Big data analytics in auditing and the consequences for audit quality: A study using the technology acceptance model (TAM)

Abstract

The study examines the impacts of using two dimensions of the technology acceptance model (TAM), perceived usefulness and perceived ease of use, on the adoption of big data analytics in auditing, and the subsequent impact on audit quality. Five hypotheses were developed. A questionnaire survey was undertaken with external affiliated audit companies and offices in Jordan. Eventually, 130 usable questionnaires were collected, representing a 72.22% response rate. Structural equation modelling (SEM) was employed for diagnosing the measurement model, and to test the hypotheses of the study. The study finds that perceived usefulness and perceived ease of use have a direct effect on audit quality, without mediating the actual use of data analytics. However, the use of big data analytics is shown to moderate the relationship between perceived usefulness and audit quality, but not between the perceived ease of use and audit quality. The study is one of the first to examine auditors’ acceptance of big data analytics in their work and the impact of this acceptance and actual use on audit quality. It contributes to the existing literature in auditing through its application of SEM to examine the impact of big data analytics usage on audit quality by using the TAM.