Current financial statements are a comprehensive description of the enterprise, which characterize not only their financial and property status, but also the risks and prospects that allows making a comprehensive picture of the market activities. The research paper deals with the study of the essence and the role of financial statements in the process of enterprise management. The objective of this research paper is to establish the necessary content and features of financial statements that would enable to fully meet the needs of managerial staff, while re-maining economical, understandable and sufficient in terms of the content. The authors, through the analysis of the works of recognized scholars, investigated the main approaches to the definition of financial statements, which could allow us to define it as a set of indicators, representing generalized accounting data in the form that fully meets the information needs of the users for the process of managing economic activities. In the process of studying the composition of financial statements, their types are identified, and as a result - their role in the management of the enterprise are determined. The authors present their own vision of the essence of high-quality accounting information and its main characteristics, which can serve as content guidelines when compiling descriptive, non-financial and text parts of financial statements. The proposed approach allows formulating financial statements from the point of view of presentation of our own business on the best side, and in compliance with the regulatory and legal principles and requirements.

This study investigates AI bias in financial technology (FinTech)-based supply chain management in Pakistan. The study employs Structural Equation Modeling (SEM) to analyze data from diverse respondents. Hypotheses examine the relationships between AI integration, algorithm diversity, employee training, data quality, regulatory compliance, organizational culture, and AI bias. The findings reveal that higher AI integration leads to increased AI bias, Algorithm diversity reduces AI bias, while employee training decreases bias, Quality and diversity of data negatively correlate with AI bias, and regulatory compliance lowers bias. In addition, organizational culture mediates the relationship between AI integration and AI bias. This research contributes a holistic understanding of AI bias factors, guiding ethical AI adoption. Policymakers can use these insights to shape regulations, and industry practitioners can make informed decisions.

This study aims to investigate the impact of Business Intelligence Systems (BIS) on the quality of strategic decision-making in top-level management. The independent variables in this study are Data Quality, Data Visualization, and BI Management, while the dependent variable is the Quality of Strategic Decision-Making. Additionally, the study explores the moderator variable, BI Scope, to further understand the relationship between BIS and the quality of strategic decision-making. By providing valuable insights into the relationship between BIS and the quality of strategic decision-making, this study contributes to the existing body of knowledge on business intelligence and strategic decision-making. The findings show that BI Management, BI Scope, Data Quality, and Data Visualization have substantial and favorable correlations with the quality of strategic decision-making. Effective BI Management techniques contribute to higher decision-making quality, emphasizing the necessity of BI resource management. The study also underlines the importance of BI Scope as a moderator variable, demonstrating its impact on the connection between BI and quality of decision-making. In addition, the research shows that Data Quality and Data Visualization have a considerable influence on strategic decision-making quality. Using effective visualization tools and ensuring high-quality data improves the results of decision-making processes. The interaction impact between BI Scope and Data Quality, on the other hand, was determined to be non-significant.

Today & apos; s methodologies for data quality assessment and improvement are considerably aimed at reducing costs. Data quality comprises different dimensions, each having certain methods and techniques to assess and improve data quality. One of the most controversial dimensions is data believability in which less attention has been paid by scholars and researchers, because of its ambiguous nature. This is categorized under the "intrinsic data quality" dimensions. The current paper offers a precise and comprehensive definition of such quality dimension, and provides some parameters to understand it. In order to calculate these parameters, furthermore, different methods are discussed.