The influence of ISO 9001 certification on the productivity of the Ecuadorian manufacturing industry

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Type of the article: Research Article

Abstract
Today, manufacturing companies seek tools that enable them to remain competitive in an increasingly demanding global environment, with quality management systems being among the most widely adopted. Despite their broad implementation, empirical evidence regarding their benefits remains inconclusive. Evaluating productivity indicators in certified manufacturing firms is essential to identifying the variables that most influence operational and financial efficiency in this sector. This paper aims to determine the effect of ISO certification on productivity indicators by applying a multivariate discriminant analysis model to a sample of industrial firms with five consecutive years of certification during the 2019–2023 period. The results show that only three indicators – operating income relative to value added, net income relative to value added, and value added relative to working capital – exhibit statistically significant average improvements, associated with increased operational efficiency and value generation. The operating income relative to value added indicator stands out as the variable with the greatest discriminant power, suggesting that ISO 9001 certification positively influences operational productivity. However, the findings also reveal high variability, indicating that the certification’s impact is not homogeneous and depends on both internal and external organizational factors. This study provides valuable empirical evidence in the Ecuadorian context, being the first to assess this relationship using discriminant analysis and contributing to the understanding of quality management system effectiveness in emerging economies.

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    • Table 1. Sample refinement
    • Table 2. Productivity and performance indicators of companies
    • Table 3. Shapiro-Wilk test
    • Table 4. Box’s M test
    • Table 5. Wilks’ Lambda test
    • Table 6. Variation in indicator coefficients
    • Table 7. Classification results
    • Table 8. Means of productivity indicators
    • Conceptualization
      Ivan Rueda
    • Formal Analysis
      Ivan Rueda
    • Funding acquisition
      Ivan Rueda
    • Resources
      Ivan Rueda
    • Writing – original draft
      Ivan Rueda, Grace Tamayo
    • Writing – review & editing
      Ivan Rueda, Byron Acosta, Jean Tamayo
    • Investigation
      Grace Tamayo
    • Project administration
      Grace Tamayo
    • Supervision
      Grace Tamayo
    • Data curation
      Byron Acosta
    • Methodology
      Byron Acosta, Jean Tamayo
    • Software
      Byron Acosta
    • Validation
      Byron Acosta, Jean Tamayo
    • Visualization
      Jean Tamayo