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.

This study develops a systemic framework to conceptualize digital organizational resilience in micro, small, and medium-sized enterprises (MSMEs) as an emergent property of entangled socio-technical systems. Building on theories of distributed cognition, sociomateriality, and resilience engineering, this paper argues that resilience does not reside in isolated elements—such as leadership, technologies, or procedures—but in their dynamic interplay. Four interdependent dimensions—human, technological, organizational, and institutional—are identified as constitutive of resilience capacities. The research design is conceptual and exploratory in nature. Two theory-driven conceptual statements are formulated: first, that natural language mediation in human–machine interaction enhances coordination and adaptability; and second, that distributed cognition and prototyping practices strengthen collective problem-solving and adaptive capacity. These conceptual statements are not statistically tested but serve as conceptual anchors for the model and as guiding directions for future empirical studies. Empirical illustrations from Ecuadorian MSMEs ground the framework in practice. The evidence highlights three insights: (1) structural fragility, as micro and small firms dominate the economy but face high mortality and financial vulnerability; (2) uneven digitalization, with limited adoption of BPM, ERP, and AI due to skill and resource constraints; and (3) disproportionate gains from modest interventions, such as optimization models or collaborative prototyping. This study contributes to organizational theory by positioning MSMEs as socio-technical ecosystems, providing a conceptual foundation for future empirical validation.

This study examines how green talent management (GTM) practices foster sustainable supply chains in micro, small, and medium-sized enterprises (MSMEs) in Quito, Ecuador. It analyzes how sustainable leadership, green organizational culture, and sustainability-oriented training influence employees’ pro-environmental motivation, organizational commitment, and sustainability attitudes, which in turn mediate the adoption of green logistics practices, supply chain efficiency, and organizational resilience. A quantitative design was employed, using survey data from 280 MSMEs analyzed through structural equation modeling. The findings demonstrate that GTM enhances employees’ motivation, commitment, and sustainability attitudes, which act as the primary behavioral mechanisms translating managerial practices into sustainability outcomes. Theoretically, the study integrates Green HRM and supply chain research with multiple organizational behavior theories, including Social Exchange Theory, the AMO model, the Theory of Planned Behavior, and the Resource-Based View. Empirically, it contributes novel evidence from Ecuadorian MSMEs, a context often underexplored in sustainability research. Practically, the study highlights leadership, culture, and training as strategic levers for building greener, more efficient, and more resilient supply chains. The results offer actionable recommendations for MSME managers and policymakers in Ecuador, highlighting the importance of investing in people as the foundation of sustainable competitiveness.

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty.

(Recibido: 2015/01/29 - Aceptado: 2015/03/25) En la presente investigación se propone un modelo matemático que permite enfocar el proceso de creación de equipos de desarrollo de software como un problema multi objetivo discreto. Los objetivos considerados son el nivel de competencia y el nivel de utilización de los profesionales en el equipo. Dada la complejidad del espacio de búsqueda del problema, se propone la aplicación de un método de optimización aproximado. Específicamente se seleccionó el algoritmo genético NSGA-II basado en el concepto de Dominancia de Pareto. Este método fue aplicado en seis escenarios diferentes con el objetivo de analizar la calidad de soluciones que obtiene. En general se puede afirmar que el método es eficiente y obtiene soluciones (asignaciones) de alta calidad.

Organizational decision-making frequently occurs in environments characterized by uncertainty, heterogeneous information, and qualitative expert judgment, which classical quantitative models cannot adequately capture. This article develops a mathematically grounded, fuzzy-logic–based theoretical decision architecture to enhance robustness, interpretability, and scalability in complex organizational systems. Building on an integrative synthesis of recent advances in fuzzy MCDM methods, AI-enhanced fuzzy inference, and sustainability-oriented performance modeling, three dominant research clusters are identified and consolidated into a unified multilayer framework. The proposed model is structured around four interdependent components—contextual conditions, technical fuzzy mechanisms, moderating structures, and observable outcomes—linked through an explicit feedback process formalized via composite fuzzy operators. Rather than introducing new algorithms, the framework specifies how established fuzzy components are functionally differentiated, constrained, and coordinated at the system level. It explains how expert-judgment quality, membership-function calibration, inference engines, interoperability with enterprise systems, and validation and traceability mechanisms jointly determine decision stability and transparency. The model further establishes key formal properties, including monotonicity, boundedness, adaptive stability, and traceable reproducibility, ensuring internal coherence and well-behaved system dynamics. By addressing the lack of unified, reproducible, and scalable architectures in the fuzzy decision-making literature, this study provides a generalizable theoretical foundation for explainable, sustainability-aligned intelligent decision systems under organizational uncertainty.