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In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

This study aims to advance mixed methods as a research methodology in accounting through three research objectives: develop a typology of mixed methods research (MMR) features from current literature, analyse accounting papers published in two leading South African journals against these features, and offer recommendations for best practice going forward.

This paper follows five elements for a MMR review study: identify the methodological aim and choice of discipline; identify the relevant accounting MMR literature and collect the data; develop a codebook and analysis procedures to assess the reviewed papers against; report on the MMR findings; and discuss the findings and make recommendations.

The use of MMR as a methodological approach is increasing; however, in many instances published papers revealed limited methodological detail. Furthermore, most accounting MMR studies use a convergent MMR design, with data collected qualitatively using interviews/focus groups and quantitatively using questionnaires. Finally, accounting education studies is the topic within accounting research that mostly use MMR.

The study is limited to a five-year period and the prevalence of applicable MMR articles during that period in two journals.

This paper presents advantages of using MMR in accounting studies and offer recommendations for best practice to answer the complex accounting research questions of today.

This study is the first systematic examination of how mixed methods is used in accountancy research as reflected in South African journals.