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.

Digital transformation has become a key factor for educational development since it not only involves the integration of disruptive technologies but also the restructuring of the process and cultural adaptation that allows Higher Education Institutions (HEI) to respond to changes in society and the demands of the labor market. This article aimed to determine the level of knowledge about the different aspects of digital transformation in a HEI, encourage the adoption of disruptive technologies and facilitate an effective transition to digital educational environments through an analysis based on 6 dimensions such as: important principles and definitions, technological support for DT, Digital tools, disruptive technologies in DT, Security in Information Technologies and Digital Culture, applying two surveys as instruments to teachers, students and graduates of the university community. A quantitative approach with quasi-experimental design was used, evaluating a pre-test and post-test with a total of 34 questions distributed among the six dimensions analyzed using a Likert scale and applying the Wilcoxon statistical test for related samples. The initial findings indicate that in the first dimension “important principles and definitions” with 6 questions, two of them do not present adequate significance with values of 0.06505 and 0.051, respectively. On the other hand, the dimensions technological support (6 questions), Disruptive technologies (9 questions), Digital tools (6 questions) and Information Technology Security (ICTs; 4 questions), obtained significant results in each of their questions, finally the Digital Culture dimension (4 questions) presents a question with a p-value of 0.24 presenting no significant difference, that is, the answers of the participants did not vary. In addition, at the dimension level, the one that shows the best results is Disruptive Technologies with a p-value of 9.08×10⁻¹⁷, which indicates that after the training, significant improvements were obtained in this dimension as opposed to the rest. Finally, performing the global analysis of the dimensions in terms of gender the scores in women are higher with respect to men, in addition, considering the analysis based on roles it was determined that teachers show better results and students have lower scores.

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.

This study examines the relationships among telework, job satisfaction, and quality of work life among employees in the Peruvian public sector, specifically at the National University Pedro Ruiz Gallo in Lambayeque. Using a quantitative approach with an explanatory scope and a nonexperimental cross-sectional design, data were collected from 194 employees through a structured questionnaire. Structural equation modeling (SEM) was employed to analyze the relationships between variables. The results reveal a positive impact of telework on both job satisfaction (β = 0.389, p < 0.001) and quality of work life (β = 0.323, p < 0.001). Job satisfaction was found to mediate the relationship between telework and quality of work life (β = 0.425, p < 0.001). Additionally, years of work experience negatively moderated the relationship between telework and job satisfaction (β = −0.114, p = 0.017), whereas gender had a positive moderating effect (β = 0.275, p = 0.016). These findings contribute to the understanding of telework dynamics in the public sector and offer valuable insights for tailoring telework policies to maximize employee well-being and organizational effectiveness.