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This paper undertakes an extensive and systematic review of the literature on earnings management (EM) over the past three decades (1992–2022). Furthermore, the study identifies emerging research themes and proposes future avenues for further investigation in the realm of EM.

For this study, a comprehensive collection of 2,775 articles on EM published between 1992 and 2022 was extracted from the Scopus database. The author employed various tools, including Microsoft Excel, R studio, Gephi and visualization of similarities viewer, to conduct bibliometric, content, thematic and cluster analyses. Additionally, the study examined the literature across three distinct periods: prior to the enactment of the Sarbanes-Oxley Act (1992–2001), subsequent to the implementation of the Sarbanes-Oxley Act (2002–2012), and after the adoption of International Financial Reporting Standards (2013–2022) to draw more inferences and insights on EM research.

The study identifies three major themes, namely the operationalization of EM constructs, the trade-off between EM tools (accrual EM, real EM and classification shifting) and the role of corporate governance in mitigating EM in emerging markets. Existing literature in these areas presents mixed and inconclusive findings, suggesting the need for further theoretical development. Further, the study findings observe a shift in research focus over time: initially, understanding manipulation techniques, then evaluating regulatory measures, and more recently, investigating the impact of global accounting standards. Several emerging research themes (technology advancements, cross-cultural and cross-national studies, sustainability, behavioral aspects and non-financial indicators of EM) have been identified. This study subsequent analysis reveals an evolving EM landscape, with researchers from disciplines like data science, computer science and engineering applying their analytical expertise to detect EM anomalies. Furthermore, this study offers significant insights into sophisticated EM techniques such as neural networks, machine learning techniques and hidden Markov models, among others, as well as relevant theories including dynamic capabilities theory, learning curve theory, psychological contract theory and normative institutional theory. These techniques and theories demonstrate the need for further advancement in the field of EM. Lastly, the findings shed light on prominent EM journals, authors and countries.

This study conducts quantitative bibliometric and thematic analyses of the existing literature on EM while identifying areas that require further development to advance EM research.

Corporate social responsibility (CSR) and fintech have emerged as critical megatrends in the banking industry. This study aims to examine the impact of financial technology on the relationship between CSR and banks' financial stability. Specifically, it investigates the moderating effect of fintech on the association between CSR and the financial stability of conventional banks operating in Qatar, UAE, Saudi Arabia, Kuwait, Bahrain, Jordan, Pakistan and Turkey from 2010 to 2021.

To achieve the authors’ objective, the authors apply Baron and Kenny's three-link model, tested with fixed and random effects regression models.

The results reveal that the development of fintech decreases banks' financial stability, whereas it promotes banks' involvement in CSR strategies. Furthermore, the findings indicate that fintech plays a moderating role in the relationship between CSR and financial stability. It positively moderates the impact of CSR on financial stability. The robustness analysis highlights the mutual reinforcement of fintech and CSR dimensions in improving the financial stability of banks. Thus, by fostering community and product responsibility, fintech could enhance the financial stability of banks.

Finally, the authors recommend that banks focus more on developing technological and environmentally friendly financial products.

This study contributes significantly by providing valuable insights for managers and policymakers seeking to improve banks' financial stability through the simultaneous adoption of new financial technology products and the strong commitment to CSR practices.

This study aims to thoroughly examine and understand the relationship between working capital management (WCM) and the sustainable financial performance (FP) in the context of the New Zealand companies listed on stock exchange.

This study has applied various regression techniques to examine WCM and the sustainable FP relationship. The data set period is from 2009 to 2019. The results are robust upon various layers of robustness parameters. The system-generalized method of moments is applied for managing endogeneity issue.

The research reveals compelling evidence of a meaningful connection between WCM and sustainable FP indicators. The study specifically highlights the significant negative associations between the cash conversion cycle, average collection period and average age of inventory with the firm’s sustainable FP. Through robust analyses and various parameter adjustments, the study ensures the credibility and reliability of its conclusions, further reinforcing the impact of WCM on the financial health of New Zealand-listed firms.

This study provides future directions for researchers to explore the dynamic relationship between WCM and a firm sustainable FP because it is still a demanding and challenging area. Future research may care to explore the optimal way to reduce the cash conversion cycle, average collection period and average age of inventory for New Zealand firms. The current study does provide insights to NZ financial managers, which is useful for improving sustainable FP by efficiently managing WCM.

WCM is problematic and constitutes a notable challenge; it requires further research, especially in small economies such as New Zealand. Hence, it is an updated and fresh attempt based on a larger data set to measure the empirical relationship between WCM and the sustainable performance of New Zealand-listed firms. Furthermore, the current study uses dynamic panel data estimation techniques in addition to multiple regression techniques.