Composite indicators are a very useful tool for conveying summary information on the overall performance of institutions and facilitating decision-making. Increasingly, there is a demand for indicators that allow performance to be assessed after the implementation of a strategy. This has several difficulties, and in this paper, we address three of them: how to evaluate at different points in time, how to estimate the weighting of the criteria and how to normalize the data. Our proposal is based on multicriteria techniques, using a recent method, uwTOPSIS, and is applied to data collected from 2975 students enrolled in the first year of science and engineering at the Industrial University of Santander (Colombia) from the first semester of 2016 to the first semester of 2019. In the paper, we show that our proposal makes it possible to measure and evaluate the academic performance of students at two points in time, and this allows the University to know whether its student support policy has been successful and to what degree it has been effective. Due to the large amount of data handled, data management has been done using R programming language, and model implementation has been done with Python.

En este artículo se analizan las emociones y la polarización de las comunidades digitales de diez candidatos presidenciales en las elecciones de Colombia, México, Bolivia, Argentina y Uruguay celebradas en 2018 y 2019. Para ello se ha extraído información de las cuentas de Twitter de los candidatos en el último mes de la correspondiente campaña, a raíz de lo cual se estudiaron 749.764 tuits y más de doce millones de palabras. Posteriormente, se hizo un análisis de contenido, cuyo resultado evidencia que el contenido emocional de los tuits evoluciona a lo largo del periodo de análisis; se aprecia, además, una diferencia en el contenido negativo/positivo de los comentarios en los tuits, y así, la polarización de las comunidades digitales. Junto a estos resultados, también se observa una negativización de los tuits, una nacionalización de las pautas del comportamiento digital y un incremento de la polarización que surge “espontáneamente”, y ello refuerza la idea de que existen valores intrínsecos a las dinámicas de la discusión política en las redes sociales. This article studies the emotions and polarization of the digital communities of ten candidates in the presidential elections held in Colombia, Mexico, Bolivia, Argentina, and Uruguay in 2018 and 2019. To this effect, information was extracted from the candidates’ Twitter accounts in the last month of the campaign, obtaining 749,764 tweets and more than twelve million words. Later, a content analysis was carried out, which evidenced that the emotional content of the tweets evolved over time, showing a trend of the negative/positive difference of the tweets and an increase in the polarization of these digital communities. As additional results, there is also evidence of a negativization of the tweets, a national rationale of patterns of digital behavior, and a “natural” polarization, reinforcing thus the existence of intrinsic values in the dynamics of political discussion on social media.

This article introduces an innovative questionnaire designed to assess key competencies in selection processes applicable across all organizational levels, including operational, tactical, and strategic. The questionnaire was developed using the Fuzzy Delphi Method, which integrates fuzzy logic to address the inherent ambiguity in consensus-building and decision-making processes. Fuzzy logic effectively manages uncertainty and subjectivity, optimizing the resources involved in selection procedures. This approach overcomes the limitations of traditional tools, offering a more objective and practical evaluation method. The instrument adapts the DISC and BFQ questionnaires to evaluate eight essential competencies, including decision-making, teamwork, and innovation. Its validation employed rigorous statistical techniques, such as Fleiss’ Kappa coefficient and Cronbach’s Alpha, confirming its reliability and internal consistency across 53 items. The practical implementation of this questionnaire in the selection process of an insurance company demonstrated its effectiveness, enabling quantitative classifications that align with organizational objectives while minimizing subjectivity. This questionnaire provides a scalable and resource-efficient solution that addresses the current demands of organizations, fostering equitable and evidence-based hiring decisions. The findings of this study establish a new standard in recruitment practices, significantly contributing to talent management and the optimization of selection processes.