This research explores the determinants affecting academic researchers’ acceptance of AI writing tools using the Theory of Reasoned Action (TRA). The impact of attitudes, subjective norms, and perceived barriers on researchers’ intentions to adopt these technologies is examined through a cross-sectional survey of 150 researchers. Structural Equation Modeling (SEM) is employed to evaluate the measurement and structural models. Findings confirm the positive influence of favorable attitudes and subjective norms on intentions to use AI writing tools. Interestingly, perceived barriers did not significantly impact attitudes or intentions, suggesting that in the academic context, potential benefits may outweigh perceived obstacles to AI writing tool adoption. Contrarily, perceived barriers do not significantly affect attitudes and intentions directly. The TRA model demonstrates considerable explanatory and predictive capabilities, indicating its effectiveness in understanding AI writing tool adoption among researchers. The study’s diverse sample across various disciplines and career stages provides insights that may be generalizable to similar academic contexts, though further research with larger samples is needed to confirm broader applicability. Results offer practical guidance for tool developers, academic institutions, and publishers aiming to foster responsible and efficient AI writing tool use in academia. Findings suggest strategies such as demonstrating clear productivity gains, establishing AI Writing Tool programs, and developing comprehensive training initiatives could promote responsible adoption. Strategies focusing on cultivating positive attitudes, leveraging social influence, and addressing perceived barriers could be particularly effective in promoting adoption. This pioneering study investigates researchers’ acceptance of AI writing tools using a technology acceptance model, contributing to the understanding of technology adoption in professional contexts and highlighting the importance of field-specific factors in examining adoption intentions and behaviors.

This article's goal is to investigate the connection between banking supervision (BakSup) and anti-money laundering (AnMeyLg). The study's hypothesized model was examined using a survey questionnaire research methodology. The Central Bank of Sudan and the compliance departments of Sudanese banks provided the information. Just 247 valid surveys were received out of a total distribution of 450 questionnaires. The partial least squares method for structural equation modeling was applied. The statistical findings supported the impact of banking supervision on anti-money laundering. In-depth discussions of the study's specifics and consequences were included at various points. There are several real-world applications of the findings of this study. Management can use the findings to guide their decisions on whether or not to establish anti-money laundering procedures in their banking firms. Banking supervision may provide managers with effective anti-money laundering strategies for maximizing bank performance and maintaining market share. This is one of the few empirical studies of its kind that looks at how banking supervision affects efforts to combat money laundering.

This paper examines the interplay between country-level control of corruption (CLCC), female board representation, and corporate sustainability performance using panel data analysis with fixed effect models on a global sample of over 36,000 observations from 2016 to 2021. Our analysis was conducted in four stages: first, assessing the baseline effects of gender diversity on sustainability outcomes within the context of corruption control; second, exploring these effects across groups categorized by Sustainable Development Goals (SDGs); third, examining differences between developing and developed regions; and finally, analyzing regional variations across America, Europe, and Asia. The results indicate that both control of corruption and increased board diversity have significant positive effects on environmental, social, and governance (ESG) scores, with a highly significant interaction effect, suggesting a substantial combined influence. Additionally, analyses based on SDG alignment demonstrate that control of corruption improves ESG performance irrespective of SDG level, while board diversity benefits both high and low SDG groups. Regional analyses further reveal varied effects across America, Europe, and Asia. These findings underscore the significance of corruption control and female representation in enhancing corporate sustainability worldwide, emphasizing the potential for firms to improve ESG performance by prioritizing anti-corruption efforts and promoting board diversity. This international study contributes valuable insights into the relationship between country-level governance, corporate board composition, and sustainability outcomes across diverse contexts, highlighting the importance of tailoring sustainability policies and practices based on regional and developmental nuances.

This study evaluates the efficiency of external auditors in combating money laundering in the Sudanese financial sector. It examines how auditors’ capabilities and characteristics influence their effectiveness in anti-money laundering (AML). Quantitative data were collected through a survey of 228 external auditors in Sudan. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. Auditors’ skills in rigorous financial analysis and investigation significantly improve their AML performance. Additionally, a strong ethical orientation positively affects auditors’ effectiveness. However, practical experience did not enhance the detection of money laundering. Most notably, the adoption of advanced technologies and analytics tools had the strongest positive impact. The findings highlight the need for enhanced auditor training, greater investments in regulatory technology (RegTech), increased oversight of ethics, and expanded information-sharing between auditors, regulators, and financial institutions. This study provides unique empirical evidence on leveraging external auditors’ capabilities to combat money laundering, specifically within the Sudanese context. The research model demonstrated good explanatory power and predictive accuracy.