This study evaluates the competitiveness of small- and medium-sized manufacturing enterprises (PYMES) through a meta-analysis that explores the role of technology and quality. Through a comprehensive literature review, 24 eligible studies, selected after applying specific criteria in a systematic search of the Scopus database, were identified and analyzed. A random-effects model was used to combine the effect sizes of the selected studies, and the heterogeneity among them was assessed using widely accepted indicators such as I², H² and tau², which confirmed a moderate variability among the studies. The results of the meta-analysis indicated a pooled effect size of 0.53 (95% CI: [0.50, 0.55]), suggesting a significant positive relationship between innovation and competitiveness, as well as between quality and competitiveness. Quality was identified as the most relevant competitive priority, while the use of state-of-the-art technologies was highlighted as a significant risk factor in the context of digital transformation. In addition, topic analysis was performed using the latent Dirichlet allocation (LDA) model, implemented with the topicmodels package in RStudio 2024.04.2. To ensure accuracy of the analysis, the texts were preprocessed using cleaning and tokenization techniques, which included the removal of punctuation, numbers and empty words. This thematic analysis identified key patterns related to innovation management, operational strategies and integration of digital technologies. The themes generated revealed that manufacturing PYMES prioritize quality as a source of competitive advantage, facing significant challenges associated with technological adoption and digitalization.

The steady advancement of online education has not automatically translated into improved educational quality. Teacher training often continues to focus on the technical use of digital tools, while the pedagogical processes through which teachers report supporting students’ digital competence remain insufficiently understood. The objective of this study was to examine the sequential and predictive structure of teachers’ digital competence using the DigCompEdu framework as a reference. A quantitative cross-sectional study was conducted with a sample of 136 university teachers involved in online education. Data were collected through a self-reported questionnaire based on DigCompEdu and analyzed in two phases: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANNs). The PLS-SEM results suggested a sequential pattern of associations among teacher-reported constructs: Professional Commitment (PC) was positively associated with Digital Resource Management (DR), which in turn was positively associated with Digital Pedagogy (DP) and Assessment and Feedback (AF). These dimensions were associated with Student Empowerment (SE), which showed the strongest positive relationship with teachers’ reported practices for Facilitating Students’ Digital Competence (FS). The ANN sensitivity analysis showed adequate predictive performance in the testing phase (RMSE = 0.155) and identified Student Empowerment as the predictor with the highest normalized importance within the specified model. These findings suggest that faculty development in online higher education may benefit from moving beyond basic digital literacy and platform management toward pedagogical design, formative assessment, inclusive participation, and learner agency. However, the results should be interpreted as evidence of teacher-reported facilitation practices within the analyzed sample, rather than as direct evidence of students’ actual digital competence development.

Este estudio analiza la valorización del pseudotallo del banano como una alternativa eco-sustentable en Ecuador, destacando su potencial para generar materiales biodegradables y fortalecer la economía circular. A través de una metodología cuantitativa basada en encuestas a 197 productores bananeros de la Zona 5, se exploraron percepciones y prácticas sobre el manejo de este subproducto. El análisis de red reveló que preguntas clave, como la disposición para comercializar el pseudotallo y el conocimiento sobre sus aplicaciones, actúan como nodos centrales en las percepciones de los productores. Los hallazgos sugieren una fuerte conexión entre la capacitación técnica y la adopción de prácticas sostenibles. Además, se identificaron barreras relacionadas con el acceso a tecnologías y mercados, lo que resalta la necesidad de políticas públicas que faciliten la implementación de estrategias de valorización. Este trabajo contribuye al desarrollo de modelos productivos sostenibles, promoviendo la integración de los pequeños productores en cadenas de valor eco-innovadoras.

The rapid expansion of generative artificial intelligence (GenAI) in virtual higher education environments has reconfigured the ways in which students access, process, and produce academic knowledge. Tools such as ChatGPT, Gemini, and Claude offer text generation, information synthesis, and problem-solving capabilities that, in the absence of face-to-face supervision and robust regulatory frameworks, can seriously compromise academic integrity. This article examines the ethical challenges and pedagogical opportunities associated with GenAI use in 100% virtual modalities, with emphasis on self-regulated learning, students' ethical awareness, and institutional policies. A mixed-methods approach with a sequential explanatory design is adopted, grounded in theoretical frameworks from Bandura's Social Cognitive Theory, Zimmerman's self-regulation model, Ajzen's Theory of Planned Behavior, and Bretag's academic integrity frameworks. Expected findings point to the identification of differentiated profiles of ethical and unethical use, the moderating role of self-regulation and policy clarity, and the formulation of pedagogical and institutional strategies to strengthen ethics in AI-mediated virtual contexts.