The objective of this research is to investigate the impact of Artificial Intelligence (AI) on improving the quality of financial reporting in the Islamic banking industry. The study is conducted within the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the data collected from a sample of 364 professionals working in the field of Islamic banking. The results of our study suggest that Performance Expectancy, Effort Expectancy, and Social Influence are important factors in predicting individuals' Behavioural Intention to use Artificial Intelligence (AI). Additionally, the presence of Facilitating Conditions further enhances the impact of these factors on individuals' actual Use Behaviour. Significantly, it was shown that Use Behaviour played a significant role in determining the perceived quality of financial reporting. Nevertheless, the study did not find empirical evidence to demonstrate the direct influence of Behavioural Intention on Financial Reporting Quality. This implies that the actual implementation of Artificial Intelligence is required to fully realize its advantages. The use of artificial intelligence (AI) into governance frameworks presents a potentially advantageous pathway for Islamic banks to uphold Shariah principles, while concurrently bolstering accountability and fostering ethical banking practices.

The main objective of this research was to determine the impact of Information and Communication Technologies (ICTs) on human talent management within a public university in Ica, Peru. Using a quantitative, basic, and correlational-causal approach, a sample of 474 participants, comprised of administrative and teaching staff, was analyzed. The participants were characterized by a professional profile with high academic qualifications and intermediate work experience. Data collection was carried out through a survey based on the Technology Acceptance and Use (UTAUT) model, and the information was processed using partial least squares structural equation modeling (PLS -SEM). The results of the structural model confirmed that all the hypotheses were significant with a p-value of 0.000, highlighting that Expectations of Effort (β = 0.635) and Enabling Conditions (β = 0.632) were the most influential predictors of the dependent variable. The model achieved a coefficient of determination R² of 0.325, explaining 32.5% of the variability in human talent management. In conclusion, the modernization of talent management in the public university environment depends critically on reducing the technical complexity perceived by the user and strengthening the institutional technological infrastructure, factors that are more important than social influence or net performance expectations.

The increasing popularity of smartphones has led to the rise of e-wallets, which allow users to store their payment information on their devices and complete financial transactions conveniently and securely. This research extended the unified theory of acceptance and use of technology (UTAUT2) model to investigate the main factors influencing users’ intention to accept e-wallets in Jordan, where little research focused on such apps. Using a quantitative method, a sample of 181 users was utilized, and an instrument of 32 items was used. Findings revealed that price value, hedonic motivations, social influence, performance expectancy and perceived trust are major predictors of users' intention to use e-wallets apps. In contrast, facilitating conditions and effort expectancy are not significant toward the adoption process. Finally, conclusions and future work are presented in the last section of the study.