Comparative Analysis of The Impact of Big Data on Corruption in Selected Developing and Developed Countries

Adewale Joel Adebisi and Cherif Guermat
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Adewale Joel Adebisi: The University of the West of England, United Kingdom
Cherif Guermat: The University of the West of England, United Kingdom

European Journal of Information Technologies and Computer Science, 2022, vol. 2, issue 3, 1-9

Abstract: Big data analytics adoption is gaining momentum amongst both policy makers and business leaders. Indeed many sectors in the economy and society have been adopting recent information technology innovations to deal with issues that were previously impossible to tackle. One of these issues is corruption. This paper considers the impact of big data on corruption in developed and developing countries. Specifically, we investigate the effect of internet usage on corruption prevention and early detection, and examine the impact of investment in data-driven technology on corruption prevention and early detection. Finally, we evaluate the impact of mobile data subscription on total corruption. We use secondary data covering 1995 to 2020 for three low FinTech developing countries and three mature FinTech developed countries. Random effect regression models are employed to estimate and test for the impact of big data on corruption in developing and developed countries. We find that internet usage and fixed telephone subscription have a significant negative impact on corruption in developing countries. Investment in technology, mobile phone users subscription and gross domestic product also have a significant negative impact on corruption in developing countries. However, inflation has no significant effect on corruption in developing countries. In contrast, we find no significant impact of big data on corruption within developed countries. Big data adoption, therefore, seems to hinder corruption in developing countries, but not in developed countries.

Keywords: Big data; corruption; internet subscription; phone subscription; technology (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:comput:v:2:y:2022:i:3:id:10064

DOI: 10.24018/compute.2022.2.3.64

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