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.

Credit cards play a central role in household financial behavior by combining payment and short-term financing functions shaped by socioeconomic, cognitive, and attitudinal factors. This study examines the determinants of credit card use and repayment behavior in Ecuador, focusing on purchasing power, financial literacy, and institutional trust. A quantitative, cross-sectional, and explanatory design was applied to a probabilistic sample of 550 credit card users from Quito and Santo Domingo. Multivariate analyses and Partial Least Squares Structural Equation Modeling (PLS-SEM)—including formative and hierarchical constructs—were used to validate the proposed behavioral framework. The results show that higher income is associated with more responsible repayment, while financial literacy and trust mediate this relationship through cognitive and attitudinal mechanisms. Moderate R² values and small-to-moderate f² effect sizes align with patterns observed in other Latin American credit markets. Behavioral differences also emerge across age, gender, and household composition, underscoring the heterogeneity of financial capability in the region. The findings demonstrate that responsible credit card indebtedness depends not only on economic capacity but also on financial knowledge and institutional trust, offering practical implications for financial inclusion policies and targeted education programs in emerging economies.

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 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.

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.

Introduction: Patient satisfaction is a key indicator of healthcare quality, particularly in dental services, where both clinical and non-clinical factors shape perceptions of care. This study aims to investigate the impact of demographic variables—specifically age, gender, and purchasing power—on patient satisfaction with dental services in Quito, Ecuador. Methods: A sample of 485 patients was surveyed using a modified SERVPERF model combined with an attribute matrix. The instrument captured the perceived importance and satisfaction associated with 14 service attributes. Stratified random sampling was applied based on age, gender, and purchasing power. Satisfaction indices were calculated, and differences among groups were analyzed using the chi-square test. Results: Age was the only variable with a statistically significant impact on patient satisfaction (p < 0.05), whereas gender and purchasing power showed no significant differences. Professionalism, quality, and empathy were rated as the most essential attributes across all groups. Interestingly, attributes considered less important—such as waiting time and payment methods—received higher satisfaction scores. The attribute matrix revealed that critical attributes (important but underperforming) differed mainly by age group. The highest satisfaction scores were observed among patients under 25, while the lowest were reported among those aged 40 to 60 and in lower-income groups. Discussion: Findings highlight the relevance of age-sensitive strategies in dental care and underscore a potential misalignment between what patients value most and what they rate most positively. The combined SERVPERF and attribute matrix approach proved effective for generating actionable insights. Further research in diverse contexts is needed to validate these findings.