Customer loyalty and retention are crucial for digital platforms, yet systematic studies integrating technological innovation and loyalty strategies remain scarce. This study addresses this gap by conducting a bibliometric analysis of key factors influencing customer retention in the digital environment. Our research employs a quantitative bibliometric approach using the Biblioshiny and Bibliometrix tools (RStudio 2022.02), analyzing 300 scientific articles from the Web of Science database (2021–2024). This study applies bibliometric techniques such as descriptive metrics, bibliographic coupling, co-citation, and scientific collaboration mapping to identify trends and thematic clusters. Our findings indicate that emerging technologies, including artificial intelligence and big data, significantly impact customer experience, trust, and engagement. Personalization and digital innovation emerge as fundamental drivers of customer retention, offering strategic insights for companies aiming to strengthen competitiveness in the global digital market.

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.

This manuscript explores the role of green hydrogen produced through ethanol reforming in accelerating Brazil’s transition to a low-carbon economic framework. Despite ongoing efforts to lessen carbon dependence, Brazil’s reliance on biofuels and other renewable energy sources remains inadequate for fully achieving its decarbonization objectives. Green hydrogen presents a vital opportunity to boost energy sustainability, especially in sectors that are challenging to decarbonize, such as industry and transportation. By analyzing Brazil’s input–output (I-O) table, using data from the Brazilian Institute of Geography and Statistics (IBGE), this study evaluates the macroeconomic potential of green hydrogen, focusing on GDP growth and employment generation. Furthermore, the research explores green hydrogen systems’ economic feasibility and potential impact on future energy policies, offering valuable insights for stakeholders and decision-makers. In addition, this investigation highlights Brazil’s abundant renewable resources and identifies the infrastructural investments necessary to support a green hydrogen economy. The findings aim to strengthen Brazil’s national decarbonization strategy and serve as a model for other developing nations transitioning to clean energy.

Purpose: This paper aimed to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis.Design/methodology/approach: This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3).Findings: From the principal component analysis, considering mainly component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated through the production of medical residency, by specialty area. Finally, it was observed that the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV shows that these groups reflect similarities with respect to the scores and importance of the variables for both hospitals’ groups.Research limitations/implications: Among the main limitations it was observed that there was incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information were dealt with for 60% of SIMEC/REHUF performance indicators.Practical implications: The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units represented by the hospitals. It was possible to generate a large quantity of information in order to contribute with assumptions to complement the decision-making processes in these organizations.Social implications: Development of public policies with emphasis on hospitals linked to teaching centers represented by university hospitals. This also involved the projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice.Originality/value: The originality of this paper for the scenarios of the Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generates a particular scenario for the measurement of performance in hospital organizations.

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to apply a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model's predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.