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.

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.

Artificial intelligence (AI) is increasingly positioned as an enabler of strategic renewal and competitiveness for small and medium-sized enterprises (SMEs) in emerging economies. However, its adoption remains limited and uneven, constrained by shortages of skilled talent, weak data infrastructures, and financial barriers. This study examines Ecuadorian SMEs as a representative case within this broader context, analyzing survey data from 385 firms to diagnose AI adoption patterns and validate a structural model linking AI adoption, dynamic capabilities, and strategic innovation performance. Results from Partial Least Squares Structural Equation Modeling (PLS-SEM) confirm that AI adoption enhances innovation and competitiveness both directly and indirectly through dynamic capabilities, specifically firms’ abilities to sense opportunities, seize them through innovation, and reconfigure resources. The model explains 41% of the variance in strategic innovation performance, providing robust empirical support for the proposed AI-Driven Dynamic Capabilities Framework for Strategic Innovation and Competitiveness. The study clarifies how perceptual and contextual enablers of adoption (TAM/TOE) interact with capability-building mechanisms (RBV/DCT), offering a more integrated understanding of how SMEs assimilate AI under resource constraints. These findings demonstrate how SMEs translate early adoption into strategic advantage under conditions of uncertainty. The study also offers actionable guidance by showing that the most effective interventions for SMEs focus on strengthening foundational data and organizational capabilities rather than promoting complex AI systems beyond current readiness levels.

The phenomenon of job satisfaction generates high costs for organizations, as it impacts on the processes of selection, training, and motivation of their human resources, while affecting the productivity and quality of organizations, even impacting on the loyalty of costumers. For this reason, forecasting or controlling the behavior of the staff turnover is of great importance for a company. However, predicting the behavior of turnover is an almost impossible intention due to the number of variables that condition this behavior. The objective of this research was to try to identify, through the use of neural network analysis, which internal variables of the organization, of an objective nature, of a demographic nature and associated with their human resources, showed a relationship or incidence on the employee turnover. For this purpose, the databases were analyzed with the turnover behavior of personnel in business organizations with different characteristics. The analysis through neural networks allowed to establish a significant relationship between variables such as average income, school level, and age; likewise, no significant differences were found in other variables such as the type of sector, years of experience in the sector, years of work in the position or years of work, the hierarchical position occupied in the organization, and the number of dependents.

The impact of self-confidence, creativity and vision on leadership performance: perceptions at Ecuadorian SMEs owner - managers

This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation.