This study examines the influence of internal and external locus of control as mediators of financial literacy, financial attitudes, financial beliefs, and financial behavior of students in Timor-Leste. This study uses a quantitative approach with a survey method to collect sample data from students throughout Timor-Leste. Structural equation modeling (SEM) analyzes the relationship between financial literacy, financial attitudes, financial beliefs, internal and external locus of control, and financial behavior. The study’s results highlight the mediating role of internal and external locus of control in the relationship between financial literacy, financial attitudes, financial beliefs, and financial behavior of students in Timor-Leste. These findings can provide insight into the complex relationship between these factors in financial decision-making. Practical implications for educational institutions and policymakers in Timor-Leste, namely emphasizing the importance of considering internal and external locus control in financial literacy programs to improve students’ financial behavior. This study aims to fill the knowledge gap about student financial literacy by expanding the understanding of the relationship between these factors.

This study focuses on the problems of imperfect internal control effectiveness, insufficient information transparency, and plummeting stock prices. The study selects the data of non-financial main board listed companies in China’s Shanghai and Shenzhen A-shares from 2012 to 2021 as a sample, and adopts an empirical research methodology, which reveals that the effectiveness of internal control is negatively related to the trend of share price crash, and efficient internal control is positively related to the transparency of corporate information environment. The findings suggest the impact of internal control on the risk of stock price crash at the individual stock level and provide empirical support for listed companies to manage their risks. This study has practical value in guiding listed companies to strengthen internal control, improve information transparency, mitigate the risk of stock price crashes, and provide a decision-making basis for the healthy and stable development of the capital market.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.