Predictive effect of intellectual capital on business innovation in Ecuadorian SMEs

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Type of the article: Research Article

Abstract
In a productive environment characterized by increasing demands for sustainability, traceability, and differentiation, small and medium-sized enterprises (SMEs) play a central role in economic development. This study aims to evaluate the predictive effect of human, structural, and relational capital on product, process, marketing, and organizational innovation in Ecuadorian SMEs. The analysis is based on a structured survey administered to 395 SME managers and owners from the manufacturing, commercial, and service sectors located in the provinces of Tungurahua, Cotopaxi, and Chimborazo (Ecuador) during July and August 2025. Data were analyzed using partial least squares structural equation modeling (PLS-SEM), incorporating bootstrapping with 5,000 resamples and external validation through a holdout approach (80% training and 20% validation). The results reveal that all hypothesized relationships are positive and statistically significant (p < 0.001), with standardized path coefficients ranging from β = 0.20 to β = 0.66. Structural capital has the strongest effect on process innovation (β = 0.60), while relational capital has the strongest effect on organizational innovation (β = 0.54). The model explains a substantial proportion of variance in innovation outcomes, with R² values of 0.47 for product innovation, 0.51 for process innovation, 0.52 for marketing innovation, and 0.44 for organizational innovation. Predictive validation confirms the model’s accuracy, yielding low prediction errors (RMSE = 0.20–0.23; MAE = 0.16–0.18). These findings provide updated empirical evidence on the strategic role of intellectual capital in enhancing innovation performance in Latin American SMEs and highlight the relevance of intangible resources.

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    • Figure 1. Actual and predicted values of innovation constructs using the prediction model
    • Table 1. Indicators of internal reliability and convergent validity of constructs
    • Table 2. HTMT index for assessing discriminant validity
    • Table 3. Variance inflation values (VIF) of the indicators
    • Table 4. Structural model analysis (PLS-SEM bootstrapping)
    • Table 5. Coefficients of determination of innovation dimensions
    • Table 6. Structural coefficients of the training model
    • Table 7. Predicting business innovation
    • Conceptualization
      Myriam Naranjo-Vaca, Esteban David Lopez-Manobanda
    • Data curation
      Myriam Naranjo-Vaca, Esteban David Lopez-Manobanda
    • Formal Analysis
      Myriam Naranjo-Vaca, Esteban David Lopez-Manobanda
    • Funding acquisition
      Myriam Naranjo-Vaca, Esteban David Lopez-Manobanda
    • Writing – original draft
      Myriam Naranjo-Vaca, Miriam Salas-Salazar, Marco Gavilanes-Sagnay, Esteban David Lopez-Manobanda
    • Writing – review & editing
      Myriam Naranjo-Vaca, Miriam Salas-Salazar, Marco Gavilanes-Sagnay, Esteban David Lopez-Manobanda
    • Investigation
      Miriam Salas-Salazar
    • Methodology
      Miriam Salas-Salazar
    • Project administration
      Miriam Salas-Salazar
    • Resources
      Miriam Salas-Salazar
    • Software
      Marco Gavilanes-Sagnay
    • Supervision
      Marco Gavilanes-Sagnay
    • Validation
      Marco Gavilanes-Sagnay
    • Visualization
      Marco Gavilanes-Sagnay