How does adopting sustainable supply chain quality management improve corporate financial performance? A transaction cost perspective

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This article examines the impact of adopting SSCQM on the financial performance of companies, based on transaction theory. The main objective of this study is to assess how reducing transactional costs through SSCQM, or within its framework, can improve the financial performance of Moroccan companies. The methodology is based on quantitative analysis, using an econometric model (GLM-Gamma) to test the association between SSCQM adoption and financial performance. The results of the study show that various SSCQM-related practices positively affect companies’ financial performance. Managing sourcing costs significantly improves profit margins. Contracts focusing on quality and sustainability, and minimizing the risk of non-compliance, also boost financial performance. However, the ability to adapt and respond to regulatory changes shows no significant impact. Optimizing production processes and investing in green technologies are proving to be profitable strategies, with significant improvements in financial performance. Customer engagement and transparency and traceability of operations also have a positive impact. These results suggest that SSCQM practices, such as the adoption of green technologies and transparency policies, are beneficial for companies’ financial performance. The originality of the study lies in its approach, which links transaction theory to sustainable supply chain management, an angle little explored in existing literature. The study confirms that SSCQM is an effective strategy for improving corporate financial health by minimizing transactional costs. It recommends integrating SSCQM into companies’ management strategies to boost their competitiveness, financial performance and sustainability.

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    • Figure 1. Distribution of the dependent variable
    • Figure 2. PP plot
    • Figure 3. Residual distribution
    • Figure 4. Hat-Matrix
    • Table 1. Hypotheses and the associated variables
    • Table 2. Questionnaire response rate
    • Table 3. Results of Confirmatory Factor Analysis (convergent validity, Cronbach’s alpha, reliability composite and AVE)
    • Table 4. Results of Confirmatory Factor Analysis (discriminant validity)
    • Table 5. Heteroskedasticity test: Breusch-Pagan-Godfrey
    • Table 6. Ramsey RESET test for OLS
    • Table 7. Choice of control variables
    • Table 8. Ramsey RESET test after adding control variables
    • Table 9. Comparison of distributions
    • Table 10. Ramsey RESET for GLM regression
    • Table 11. Variance Inflation Factors (VIF) for the GLM-Gamma regression
    • Table 12. Heteroscedasticity test: Breusch-Pagan-Godfrey
    • Table 13. GLM-Gamma regression results
    • Conceptualization
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Data curation
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Formal Analysis
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Funding acquisition
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Investigation
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Methodology
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Project administration
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Resources
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Software
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Supervision
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Validation
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Visualization
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Writing – original draft
      Anass Touil, Aziz Babounia, Nabil El Hamidi
    • Writing – review & editing
      Anass Touil, Aziz Babounia, Nabil El Hamidi