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Abstract
This study aims to examine the influence of information systems, human resource quality, and economic capability on corruption levels in 10 ASEAN countries from 2010 to 2020. This study uses secondary panel data regarding the E-Government Development Index (HDI), Human Development Index (HDI), GDP per capita (PPP), and Corruption Perception Index (CPI). The data were analyzed using panel data regression with a Fixed Effects Model (FEM), selected based on the results of the Hausman test. The findings indicate that human resource quality has a significant negative effect on corruption levels. Meanwhile, information systems have a significant effect, indicating that technological progress contributes to reducing corruption, although its effectiveness may depend on institutional factors. On the other hand, economic capability does not show a significant effect on corruption. These results suggest that improving human resources is a crucial strategy in eradicating corruption in ASEAN countries, while technology and economic growth alone may not be sufficient without strong institutional support.
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Copyright (c) 2025 Rapon Yuniar Suhardi, Arief Rahman

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References
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References
Adam, I. O. (2020). Examining E-Government development effects on corruption in Africa: The mediating effects of ICT development and institutional quality. Technology in Society, 61(February), 101245. https://doi.org/10.1016/j.techsoc.2020.101245
Allison, P.D. (2001). Missing data. Thousand Oaks, CA: Sage. Allison
Baltagi, B. H., & Baltagi, B. H. (2008). Econometric analysis of panel data (Vol. 4, pp. 135-145). Chichester: John Wiley & Sons.
Cárdenas, G. C., & González, R. V. (2022). Mapping of clusters about the relationship between e-government and corruption in Mexico. Competitiveness Review, 2001. https://doi.org/10.1108/CR-05-2022-0064
Cressey, D. R. Other people's money. Montclair, NJ: Patterson Smith, pp.1-300. (1953).
Domashova, J., & Politova, A. (2021). The corruption perception index: Analysis of dependence on socio-economic indicators. Procedia Computer Science, 190(2020), 193–203. https://doi.org/10.1016/j.procs.2021.06.024
Gleason, K., Kannan, Y. H., & Rauch, C. (2022). Fraud in startups: what stakeholders need to know. Journal of Financial Crime, 29(4), 1191–1221. https://doi.org/10.1108/JFC-12-2021-0264
Gokturk, İ. E., & Yalcinkaya, H. S. (2020). The investigation of relationship between corruption perception index and gdp in the case of the Balkans. International Journal of Management Economics and Business, 16(4), 910–924. https://doi.org/10.17130/ijmeb.853535
Gujarati, D. N., & Porter, D. C. (2012). Dasar-dasar ekonometrika, edisi 5. Jakarta: Salemba Empat.
Kagias, P., Cheliatsidou, A., Garefalakis, A., Azibi, J., & Sariannidis, N. (2022). The fraud triangle–an alternative approach. Journal of Financial Crime, 29(3), 908–924. https://doi.org/10.1108/JFC-07-2021-0159
Klitgaard, R. (1988). Controlling corruption. University of California Press.
Law no. 31 of 1999 jo. Law no. 20 of 2001 of the Republic of Indonesia.
Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Wiley.
Martitah., Sumarto, S., & Widiyanto. (2021). E-government’s effect on corruption reduction in indonesian local government bureaucracy: A case study in Central Java. Journal of Legal, Ethical and Regulatory Issues, 24(Special Issue 1), 1–12.
Nan, S. (2021). Study on the relationship of grassroots corruption and government expenditure based on panel data. Procedia Computer Science, 199, 1192–1197. https://doi.org/10.1016/j.procs.2022.01.151
Sarabia, M., Crecente, F., del Val, M. T., & Giménez, M. (2020). The human development index (HDI) and the corruption perception index (CPI) 2013-2017: Analysis of social conflict and populism in Europe. Economic Research-Ekonomska Istrazivanja, 33(1), 2943–2955. https://doi.org/10.1080/1331677X.2019.1697721
Sriyana, J. (2014). Metode regresi data panel. Yogyakarta: Ekosiana.
Tickner, P., & Button, M. (2020). Deconstructing the origins of cressey’s fraud triangle. Journal of Financial Crime, 28(3), 722–731. https://doi.org/10.1108/JFC-10-2020-0204
Transparency International (2024). Corruption perceptions index 2024. Retrieved from https://images.transparencycdn.org/images/CPI2024_Map-plus-Index.pdf
UNDP. (2017). Anti-corruption Strategies: Understanding what works, what does'nt and why? Retrieved from https://www.undp.org/publications/anti-corruption-strategies-understanding-what-works-what-doesnt-and-why
Widarjono, A. 2018. Ekonometrika pengantar dan aplikasinya disertai panduan eviews. Yogyakarta: UPP STIM YKPN.
World Bank. (2017). World development report 2017: Governance and the law. The World Bank.
World Bank. (2016). World development report 2016: Digital dividends. World Bank Publications.
Yap, J. B. H., Lee, K. Y., & Skitmore, M. (2020). Analysing the causes of corruption in the Malaysian construction industry. Journal of Engineering, Design and Technology, 18(6), 1823–1847. https://doi.org/10.1108/JEDT-02-2020-0037