The purpose of this investigation was to establish the connection between ownership arrangement and valuation of the ability of a business to carry on. The investigation's goal is to clarify how various ownership forms affect how to assess a company's ability to remain in business. The listed firms at ASE throughout the year 2016–2022, according to this study of 65 covers the years 2016–2022, a dynamic panel system GMM estimation, had demonstrated a substantial degree of ownership structure in line with higher going concern awareness and implementation in Jordan. This study indicated that family ownership, foreign ownership, and block holder ownership were particularly important in affecting Jordan's going concern. This study explores the complex relationship between ownership forms and a company's ability to continue operating. In light of our findings, it is crucial for both practitioners and policymakers to adopt a thoughtful and nuanced approach when assessing the continued viability of businesses. This involves considering the unique ownership structures and governance mechanisms of each company.

This study aims to examine the connection between the disclosure of AI and financial performance of the banking sector in Jordan within a robust panel econometric setup. The purpose of this study is to examine the connection between Artificial Intelligence (AI) disclosure and financial performance of Jordanian banks in a wide-ranging panel econometric approach. The study uses the annual data of the 20 banks in Jordan for the period (2017-2023). The current study differs from previous studies in using panel unit root tests, panel cointegration analysis, fixed-effects and random-effects estimation, Hausman specification testing, and dynamic System Generalized Method of Moments (System GMM) estimation to overcome econometric problems such as non-stationarities, heteroskedasticity, serial correlation, cross-sectional dependence, and endogeneity. AI disclosure is assessed using a newly-developed AI Disclosure Index (AIDI) created by analyzing the textual content of annual reports against the AI Disclosure categories derived from the validated AI disclosure categories covering AI infrastructure, applications, governance, cybersecurity, and digital transformation. ROA and ROE are used for measuring financial performance. The empirical results show that AI disclosure has a positive and significant impact on bank financial performance, as measured by ROE, while accounting for the dynamic persistence and heterogeneity of banks. The results are consistent with alternative specifications and diagnostic procedures. The study is a novel and valuable addition to the literature, as it combines cutting-edge financial econometric techniques with AI disclosure analysis in a new banking market. The results indicate that clear transparency in AI use boosts operational efficiency, builds stakeholder trust, and fosters financial sustainability in banking institutions. The study also offers policy recommendations to regulators and financial institutions to develop a standard for disclosure of AI.