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Revisiting Corruption Mathematical Models in the Fourth Industrial Revolution

Revisiting Corruption Mathematical Models in the Fourth Industrial Revolution

Arpita Patra, Lovemore Matipira, Fanny Saruchera, K. S. Sastry Musti
Copyright: © 2021 |Pages: 27
ISBN13: 9781799843030|ISBN10: 1799843033|EISBN13: 9781799843047
DOI: 10.4018/978-1-7998-4303-0.ch013
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MLA

Patra, Arpita, et al. "Revisiting Corruption Mathematical Models in the Fourth Industrial Revolution." Advanced Perspectives on Global Industry Transitions and Business Opportunities, edited by Fanny Saruchera, IGI Global, 2021, pp. 270-296. https://doi.org/10.4018/978-1-7998-4303-0.ch013

APA

Patra, A., Matipira, L., Saruchera, F., & Musti, K. S. (2021). Revisiting Corruption Mathematical Models in the Fourth Industrial Revolution. In F. Saruchera (Ed.), Advanced Perspectives on Global Industry Transitions and Business Opportunities (pp. 270-296). IGI Global. https://doi.org/10.4018/978-1-7998-4303-0.ch013

Chicago

Patra, Arpita, et al. "Revisiting Corruption Mathematical Models in the Fourth Industrial Revolution." In Advanced Perspectives on Global Industry Transitions and Business Opportunities, edited by Fanny Saruchera, 270-296. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4303-0.ch013

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Abstract

Analyzing corruption is a topic of interest to many and is indeed very complex due to its inherent difficulties with its identification and quantification. Past studies present several variables, indices, computational models, and approaches, but their relevance in the fourth industrial revolution (Industry 4.0) has been debatable. This chapter addresses the need to revisit the mathematical models and approaches in the Industry 4.0 context. The chapter provides a foundation for this argument through a compressive literature review followed by a proposal of a three-stage concept for corruption identification. The chapter illustrates two case studies from which a strong justification derives for considering the digital transformation and use of big data to deal with corruption and improve the external and internal perceptions about corruption in general.

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