Search results

Non-performing loans (NPLs) may determine an overall weakness of the banking system within a country. The purpose of the present study is to analyze the impact of government failures on NPLs in Asian countries in the time span 2000–2020. The variables employed as proxies of government failures are public debt as % of gross domestic product (GDP) and a government ineffectiveness index proposed by the World Bank.

The econometric approach employed is a panel generalised time series (GLS) model with heteroskedasticity and autocorrelation specific to each panel.

The results confirm that public debt as % of GDP and governmental ineffectiveness impacted significantly on NPLs for Asian countries in the observed period.

The literature offers similar results only for some individual Asian countries, while a wider analysis is lacking for Asian macroareas. The present paper considers 31 Asian countries, and supports the idea that a healthy financial sector is correlated to institutional quality and political regime. Hence, policy makers are advised to monitor governance indicators to reduce NPLs.

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.