Abstract
Bibliometric analysis is a widely used technique for investigating and studying scientific information. There is no previous research that explains bibliometric analysis related to the adoption of big data analytics in auditing. Thus, this research will fill the gap in previous research to examine bibliometric analysis related to the adoption of big data analytics in auditing. This paper employs bibliometric analysis on Scopus-indexed journals to examine the topic of big data analytics in audits, utilizing the VOSviewer tool. The objective of utilizing bibliometric analysis in this research is to ascertain the progression of articles concerning the application of big data analytics in the field of auditing. This article discusses the development of the number of publications and citations, the trend of publication researchers, the country of publication articles, the relationship between researchers, and the relationship between words with the topic of big data analytics in the period 2010–2022. This research reveals areas of application of big data analysis adoption in auditing. Qualitative research, especially library research, is the best method widely used among writers. This study provides several useful insights into the meaning of big data and data analysis, the benefits of using big data analysis in the audit process, and how the audit process can be made easier with big data analysis. Among the most interesting insights, the results suggest that big data implies vast amounts of data that exceed the limits of what can be stored and processed. Thus, the use of data analytics helps auditors reduce cognitive errors arising from large and diverse data sets. This bibliometric research presents the number of articles and citations of research publications, which authors and countries have the most research on this topic, and the keywords/terminologies that appear most frequently as well as the meaning of these keywords/terminologies.
Publisher
National Research University, Higher School of Economics (HSE)