship Monitor encompassing 21,053 entrepreneurs across various countries, a multilevel logit model was employed to assess the impact of SDG awareness and entrepreneurial motivation on the implementation of sustainable practices. Findings: The findings indicate that entrepreneurs who are aware of the SDGs and motivated to contribute meaningfully to society are more likely to engage in sustainable practices. Sustainable strategies emerged as the primary driver of sustainable behaviour and motivational factors were found to play a crucial role in translating awareness into action. Originality/value: This study provides novel empirical evidence on the relationship between individual entrepreneurial motivations and sustainable actions in a global post-COVID context, filling a gap in the literature on the role of personal intention in sustainable entrepreneurship. The research offers practical and social value by revealing how individual motivations drive sustainable entrepreneurial behaviour. It provides insights that can be used to develop policy initiatives and institutional programmes. These measures are aimed at raising awareness of the SDGs and fostering intrinsic motivation to engage in sustainability-orientated ventures. This dual insight strengthens the foundation for more effective strategies to promote responsible and inclusive entrepreneurship aligned with global sustainability goals.