Unveiling the drivers of digital governance adoption in public administration

  • Received September 13, 2023;
    Accepted November 2, 2023;
    Published November 23, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.21(4).2023.35
  • Article Info
    Volume 21 2023, Issue #4, pp. 454-467
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    2 articles
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This work is licensed under a Creative Commons Attribution 4.0 International License

The purpose of this paper is to investigate the factors, both internal and external, that impact the adoption of digital governance in public administration. The quantitative data were collected through online questionnaires from 556 public servants, all of whom were enrolled in a Master of Public Administration program, representing a variety of public organizations, in a non-random way. The study draws from a comprehensive literature review and leverages structural equation modeling (SEM) analysis to derive empirical insights. The empirical analysis revealed positive relationships between digital governance, service quality, safety, trust, and transparency within public services. Contrary to previous results, internal factors such as leadership, organizational culture, and skillsets do not exhibit significant impacts. Overall, the study supports the idea that improving the quality of digital services and embracing innovative technologies are key drivers of digital governance in public administration, leading to increased transparency and public trust. These findings can guide policymakers and administrators in implementing effective digital governance strategies tailored to the specific context of each public organization.

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    • Figure 1. Conceptual model
    • Figure 2. Results of the structural equation modeling showing statistically significant paths
    • Table 1. Research hypotheses
    • Table 2. Sample demographics (n = 556)
    • Table 3. Factor loadings, reliability, and convergent validity
    • Table 4. Evaluation of model’s goodness-of-fit
    • Table 5. Square roots of AVE and correlations
    • Table 6. Path coefficients (standardized regression coefficients)
    • Conceptualization
      Panagiota Xanthopoulou
    • Data curation
      Panagiota Xanthopoulou
    • Formal Analysis
      Panagiota Xanthopoulou, Giorgos Avlogiaris
    • Investigation
      Panagiota Xanthopoulou
    • Methodology
      Panagiota Xanthopoulou, Giorgos Avlogiaris
    • Project administration
      Panagiota Xanthopoulou, Ioannis Antoniadis
    • Resources
      Panagiota Xanthopoulou
    • Supervision
      Panagiota Xanthopoulou, Ioannis Antoniadis
    • Validation
      Panagiota Xanthopoulou, Ioannis Antoniadis, Giorgos Avlogiaris
    • Visualization
      Panagiota Xanthopoulou, Ioannis Antoniadis, Giorgos Avlogiaris
    • Writing – original draft
      Panagiota Xanthopoulou
    • Funding acquisition
      Ioannis Antoniadis
    • Writing – review & editing
      Ioannis Antoniadis, Giorgos Avlogiaris
    • Software
      Giorgos Avlogiaris