This study critically explores the ethical dimensions of Artificial Intelligence (AI) in digital marketing, with a particular emphasis on AI accountability and fairness among online consumers in Palestine. Employing a quantitative research design, the study utilizes Structural Equation Modeling (SEM) to analyze data collected from 385 participants. The primary aim is to examine how AI-driven marketing strategies shape perceptions of fairness, with accountability acting as a mediating variable. The results demonstrate that AI accountability significantly mediates the relationship between AI-generated marketing content and consumer perceptions of fairness, highlighting the critical role of accountability in promoting ethical AI practices. This research underscores the pressing need for transparency in AI systems, as well as the importance of mitigating algorithmic biases that may adversely affect consumer experiences. The findings offer actionable insights for businesses and policymakers, advocating for the development of comprehensive regulatory frameworks that ensure the ethical application of AI technologies in marketing while fostering consumer trust. By contributing to the ongoing discourse on AI ethics, this study provides a robust foundation for understanding the complex interplay between AI technologies, fairness, and accountability in digital marketing. These insights are particularly pertinent to industries leveraging AI for content creation, as they navigate the ethical challenges associated with consumer engagement in an increasingly digitalized environment.

Purpose: This study explores the relationships between Environmental, Social, and Governance (ESG) disclosure and firm performance (FP)—specifically operational (ROA), financial (ROE), and market performance (Tobin's Q)—for companies listed on the Palestine Exchange (PEX) and Amman Stock Exchange (ASE). Design/methodology/approach: The analysis focuses on firms listed on PEX and ASE over the period 2015–2024. ESG disclosure data and financial indicators were compiled from annual reports and publicly available databases. To examine the impact of ESG and its sub-components on firm performance, the study employs panel data regression techniques, allowing for robust testing of the proposed hypotheses. Findings: The results indicate that overall ESG disclosure is positively associated with both operational and market performance (ROA and Tobin's Q) among firms in Palestine and Jordan. However, when examining ESG sub-components individually, environmental (EVN) and social responsibility (CSR) disclosures exhibit a mixed relationship: they are positively linked to market valuation (Tobin's Q), but their association with operational and financial performance (ROA and ROE) is statistically insignificant. Governance (CG) disclosure demonstrates a consistent positive relationship with both ROA and Tobin's Q, while its effect on ROE is less pronounced. Notably, firms with larger asset bases and higher leverage tend to exhibit greater levels of ESG, EVN, CSR, and CG disclosure. Enhanced disclosure in these areas is generally accompanied by improvements in operational efficiency and market perception. Research limitations/implications: The findings provide valuable insights for policymakers in Palestine and Jordan, highlighting the importance of ESG reporting in enhancing firm performance and market confidence. The study underscores the need for further development of ESG disclosure frameworks tailored to the unique characteristics of these markets. Originality/value: By focusing on the Palestinian and Jordanian stock exchanges—markets that have received relatively limited attention in ESG research—this study contributes new evidence to the literature. It sheds light on the varied impact of ESG and its components on firm performance in emerging economies, providing insights that can inform both future research and practical policy development.

This study investigates the determinants of chatbot adoption intention in Jordan's civil status and passport department, with a particular focus on technology trust as a moderating variable. Drawing on the Technology Acceptance Model (TAM), we developed and tested hypotheses regarding chatbot adoption factors. The research employed a quantitative approach, collecting data from 687 valid respondents out of 743 distributed questionnaires. Analysis using Smart-PLS software revealed that most examined factors positively influenced adoption intentions, with one notable exception: ease of use showed no significant impact on user attitudes. The findings confirmed that positive attitudes toward chatbots significantly enhanced adoption intentions, while trust in technology strengthened this attitude-intention relationship. This study extends existing theoretical frameworks by providing empirical evidence from a Middle Eastern context, specifically in Jordan's public sector, offering valuable insights for both researchers and practitioners implementing chatbot solutions in public services. The results particularly emphasize how trust in technology shapes user acceptance in the Jordanian context, contributing new perspectives to the current understanding of chatbot adoption dynamics.

Although small and medium-sized enterprises (SMEs) are essential to economic growth, Saudi Arabia’s Vision 2030 has expanded their access to financing, increasing the need for effective credit-risk management and early financial-distress detection. While artificial intelligence (AI) and machine learning (ML) have shown considerable potential in credit-risk assessment, limited research has examined how banking professionals’ perceived reliance on financial, behavioral, and firm-characteristic information is associated with organizational AI/ML use and perceived early-warning effectiveness in SME lending. This study examines these relationships using survey data from 118 banking professionals in Saudi Arabia and partial least squares structural equation modeling (PLS-SEM). The results show that Perceived Reliance on Financial Indicators (β = 0.215, p = 0.024), Perceived Reliance on Behavioral Indicators (β = 0.266, p = 0.023), and Perceived Reliance on Firm-Characteristic Information (β = 0.242, p = 0.020) are significantly positively associated with Perceived Organizational Use of AI/ML. Among these antecedents, Perceived Reliance on Behavioral Indicators has the largest estimated standardized path coefficient (β = 0.266), although differences among the three coefficients were not formally tested and should not be interpreted as establishing relative superiority. In turn, Perceived Organizational Use of AI/ML is significantly positively associated with Perceived AI-Enabled EWS Effectiveness (β = 0.723, p < 0.001) and partially mediates the relationships between the three credit-risk information domains and perceived early-warning effectiveness. Building on these findings and the relevant machine-learning literature, the study proposes a conceptual XGBoost–SHAP architecture for future borrower-level implementation and validation. No XGBoost model is trained or validated, and the survey findings do not constitute empirical validation of actual SME default or fraud prediction.