Perceived external R&D purchases and internal R&D expenditures as innovation drivers in large Ecuadorian firms

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Type of the article: Research Article

Abstract
This study examines the relationship between external R&D purchases and internal R&D expenditure with technological and non-technological innovation in large Ecuadorian companies, using internal R&D expenditure as a mediating variable and company age as a moderating variable. A structured survey was administered in 2024 to the CEOs of 380 large companies in Ecuador’s three main business centers: Guayaquil, Quito, and Cuenca. Data were analyzed using PLS-SEM. The results confirmed that internal R&D expenditure is the main driver of reported innovation, with large direct effects on technological (β = 0.545; p < 0.001; f² = 0.434) and non-technological innovation (β = 0.526; p < 0.001; f² = 0.400). External R&D purchases directly affect non-technological innovation (β = 0.178; p < 0.001) and internal R&D expenditure (β = 0.308; p < 0.001; f² = 0.105), with significant indirect effects on both types of innovation (β = 0.168 and 0.162; p < 0.001). The model explains 37.9% of the variance in technological innovation and 37.5% of the variance in non-technological innovation (Q² > 0). Firm age negatively moderates the external R&D purchases–technological innovation relationship (β = −0.141; p = 0.045), a pattern consistent with younger firms relying more on external R&D purchases for technological innovation, although this interpretation requires caution given the moderate significance of the effect. Therefore, internal R&D expenditure relates to reported outcomes of innovation, while external R&D purchases appear to contribute mainly to the development of internal R&D.

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    • Figure 1. Research model
    • Figure 2. Analysis of the proposed model after applying partial least squares
    • Figure 3. Simple slopes analysis
    • Table 1. Convergent validity analysis
    • Table 2. Discriminant validity analysis
    • Table 3. Hypothesis testing
    • Table 4. Mediating effects
    • Table 5. Structural path estimates with industry-sector controls
    • Table 6. PLS-SEM model goodness of fit indicators
    • Table A1. Distribution of participating firms by economic sector and business hub (ISIC classification)
    • Table A2. Distribution of participating firms by years of operation
    • Table B1. Questionnaire
    • Table C1. Results of Harman’s single-factor test for common method bias
    • Table C2. Inner model collinearity statistics (Full collinearity VIF)
    • Conceptualization
      José Luis Román Vásquez, Iván De La Vega
    • Data curation
      José Luis Román Vásquez
    • Formal Analysis
      José Luis Román Vásquez, Iván De La Vega
    • Investigation
      José Luis Román Vásquez
    • Methodology
      José Luis Román Vásquez
    • Project administration
      José Luis Román Vásquez, Iván De La Vega
    • Resources
      José Luis Román Vásquez
    • Software
      José Luis Román Vásquez
    • Visualization
      José Luis Román Vásquez
    • Writing – original draft
      José Luis Román Vásquez
    • Supervision
      Iván De La Vega
    • Writing – review & editing
      Iván De La Vega