Digital budget transparency and perceived budget credibility: Technocratic and normative mechanisms in Indonesian local governments

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Type of the article: Research Article

Abstract
Budget credibility is a critical indicator of fiscal reliability in local governments, yet the mechanisms through which digital transparency enhances credibility remain poorly understood. This study examines how digital budget transparency influences perceived budget credibility in Indonesian regency and city governments, using performance information (technocratic mechanism) and procedural justice (normative mechanism) as two parallel mediating pathways. The technocratic mechanism reflects the instrumental application of budget data in planning and decision-making, while the normative mechanism reflects perceptions of fairness and legitimacy in budgeting processes. Using partial least squares structural equation modeling with data from 362 public officials (specifically heads of planning agencies, budget officials, and financial managers) across 33 regencies and city governments in North Sumatera, Indonesia, the study tests these dual pathways. Results show that digital budget transparency has only a modest direct effect on perceived budget credibility (β = 0.118), but exerts far greater influence through mediation. Procedural justice proved to be the primary mediating mechanism (indirect effect β = 0.081), with the use of performance information serving as a secondary pathway (β = 0.035). Multi-group analysis confirms that these relationships hold across both regency and city governments. The findings suggest that digital transparency by itself will not improve budget credibility. Its influence hinges on whether disclosed information actually gets used in decision-making and whether budgeting processes are perceived as fair. Reforms should, therefore, target both information usability and procedural legitimacy if they are to strengthen fiscal discipline in decentralized governance systems.

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    • Figure 1. Measurement model
    • Table 1. Respondent characteristics
    • Table 2. Descriptive statistics
    • Table 3. Measurement model evaluation
    • Table 4. Discriminant validity (HTMT and Fornell–Larcker criterion)
    • Table 5. Path coefficient results
    • Table 6. Cross-validated redundancy (Q²) and adjusted R²
    • Table 7. MICOM (measurement invariance assessment) results
    • Table 8. Multi-group analysis (MGA) results
    • Conceptualization
      Renny Maisyarah
    • Formal Analysis
      Renny Maisyarah
    • Investigation
      Renny Maisyarah
    • Methodology
      Renny Maisyarah
    • Validation
      Renny Maisyarah
    • Writing – original draft
      Renny Maisyarah
    • Writing – review & editing
      Renny Maisyarah