Determinants of village SDG achievement in Kuningan Regency, Indonesia: The mediating role of village fund optimization

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Type of the article: Research Article

Abstract
Fiscal decentralization has transferred substantial financial authority to Indonesian villages, yet whether these resources translate into measurable development progress continues to depend on the quality of local governance and institutional capacity. This study examines the determinants of perceived village-level Sustainable Development Goal (SDG) achievement by testing the mediating role of village fund optimization in the relationships among collaborative governance, human resource capacity, and local government support.
Using a quantitative, cross-sectional design, primary data were collected between September 2025 and February 2026 from 100 village officials in Kuningan Regency, West Java, Indonesia, and analyzed using partial least squares structural equation modeling (PLS-SEM), with hypothesis decisions based on 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. Collaborative governance (β = 0.650) and local government support (β = 0.451) significantly predict village fund optimization (adjusted R2 = 0.675). The direct effect of human resource capacity (β = 0.204) is not statistically distinguishable from zero. Village fund optimization is also significantly associated with perceived village SDG achievement (β = 0.762; adjusted R2 = 0.576) and mediates the relationship for collaborative governance (β = 0.495) and local government support (β = 0.343), but not for human resource capacity (β = 0.155).
These findings indicate that how village funds are managed, rather than the volume of the transfer alone, is closely associated with village officials' perceptions of progress toward village SDGs, and that this effectiveness is shaped primarily by collaborative governance and local government support rather than by human resource capacity as measured here.

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    • Figure 1. Standardized loading factor (SLF) model
    • Figure A1. Bootstrapped structural model (t-statistics)
    • Table 1. Variables
    • Table 2. Composite reliability and average variance extracted
    • Table 3. Discriminant validity (cross-loadings)
    • Table 4. Heterotrait-monotrait ratio (HTMT) of correlations
    • Table 5. Confirmatory tetrad analysis (CTA-PLS) summary
    • Table 6. Results of the direct effect testing
    • Table 7. Indirect effect testing
    • Table 8. Goodness of fit
    • Table 9. Effect size (f2)
    • Table A1. Descriptive statistics of research variables
    • Table A2. Model fit summary
    • Table A3. Fornell–Larcker criterion
    • Table A4. Inner VIF values
    • Table A5. Bootstrapping detail: Total effects (direct and indirect)
    • Table A6. Bootstrapping detail: Outer loadings
    • Table A7. Bootstrapping detail: Outer weights
    • Table A8. Village SDGs achievement (Z) formative re-specification: Robustness check
    • Conceptualization
      Neni Nurhayati
    • Funding acquisition
      Neni Nurhayati
    • Methodology
      Neni Nurhayati
    • Writing – review & editing
      Neni Nurhayati, Dadang Suhendar
    • Data curation
      Iman Teguh
    • Formal Analysis
      Iman Teguh
    • Investigation
      Iman Teguh
    • Software
      Iman Teguh
    • Writing – original draft
      Iman Teguh
    • Project administration
      Dadang Suhendar
    • Resources
      Dadang Suhendar
    • Validation
      Dadang Suhendar
    • Visualization
      Dadang Suhendar