Integrating circular economy, digital economy, and social protection policies to drive green business innovation: Insights from Indonesia’s culinary SMEs

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Global concern over environmental pollution has sparked the adoption of green business practices, which are essential for the sustainability of SMEs through green economy initiatives and renewable resources. This study aims to analyze the integration of circular and digital economy policies along with social protection policies in supporting green business innovation among culinary SMEs. By emphasizing the significance of government roles and innovation capabilities, the paper proposes that policies promoting environmentally friendly practices and social security can enhance sustainable performance in alignment with SDG principles. The study employs a quantitative approach, surveying 200 culinary SMEs in Surabaya City in Indonesia, a city known for its vibrant SME sector and commitment to sustainable practices. The data analysis, conducted using PLS-SEM through SmartPLS 4, reveals that circular economy policies (t-test = 6.503; p-value = 0.000) and social security (t-test = 3.848; p-value = 0.000) significantly enhance green business innovation, while digital economy policies are not significant (t-test = 0.725; p-value = 0.468). Furthermore, green business innovation positively impacts sustainable performance (t-test = 24.418; p-value = 0.000). However, internal innovation capabilities do not moderate the relationship between integrative policies and green business innovation. The findings indicate that government policies significantly influence green business innovation in MSMEs, particularly through circular economy regulations. Regulatory support and incentives are crucial for driving environmental sustainability and enhancing competitiveness. Strengthening digital economy policies through improved technology access and digital literacy will further support green innovation, while investments in internal innovation and human resources are vital for sustainable growth.

Acknowledgment
This study is funded by the Riset Kolaborasi Indonesia (Grant No. B/43869/UN38.III.1/LK.04.00/2024) by LPPM Universitas Negeri Surabaya.

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    • Figure 1. Proposed research model
    • Figure 2. Bootstrapping output
    • Table 1. Respondent characteristics
    • Table 2. Validity, reliability, and multicollinearity testing
    • Table 3. Fornell-Larcker test results
    • Table 4. HTMT test results
    • Table 5. Direct effects and R-square result
    • Table 6. Results of checking the moderating hypotheses
    • Table A1. Variable-indicator description
    • Conceptualization
      Suci Megawati, Herdis Herdiansyah
    • Data curation
      Suci Megawati, Herdis Herdiansyah, Amir Machmud
    • Formal Analysis
      Suci Megawati, Amir Machmud
    • Funding acquisition
      Suci Megawati
    • Investigation
      Suci Megawati, Ernoiz Antriyandarti
    • Methodology
      Suci Megawati, Herdis Herdiansyah
    • Project administration
      Suci Megawati, Sud Sudirman
    • Software
      Suci Megawati, Herdis Herdiansyah
    • Supervision
      Suci Megawati, Herdis Herdiansyah, Sud Sudirman
    • Writing – original draft
      Suci Megawati, Herdis Herdiansyah, Amir Machmud, Ernoiz Antriyandarti, Sud Sudirman
    • Writing – review & editing
      Suci Megawati, Amir Machmud, Ernoiz Antriyandarti
    • Resources
      Amir Machmud, Ernoiz Antriyandarti
    • Visualization
      Amir Machmud, Ernoiz Antriyandarti
    • Validation
      Sud Sudirman