This study aims to explore the impact of AI chatbot marketing on customer satisfaction and loyalty within the context of online shopping in Saudi Arabia. It seeks to understand how the usability and responsiveness of AI chatbots influence customer perceptions and behaviors, ultimately contributing to enhanced satisfaction and loyalty. A cross-sectional research design was employed, utilizing a structured questionnaire to collect data from 271 customers with prior experience in online shopping. Convenience sampling was used to gather responses, and the questionnaire included dimensions related to the usability and responsiveness of AI chatbots. Structural equation modeling (SEM) was applied to analyze the data and assess the relationships between AI chatbot marketing, customer satisfaction, and customer loyalty. The results reveal that AI chatbot marketing significantly enhances customer satisfaction, which in turn positively influences customer loyalty. Additionally, customer satisfaction was found to mediate the relationship between AI chatbot marketing and customer loyalty. These findings highlight the critical role of AI chatbots in shaping customer experiences and fostering long-term loyalty. The study concludes that AI chatbot marketing is a powerful tool for improving customer satisfaction and loyalty in the online shopping environment. By investing in advanced AI technologies and optimizing chatbot functionalities, businesses can create more engaging and satisfying customer interactions. The findings suggest that businesses should prioritize the development and implementation of AI chatbots to enhance customer experiences. By focusing on usability and responsiveness, companies can drive higher levels of satisfaction and loyalty, thereby gaining a sustainable competitive advantage in the digital marketplace. These insights are particularly relevant for online retailers in Saudi Arabia and other regions aiming to capitalize on the growing trend of AI-driven customer engagement.

The research aims to analyze the determinants of cybersecurity risk disclosure (CSRD) in Saudi Arabia and discover the influence of CSRD on both firm value and stock return volatility. The study used a mixed-methods approach that combines qualitative and quantitative techniques to determine the relationships used by the content analysis method to analyze the annual financial reports of Saudi firms for the period from 2015 to 2022, to estimate the volume of CSRD, firm value, and stock return volatility. The results of the study show that the impact of a firm’s size, age, leverage, and profitability are positive and significant on CSRD. In contrast, free cash flow has no significant effect on CSRD. Moreover, a curvilinear relationship exists between operating expenses and CSRD. In addition, Firm value is positively and significantly correlated with CSRD and many firm characteristics. However, stock return volatility is negatively and significantly correlated with CSRD in the Saudi business environment.

This study examines the relationships between digital content marketing, technology innovation, and consumer buying decisions in the context of online shopping in Saudi Arabia. The study aims to determine how digital content marketing influences technology innovation and buying decisions, as well as the mediating role of technology innovation. A cross-sectional research design was employed, collecting data from 341 individuals with online shopping experience through convenience sampling. Structural equation modeling (SEM) was used to analyze the direct and indirect effects among the study constructs. The results indicate that digital content marketing significantly influences technology innovation and directly impacts consumer buying decisions. Additionally, technology innovation serves as a mediator, strengthening the effect of digital content marketing on buying decisions. The study underscores the importance of integrating digital content marketing with technology-driven innovations to enhance consumer engagement and drive purchasing behavior. The findings provide actionable insights for marketers seeking to optimize digital marketing strategies and leverage technological innovations to align with consumer preferences, ultimately improving online shopping experiences in Saudi Arabia.

This study’s purpose is to examine the impact of artificial intelligence (AI) on sustainable performance through the mediating effects of supportive leadership and organizational change. The study examines how AI influences supportive leadership and organizational change, which in turns leads to sustainable outcomes for organizations. A quantitative study was used to examine the relationship between the constructs. Data were collected from 364 employees working in Saudi Arabian service organizations and data analysis was performed using structural equation modeling to investigate the direct and indirect effects of the constructs. The findings indicate that the application of AI has significant and positive effects on supportive leadership and organizational change, both of which, in turns, improve sustainable performance. Essentially, this study demonstrates that AI provides extensive support for achieving sustainable performance through these mediative roles, enhancing the level of understanding of these phenomena through this specification. Applying these findings in organizational settings will enable managers to enhance work efficiency, with AI playing a vital role in achieving performance indicators for sustainability and ensuring the active involvement of humans in the strategic modification of systems to achieve long term goals. The practical guidance for organizational leaders offered here is to leverage AI-based systems for operational efficiency and long-term sustainability in service organizations.

Conventional linear discriminant analysis (LDA) and its graph-based extensions encounter challenges with class overlap, uneven sampling, and inflexible neighborhood selection. Although reverse nearest neighbor (RNN) graphs address data density, standard Reverse Neighborhood Discriminant Analysis (RNDA) relies on hard labels and binary edges. This work introduces Fuzzy Reverse Neighborhood Discriminant Analysis (FRNDA), which incorporates fuzzy class memberships, multiple fuzzy RNN graphs with smoothly decaying edge weights, and Choquet integral-based regularization to model non-additive interactions. The ${\mathsf{\ell }}_{2,1}$-norm is employed to induce joint feature sparsity. Theoretical contributions include a proof of monotonic convergence under supermodularity, a correction to a gradient error in Choquet fusion, and a tighter complexity bound for sparse approximate-neighbor graphs. FRNDA reduces to RNDA in the crisp case and generalizes to matrix inputs as 2DFRNDA. Evaluation on 12 benchmarks, including CIFAR-10, Fashion-MNIST, Caltech-101, Tiny ImageNet, Office-31, and FER2013, demonstrates that FRNDA/2DFRNDA outperforms classical and modern graph-embedding methods on 10 of the 12 datasets. Crisp RNDA remains preferable on Fashion-MNIST (for FRNDA alone) and FER2013, and these exceptions are analyzed. When applied to frozen ResNet-50 and Supervised Contrastive Learning features, FRNDA consistently improves accuracy, supporting its role as a hybrid post-processing method. FRNDA exhibits robustness to Gaussian noise, with only a 3.65-percentage-point drop in accuracy compared to RNDA’s 7.0-percentage-point decrease, and Choquet fusion surpasses linear averaging. Results are consistent across diverse initializations.

Information has developed as a vital reserve for managing contemporary organizations, since today’s commercial environment is instable, dynamic, tempestuous, and necessitates the escalating demand for precise, relevant, wide-ranging, well-timed, and cost-effective information needed to get-up-and-go the decision-making method in order to emphasize organizational capabilities to manage opportunities and pressures. Unfortunately, Saudi Arabian organizations have generally lagged behind as compared to developed countries in developing and implementing MIS for management decision-making. This research paper is a reflection of the collective discourse available in the literature regarding the relationship between management information systems and managerial decision-making and their impact on Saudi organizations. A thorough development and organization of MIS in an organization is capable of activating choices that would not only be debauched and precise but would be in streak with industry best practices and eventually result in organizational competence and efficiency in Saudi Arabian organizations.