Digital transformation has become constant and has forced governments to reevaluate the validity of their educational models; therefore, regarding digital and information literacy, to train teachers to improve new digital skills becomes essential. For these reasons, this research will explore the instruction of teachers in digital and information literacy in basic education; likewise, there will be an observation of the research’s theoretical-methodological characteristics related to these variables, and, also, we will carry out an analysis of the most pertinent contributions on the impact of new literacies and competencies in the teaching–learning processes in basic education, with the purpose of obtaining a current state overview of its teacher training within the framework of the technologies’ usage linked to teaching. This review was based on the guidelines of the PRISMA protocol, and to select 56 documents, the Scopus and Web of Science (WoS) databases were used. The results show that, in the Scopus database, Spain is the country with the most research on the subject, with 29% of the total, followed by Indonesia, with 6%, and the United States, with 4%, and that the articles focus mainly on the social sciences and computer science. Likewise, in WoS, the country with the most research on the subject is Spain, with 30%, followed by Russia, with 10%, and Norway, with 8%, and the articles mainly revolve around the categories of education and communication. The research related to this topic uses a quantitative approach in 68%, a qualitative approach in 25% and a mixed approach in 7%. It was shown that there is a direct relationship between digital and information literacy and digital competency. In addition, it is also emphasized that digital and information literacy are continuous and long-term processes. More didactic proposals on digital skills would be necessary, over government policies and efforts, to achieve a community with a high level of digital and information literacy.

This study evaluated the maturity of Digital Transformation (DT) in a Higher Education Institution (HEI), analyzing how teachers, students, administrators and managers perceive various dimensions of DT, including Sociocultural, Teaching-Learning, Digital Governance, Research-Innovation, Academic Management, Administrative Management, Institutional Image-Marketing and University Extension. A quantitative approach was used, applying four structured surveys validated by experts to 338 participants from a HEI, which were processed by applying Spearman’s correlation analysis. It was found that digital maturity varies significantly according to the actor and the dimension considered. For teachers, Digital Governance shows strong links with University Extension and Research, suggesting that digitalization enhances both social projection and academic collaboration. Students perceive the importance of Institutional Image-Marketing in creating a positive presence of the institution. As for administrative staff, administrative management is strongly related to Institutional Image, reflecting the impact of internal efficiency on public perception. Managers, on the other hand, emphasize the role of University Extension in strengthening institutional prestige. The study concludes that DT in HEIs is multifactorial and recommends adopting customized digital maturity strategies that respond to the needs of each actor, thus facilitating an effective and sustainable digital transformation.

Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

In these times of increasing digitization and process automation, digital transformation (DT) has become a determining factor for continuous improvement and competitiveness in organizations in all areas. In the context of higher education institutions (HEIs), DT plays a fundamental role in the evolution of teaching and learning, as well as in the administrative and operational management of universities, where the use of digital tools and strategies allows greater accessibility and flexibility in education, facilitating innovation, collaboration, and knowledge generation in globalized environments. The objective of this research was to develop a theoretical-methodological framework (TMF) that allowed the creation of a model to determine the level of maturity of DT in HEIs, for which a qualitative research methodology was used, based on grounded theory, with eight phases: literature review, interviews with managers and administrators, content analysis of the interviews, definition of dimensions, definition of components, definition of actors, creation of the practical model, and determination of maturity levels. This methodology allowed the construction of a digital maturity model integrated with eight dimensions, based on the main organizational processes in higher education, with four key components of DT and four relevant actors of the university system, thus structuring a framework that allows analyzing and evaluating the state of DT in the various institutions and thus improving the quality and efficiency of their processes in the current digital era.

The COVID-19 crisis has turned teleworking from a minority option into an imposed and generalized way of life and has called into question its contribution to corporate sustainability. The present review is the first systematic review of the effects of telework on the environmental, social, and economic pillars of corporate sustainability in the scholarly literature published from 2020 to 2024. A total of 50 studies from three databases (Scopus, Science Direct, and Taylor and Francis) were reviewed according to PRISMA guidelines by both a data bibliometric analysis and narrative synthesis. The findings show that telework has the potential to improve environmental sustainability by decreasing commuting emissions (29–54% depending on its deployment intensity), but rebound effects such as increased residential energy use work against this (in part) positive regard. From a social point of view, telework is double-edged between helping balance personal and work life and possessing the potential to lead to greater isolation and aggravate existing inequalities, particularly in developing countries. Economically, it drives operational costs down and expands the talent pool, with micro-, meso-, and macroeconomic impacts. The possibility of telework as a tool of sustainable development is substantially moderated by organizational culture, digital infrastructure, sociodemographic reality, and even the physical environment. We argue that telework is a potentially transformative driver of corporate sustainability if deployed strategically within a given context; however, disciplinary fragmentation and methodological lacunae in common metrics remain, especially with regard to long-term effects and implementation in developing economies.

El dengue es una enfermedad arboviral que anualmente reporta un gran número de infectados en la costa norte y la selva peruana. Según las estadísticas, está aumentando cada año. Este artículo tiene como objetivo desarrollar una revisión sistemática de la literatura científica sobre las variables de estudio y los métodos de aprendizaje automático utilizados actualmente para detectar la infección por dengue. La metodología utilizada fue PRISMA, mapeando inicialmente la literatura de 274 artículos científicos, quedando seleccionados 33 artículos para la revisión sistemática. Los resultados obtenidos son que los algoritmos de aprendizaje automático más utilizados son las redes neuronales (NN) y support vector machine (SVM). Asimismo, se ha encontrado que los científicos tienden a realizar investigaciones con variables climáticas o demográficas para obtener mejores resultados. Se concluye que los métodos de aprendizaje automático que más se han utilizado son las redes neuronales de diferentes tipos: convolucional, recurrente, profunda y multicapa, y para la predicción de brotes de dengue predominaron los métodos de series de tiempo con LSTM y ARIMA, también se estableció que la tendencia es hacia la inclusión de variables climáticas y demográficas en los modelos de predicción.