The impact of digital platforms in tax administration services on local government tax revenues: evidence from Indonesia
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DOIhttp://dx.doi.org/10.21511/pmf.13(2).2024.16
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Article InfoVolume 13 2024, Issue #2, pp. 195-203
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Creative Commons Attribution 4.0 International License
In Indonesia, digital platforms in tax administration services have been implemented for more than a decade. This study aims to investigate whether digital platforms for motor vehicle tax administration services can increase local government tax revenues. Then, it is continued by testing the moderating role of motor vehicle tax revenue targets and online service information. Data were collected from the Unit Penerimaan Pendapatan Daerah (UPPD) Central Java – Indonesia. Observations focused on motor vehicle tax services carried out during the 2018–2022 period in 37 district and city UPPDs. The analysis uses GLS regression, which was developed with modeling regression analysis (MRA). The study results show that implementation of digital platforms in motor vehicle tax administration services can increase local government tax revenues. This relationship will be further strengthened if there are online information services, both circular and standby. Further investigation results revealed that relying on tax revenue targets to strengthen the relationship between digital platforms in tax administration services and local government tax revenues is not viable.
Acknowledgment
This study is supported by the Direktur Riset, Teknologi dan Pengabdian Masyarakat (DRTPM) based on decree number 108/E5/PG.02.00.PL/2024 and contract number 007/LL6/PB/AL.04/2024, 196.43/A.3-III/LRI/VI/2024.
- Keywords
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JEL Classification (Paper profile tab)H21, H25, H26, H27
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References39
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Tables7
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Figures1
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- Figure 1. Research framework
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- Table 1. Percentage of users based on type of motorized vehicle
- Table 2. Definition and measurement of variables
- Table 3. Variables tested in each model
- Table 4. Descriptive statistics of variables
- Table 5. Pearson correlation matrix for all variables
- Table 6. GLS regression with the random impacts analysis for vehicle performance
- Table 7. Robustness testing
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