Determinants of Digital Payment Adoption in Local Government Tax Systems

Authors:
Ade Bagus Rianto Wicaksono, Samel W. Ririhena, Alexander Phuk Tjilen, Hussein Nayef Nabulsi, Mohamad Mokdad

Addresses:
Faculty of Social and Political Science, Musamus University, Merauke, South Papua, Indonesia. Department of Business Administration, Lebanese University and AUCE, Beirut, Lebanon. Department of Public Policies, University of Zaragoza, Zaragoza, Aragon, Spain.

Abstract:

This study analyses the digitalisation of local tax governance in Merauke Regency, South Papua Province, from manual cash-based collections to non-cash payments. As local governments in Eastern Indonesia focus on improving revenue transparency while minimising fiscal leakage, the use of digital payment systems such as QRIS and virtual accounts is an important policy target. The study uses a single dataset of 444 cases prepared by taxpayers within the community, including businesses in hospitality, food and beverage, retail, and resident property owners. The analysis is quantitative and based on adoption factors, including constructs such as perceived ease of use, infrastructure availability, and confidence in local government systems. Structural equation modelling software was used to analyse data for response manipulation and hypothesis validation. The results show that, although policy-driven mandates greatly influence the use of non-cash systems in Merauke, actual use is significantly influenced by network stability and the user interface of banks' regional app. The research offers a roadmap for policymakers seeking to connect digital ambition with on-the-ground reality in developing frontier regions. By utilising this dataset, the study provides empirical evidence on rural-urban dynamics in South Papua regarding financial behaviour. It contributes to a broader debate on e-government implementation in developing economies.

Keywords: Digital Governance; Tax Compliance; Non-Cash Payments; Financial Inclusion; Virtual Accounts; Network Stability; Structural Equation Modelling (SEM); Frontier Regions.

Received on: 22/02/2025, Revised on: 01/05/2025, Accepted on: 24/06/2025, Published on: 03/01/2026

DOI: 10.64091/ATITP.2026.000268

AVE Trends in Intelligent Technoprise Letters, 2026 Vol. 3 No. 1 , Pages: 28–35

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