Personal and reliability factors affecting adoption and utilization of e-government: An effect of intention to use

  • Received October 5, 2021;
    Accepted May 6, 2022;
    Published May 19, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.20(2).2022.23
  • Article Info
    Volume 20 2022, Issue #2, pp. 281-290
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This work is licensed under a Creative Commons Attribution 4.0 International License

This study aims to examine and prove the effect of personal and reliability factors on both the adoption as well as the utilization of e-government indirectly through the intention to use. The proposed model uses various theories, such as technology acceptance, diffusion of innovation, and unified theory of acceptance and use of technologies. It incorporates contracts from the e-government adoption and usage model to explore and understand the factors that drive different types of e-technology adoption and use. Employing purposive sample, the paper collected around 158 respondents that were used to support this study. According to the findings, there are 103 government employees in the sample, 36 general public, and 19 businessmen in the Provincial Government of DKI Jakarta (Indonesia). After the questionnaire’s reliability and validity were rigorously evaluated, the data were analyzed using the Structural Equation Modeling (SEM) technique. The results indicate that personal factors and perceived trust significantly affect the adoption and the utilization of e-government. In addition, reliability variables highly influence intention to use. Moreover, intention to use does not mediate the effect of personal factors, reliability factors, and e-government adoption use. This study is expected to be material for consideration and evaluation of the quality of ICTs-based public information for government officials and staff.

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    • Figure 1. Research model
    • Table 1. Variable measurement
    • Table 2. Descriptive statistics
    • Table 3. Result of validity and reliability
    • Table 4. Fornell-Larcker criterion of discriminant validity
    • Table 5. Bootstrapping and the PLS path algorithm
    • Table 6. Indirect effect
    • Conceptualization
      Rachmawati, Khoirul Aswar
    • Data curation
      Rachmawati, Khoirul Aswar, Mahendro Sumardjo
    • Software
      Rachmawati, Khoirul Aswar, Meilda Wiguna, Eka Hariyani
    • Writing – original draft
      Rachmawati
    • Formal Analysis
      Khoirul Aswar
    • Methodology
      Khoirul Aswar, Mahendro Sumardjo
    • Supervision
      Khoirul Aswar
    • Validation
      Khoirul Aswar, Meilda Wiguna, Eka Hariyani
    • Writing – review & editing
      Khoirul Aswar
    • Project administration
      Mahendro Sumardjo
    • Resources
      Mahendro Sumardjo, Eka Hariyani
    • Funding acquisition
      Meilda Wiguna
    • Investigation
      Meilda Wiguna, Eka Hariyani