Toward greener supply chains: Analysis of the determining factors

  • Received November 28, 2023;
    Accepted December 19, 2023;
    Published December 26, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ee.14(2).2023.09
  • Article Info
    Volume 14 2023, Issue #2, pp. 114-126
  • TO CITE АНОТАЦІЯ
  • Cited by
    2 articles
  • 204 Views
  • 55 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The green supply chain (GSC) has become essential for companies seeking to improve their environmental performance and meet the requirements of sustainable development. This concept is particularly relevant in an era of globalization and growing environmental awareness. The study used a Probit regression method to analyze data collected from Moroccan SMEs. It aimed to examine the impact of different factors, such as economic and energy efficiency, government incentives, stakeholder pressure, managerial age, company size, and profitability, on the adoption of GSC practices. The results showed that economic and energy efficiency, as well as stakeholder pressure, are significant factors positively influencing the adoption of GSCs. When combined with stakeholder pressure, government incentives also have a positive impact. The age of the executive has a negative influence on the adoption of GSC, indicating that younger executives are more likely to adopt these practices. Company size showed no significant impact, while profitability had a positive impact with the adoption of a GSC.

view full abstract hide full abstract
    • Figure 1. Residual normality tests for the two models
    • Table 1. Hypotheses and the variables that represent them
    • Table 2. Descriptive statics
    • Table 3. Ramsey RESET test
    • Table 4. Correlation matrix
    • Table 5. Variance inflation factors for the base model
    • Table 6. Ramsey RESET tests for the two alternative models
    • Table 7. Variance inflation factors for the two alternative models
    • Table 8. Heteroscedasticity test (Breusch-Pagan-Godfrey)
    • Table 9. Probit regression results
    • Conceptualization
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Data curation
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Formal Analysis
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Funding acquisition
      Anass Touil, Khalid Ayad, Nabil El Hamidi
    • Investigation
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Methodology
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Project administration
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Resources
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Software
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Supervision
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Validation
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Visualization
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Writing – original draft
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia
    • Writing – review & editing
      Anass Touil, Khalid Ayad, Nabil El Hamidi, Aziz Babounia