Vulnerabilities represent a constant and growing risk for organizations. Their successful exploitation compromises the integrity and availability of systems. The use of specialized tools facilitates the vulnerability monitoring and scanning process. However, the large amount of information transmitted over the network makes it difficult to prioritize the identified vulnerabilities based on their severity and impact. This research aims to design and implement a prioritization model for detecting vulnerabilities based on their network environment variables and characteristics. A mathematical prioritization model was developed, which allows for calculating the risk factor using the phases of collection, analysis, and extraction of knowledge from the open information sources of the OSINT framework. The input data were obtained through the Shodan REST API. Then, the mathematical model was applied to the relevant information on vulnerabilities and their environment to quantify and calculate the risk factor. Additionally, a software prototype was designed and implemented that automates the prioritization process through a Client–Server architecture incorporating data extraction, correlation, and calculation modules. The results show that prioritization of vulnerabilities was achieved with the information available to the attacker, which allows evaluating the overexposure of information from organizations. Finally, we concluded that Shodan has relevant variables that assess and quantify the overexposure of an organization’s data. In addition, we determined that the Common Vulnerability Scoring System (CVSS) is not sufficient to prioritize software vulnerabilities since the environments where they reside have different characteristics.

Ecosystems are currently at risk, and scientific methods have been developed to assess these impacts. In this scenario, the circular economy makes it possible to reuse raw materials and reduce waste. The objective of the research is to analyze the scientific production related to the evaluation of environmental risks in ecosystems with a circular economy approach. Quantitative research was carried out, with a retrospective and descriptive approach, from a bibliometric study in the SCOPUS database in the period 2014-2024. The peak of research was 4, where research articles predominated with 7 in 12 areas of knowledge. The most producing country was the United States. The most producing journal was Thunderbird International Business Review with 159. four research lines and their gaps were identified. Environmental risk assessment is much more than a legal requirement; it is an opportunity to demonstrate an organization's commitment to sustainability.

Purpose: The objective of the research was aimed at achieving the implementation of the theory of constraints on the operating conditions of a hotel, which differs by its characteristics of traditional processes that have applied this method, from the great heterogeneity of resources needed to meet the demand of customers.Design/methodology/approach: To achieve this purpose, a method of generating conversion equations that allowed to express all the resources of the organization under study depending on the number of customers to serve facilitating comparison between different resources and estimated demand through techniques developed traditional forecasting, these features were integrated into the classical methodology of theory of constraints.Findings: The application of tools designed for hospitality organizations allowed to demonstrate the applicability of the theory of constraints on entities under conditions different from the usual, develop a set of conversion equations of different resources facilitating comparison with demand and consequently achieve improve levels of efficiency and effectiveness of the organization.Originality/value: The originality of the research is summarized in the application of the theory of constraints in a very different from the usual conditions, covering 100% of the processes and resources in hospitality organizations.

This study analyzes the current state of artificial intelligence (AI) adoption among micro-, small-, and medium-sized enterprises (MSMEs) in Ecuador, with a focus on its application across core business functions. Using a stratified random sample of 385 firms from the most representative economic sectors, a survey instrument was designed to assess three dimensions: access to AI-enabling conditions, degree of AI utilization, and organizational characteristics. The results reveal that AI adoption remains limited and highly concentrated in marketing-related functions, particularly in content generation and social media automation, with minimal implementation in finance, logistics, and human resource management. The study also identifies the main barriers hindering AI adoption. The lack of qualified professionals and the unavailability of structured databases emerged as the most critical obstacles, followed by limited financial capacity. One-way ANOVA and Kruskal–Wallis tests confirmed significant differences in AI adoption levels based on company size and sector, especially in areas such as inventory optimization and design prototyping. These findings highlight a gap between the potential of AI technologies and their real-world implementation in Ecuadorian MSMEs. They underscore the need for targeted strategies focused on workforce training, digital infrastructure development, and institutional support to promote broader and more effective AI integration.

The volume of scientific publications has increased exponentially over the past decades across virtually all academic disciplines. In this landscape of information overload, objective criteria are needed to identify high-impact research. Citation counts have traditionally served as a primary indicator of scientific relevance; however, questions remain as to whether they truly reflect the intrinsic quality of a publication. This study investigates the relationship between citation frequency and a wide range of editorial, authorship, and contextual variables. A dataset of 339,609 articles indexed in Scopus was analyzed, retrieved using the search query TITLE-ABS-KEY (management) AND LIMIT-TO (subarea, “Busi”). The research employed a descriptive analysis followed by two predictive modeling approaches: a Random Forest algorithm to assess variable importance, and a binary logistic regression to estimate the probability of a paper being cited. Results indicate that factors such as journal quartile, country of affiliation, number of authors, open access availability, and keyword usage significantly influence citation outcomes. The Random Forest model explained 94.9% of the variance, while the logistic model achieved an AUC of 0.669, allowing the formulation of a predictive citation equation. Findings suggest that multiple determinants beyond content quality drive citation behavior, and that citation probability can be predicted with reasonable accuracy, though inherent model limitations must be acknowledged.

This study analyzes the temporal evolution of citations received by academic articles in the field of micro, small, and medium-sized enterprises (SMEs) and internationalization processes, with the aim of identifying patterns of growth and decline in scientific visibility. Based on a dataset of 1936 articles retrieved from Scopus, we constructed an article–year panel that enabled the application of multiple statistical approaches. Discrete-time survival models showed that the annual probability of receiving at least one citation is initially low, increases slightly until the fifth year, and then declines progressively thereafter. Negative binomial regression confirmed significant growth during the first five years, followed by a slowdown. Kaplan–Meier estimations reinforced this finding by showing that the cumulative proportion of articles receiving their first citation within a decade remains limited. These results confirm that citation dynamics are nonlinear and subject to early obsolescence, with most visibility concentrated in the short term. Importantly, this temporal bias in indexing and evaluation systems disproportionately favors recent publications while undervaluing older but still influential research. Such structural bias has profound implications for visibility and equity in scholarly communication, especially for disciplines and regions where citation cycles are longer. The findings thus validate the study’s propositions: first, that citation growth slows significantly after the fifth year, and second, that this slowdown represents a structural bias that amplifies inequities in research evaluation.

The purpose of this research work is to illustrate that using the logic of theory of constraints (TOC) concerning the Throughput is adequate in management decisions to improve performance in the context of small and medium enterprises. The TOC approach assumes the use of Throughput as an indicator of performance, and in this paper the method of chain substitution and successive approximation is proposed to analyze this indicator. After the implementation of the Throughput per hour analysis, the owner-manager changed her perspective about what her priorities should be in the exercise of the administrative process. The identification of the restriction of what prevented him from obtaining more money allowed him to make the decisions or courses of action that maximized the Throughput per hour, thus improving his performance as owner-manager, the performance of his enterprise and the economic situation of his employees. The originality of this article consists in the application of TOC approach through the design of the indicator Throughput per hour to analyse the performance of an entrepreneur and the application of the chain substitution and successive approximation method for the study of the restrictions that inhibit it.

The study provides a discussion about the leadership style of small businesses owner-managers at Santo Domingo de los Tsáchilas, Ecuador, supported on contingency theory of leadership. And adaptation of Hershey and Blanchard questionnaire was made in order to make it clear to the target population. A total of 175 owner-managers of small businesses and 1218 employees comprised the respondents of the study. The perception of leadership style of owner-managers and employees, style range, and style adaptability and perception of the maturity degree of employees were the variables involved in the hypotheses raised. In spite of totally or partially proving the hypothesis raised, findings of the study revealed mixed support to the theory which impedes giving clear recommendations about how to behave in certain organizational situations in the context of small Ecuadorian businesses. Results of the study caution owner-managers of small businesses to consider carefully the way they behave in certain situation to the enrichment of employee motivation toward work with the purpose to obtain a better performance of their enterprises; but should avoid assuming that there are more or less appropriate leader behaviors based on the results of this research. The study constitutes a starting point to focus the research related to leadership style in small companies, considering the preponderance of this type of business in the economy of Ecuador.

Resumen La concepción ambiental para los recursos construidos de valor patrimonial permitió adaptar e integrar teorías, modelos y sistemas empleados universalmente en la gestión ambiental, urbana y patrimonial, en un modelo conceptual para la gestión ambiental urbana de los recursos construidos de valor patrimonial. Derivado este, se concibió y desarrolló una metodología que incorpora los enfoques ecosistémicos, estratégicos y participativos, así como describe un proceso que transita por tres etapas: valoración-planificación, conservación-implementación y monitoreo-control y un conjunto de herramientas que contribuyó a conservarlos, y enriquecer dicho ambiente de una forma equilibrada, territorialmente factible y equitativa para la sociedad. Su aplicación en tres pueblos cubanos, posibilitó generar cambios e impactos favorables en el desarrollo económico local y en la calidad de su ambiente, propiciando mejores condiciones de vida; a través de ofertas de servicios, empleos, espacios sociales de uso colectivo, entre otros componentes que han ayudado al desarrollo local respectivamente. Es por ello que el objetivo del presente artículo es socializar los resultados de las contribuciones de la gestión ambiental urbana, desde el tratamiento como recurso ambiental a los valores del patrimonio construido, mediante un proceso metodológico que ayuda a su conservación y al desarrollo local.

Purpose: The objective of the present research is to design and apply a methodology to evaluate the logistics system in the transportation process in a base vehicle fleet, which contributes to decrease the costs of distribution and to increase the performance of the logistics system of the organization.Design/methodology: The proposal of a holistic technology for the management of this process is carried out, which integrates indicators and tools that improve control and decision-making activities in this area.Findings: The application of the procedure developed in the selected organization contributed to the identification of deficiencies related to the availability of the equipment and the needs of the clients, the low technical availability of the automotive plant, the low utilization of the capacity of the freight vehicles, the absence of a plan of measures to diminish the empty routes of the transport and the overconsumption of fuel due to the accomplishment of extra trips. Aspects that contributed to the redesign of some of the main functions of physical distribution such as itinerary planning, selection of means of transport and analysis of operating indicators, aspects that favored the optimization of the number of trips and, consequently, the adequate use of the equipment and the loads to be transported, observing a saving of 15% in the fuel consumption per load transported.Originality: The originality of the present research lies in the combination of different theories and techniques that contribute from a holistic approach to the logistics evaluation of the transportation process, facilitating the optimization of transportation requirements, its operation and maintenance.

Customer satisfaction (CS) is a cornerstone of competitiveness in the hospitality sector, particularly in restaurants, where service interactions are highly sensory and time-sensitive. Traditional measurement instruments, such as SERVQUAL, SERVPERF, and the American Customer Satisfaction Index, provide valuable diagnostic insights but remain limited by recall bias, social desirability, and delayed feedback. Advances in deep learning now enable non-intrusive, real-time monitoring of customer experience. This study evaluates the feasibility of using a convolutional neural network (CNN) to automatically classify customer satisfaction based on facial expressions captured at the point of payment in a restaurant. From an initial dataset of over 5000 images, 2969 were validated and labeled through a binary self-report mechanism. The CNN, implemented with transfer learning (MobileNetV2), achieved robust performance, with 93.5% accuracy, 92.8% recall, 91.0% F1-score, and an area under the ROC curve of 0.93. Comparative benchmarks with Support Vector Machine and Random Forest classifiers confirmed the superiority of the CNN across all metrics. The findings highlight CNNs as reliable and scalable tools for continuous CS monitoring, complementing rather than replacing classical survey-based approaches. By integrating implicit, real-time signals with traditional instruments, restaurants can strengthen decision-making, enhance service quality, and co-create personalized experiences while addressing challenges of explainability, external validity, and data ethics.

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South.