Relevance of big data to forensic accounting practice and education

Zabihollah Rezaee and Jim Wang

Managerial Auditing Journal, 2018, vol. 34, issue 3, 268-288

Abstract: Purpose - This paper aims to examine the relevance of Big Data to forensic accounting practice and education by gathering opinions from a sample of academics and practitioners in China. Design/methodology/approach - The authors conduct a survey of academics and practitioners regarding the desired demand, importance and content of Big Data educational skills and topics for forensic accounting education to effectively respond to challenges and opportunities in the age of Big Data. Findings - Results indicate that the demand for and interest in Big Data/data analytics and forensic accounting will continue to increase; Big Data/data analytics and forensic accounting should be integrated into the business curriculum; many of the suggested Big Data topics should be integrated into forensic accounting education; and some attributes and techniques of Big Data are important in improving forensic accounting education and practice. Research limitations/implications - Readers should interpret the results with caution because of the sample size (95 academics and 103 practitioners) and responses obtained from academics and practitioners in one country (China) that may not be representative of the global population. Practical implications - The results are useful in integrating Big Data topics into the forensic accounting curriculum and in redesigning the forensic accounting courses/programs. Social implications - The results have implications for forensic accountants in effectively fulfilling their responsibilities to their profession and society by combating fraud. Originality/value - This study provides educational, research and practical implications as Big Data and forensic accounting are advancing.

Keywords: Big data; Big data analytics; Business curriculum; Forensic accounting education and practice; M40; M41; M42 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:majpps:maj-08-2017-1633

DOI: 10.1108/MAJ-08-2017-1633

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