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This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.

This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.

This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.

This paper aims to examine the antecedents of the Automated VAT Solution (AVS) and its eventual consequence on value-added tax (VAT) compliance costs among the small and medium enterprises (SMEs) in Gulf Cooperation Countries (GCC), with the United Arab Emirates (UAE) as context.

A quantitative research design was deployed through a survey of 576 SMEs in the UAE. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The findings revealed that technological factors (IT complexity and IT competency) and organizational factors (management support and size of SME) significantly influence AVS adoption. However, only consumer pressure was found to be significant among the environmental factors, and AVS adoption was found to have a significant negative effect on the VAT compliance cost.

A lower coefficient of determination for the effect of AVS adoption on VAT compliance cost meant that there may be other accounting-related technologies that improve operational efficiency and process automation and, in the long run, lower the cost of VAT compliance. These technologies should be included in future studies.

The findings imply that the adoption of AVS among SMEs is highly desirable, as it reduces VAT compliance costs. Increased regulatory pressure by the UAE’s policymakers is also desirable to accelerate AVS adoption for enhanced cost reduction and revenue maximization from the perspectives of both the government and SMEs.

To the best of the authors’ knowledge, this study could be the first to expand the Technology-Organization-Environmental (TOE) Framework through the integration of determinants of AVS adoption and VAT compliance costs among SMEs in GCC countries.