This study examines the influence of AI-driven personalization capabilities on consumer purchase intention, focusing on the mediating role of customer engagement. It explores how recommendation accuracy, real-time adaptation, and predictive analytics capability enhance customer engagement in digital commerce. Data were collected from 473 online consumers using a questionnaire and analyzed using Structural Equation Modeling with SmartPLS. The findings reveal that all three AI-driven personalization dimensions significantly and positively affect customer engagement. In turn, customer engagement has a strong positive impact on purchase intention. Moreover, customer engagement was found to mediate the relationships between AI-driven personalization capabilities and purchase intention, confirming its critical role in translating technological capabilities into consumer behavior. All hypotheses were statistically supported. This study enriches the literature on AI in marketing context by clarifying how personalization capabilities influence consumer decisions. It also provides practical insights for businesses aiming to enhance engagement and increase purchase intention through advanced AI-driven personalization strategies.

The integration of AI in DM is reshaping strategies to promote sustainability-oriented outcomes. AI-powered capabilities, including PA and PS, offer opportunities for firms to improve the MP while addressing the environmental responsibilities. This research examines the straight impacts of AIA on advertising presentation and examines the part of PA, PS, and SDM as key mechanisms through which AI enhances sustainable DM outcomes. A structural method was developed and tested using SPSS 26.0 and SmartPLS-SEM 4.0 based on data gathered from 312 marketing professionals. AIA was treated as the primary construct, hypothesized to positively influence PA and PS, which subsequently enhanced the SDM. The structural method was assessed to test the significance of direct relations and assess the explanatory power of the constructs. The analysis indicates that the proposed hypotheses were well supported. AIA significantly improved PA (β = 0.78), PS (β = 0.63), and SDM, enhancing overall marketing outcomes. The results emphasize the central role of SDM in leveraging AI insights to enhance campaign effectiveness, customer engagement, and brand loyalty in sustainability-focused marketing. This research demonstrates that AIA is strategically leveraged to improve MP while advancing environmental responsibility. The proposed method offers practical guidance for managers and policymakers aiming to integrate AI in sustainable DM, balancing ecological imperatives with competitive growth.

Digital Twin (DT) technology combined with the Internet of Things (IoT) can be used to provide new solutions to real-time monitoring and management of agriculture. This paper introduces an IoT-based digital twin platform that will help in streamlining agricultural operations by incorporating different sensor technologies to monitor vital soil and crop conditions, such as moisture, temperature, pH, and nitrogen concentrations. The system offers predictive analytics to inform irrigation control, pest control, and fertilizer application, to help in making agricultural activities more sustainable. The effectiveness of the model is tested based on real time data integration and predictive modeling with 92% accuracy in monitoring soil moisture and an 87 percent accuracy in predicting crop yields. Although the system shows a high potential in terms of resource optimization and productivity, issues like sensor calibration, network connectivity and scalability to bigger operations exist. The future direction must be aimed at making sensors more reliable, more scalable, and adding AI and automation to make the system even more efficient and applicable in precision farming.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.

Up to 78% of oil and gas megaprojects fail to deliver on initial commitments, yet maintenance readiness is routinely deferred until after construction decisions are already irreversible. This study investigates the previously unexamined link between maintenance readiness and interface management across the capital-project life cycle through a systematic literature review combined with a 30-year longitudinal analysis of 941 anonymised interface lessons-learned records spanning 21 countries. Root causes were classified using ISO 14224 of 2016, and predictive models were validated through stratified coding (Cohen's κ = 0.81) and five-fold cross-validation. The analysis revealed that five root causes drove 81.2% of all failures, including slow interface identification (18.5%), uncertain technical definitions (17.4%), weak governance (17.4%), unclear scope and ownership (16.3%), and cross-discipline communication breakdowns (11.6%), with key effects being performance impact (41.8%) and schedule delay (25.7%), while 33.0% of records were classified as high or critical risk and approximately 40.3% originated in Front-End Engineering Design. The Interface Criticality Index (ICI) correlated strongly with high-risk classification (r = 0.83), and the Random Forest classifier achieved ROC-AUC = 0.848 and PR-AUC = 0.850 respectively. The findings conclude that maintenance readiness should be established as a distinct, front-end-loaded discipline rather than a post-handover activity, as embedding readiness controls upstream converts lessons-learned records from retrospective artefacts into proactive design constraints, enabling early intervention before costly lock-in.

The Saudi Vision 2030 framework has catalyzed an ambitious digital health transformation within the Kingdom's healthcare system. This systematic review provides a comprehensive analysis of the landscape of research concerning the application of Artificial Intelligence (AI) in Saudi Arabia's health sector and its impact on healthcare efficiency. Utilizing a dataset of 1,250 records from the Scopus and Web of Science databases, with an in-depth analysis of 85 relevant studies, this review maps the conceptual structure and dynamics of this emerging field. The analysis examines publication trends, key research themes, leading contributors, and the methodological focus of the published papers. The results disclose a rapidly growing trend in publications, accelerating from 2021 onwards, driven by national strategic priorities and the need to optimize healthcare delivery. The research is characterized by strong institutional contributions from major Saudi universities and medical cities, with emerging international collaborations. Thematic clusters are dominated by AI in medical imaging and diagnostics, predictive analytics for patient management, AI-driven health informatics, and resource optimization. The findings indicate that AI applications are significantly enhancing diagnostic accuracy, streamlining administrative processes, predicting disease outbreaks, and optimizing resource allocation, thereby contributing markedly to healthcare efficiency. This survey offers a foundational overview of a critical domain within Saudi Arabia's health sector evolution, highlighting the synergistic role of national policy and technological innovation in shaping a future-ready healthcare system.

The use of Artificial Intelligence (AI) in healthcare systems is a major change or rather a shift of paradigms that can potentially change through and through the whole medical practice, that is from the administrative logistics to the clinical diagnostics and therapeutic interventions. This review is an amalgamation of decade-long research which provides a holistic view of the applications of AI in healthcare continuum, hence consulting its role in the optimization of hospital operations and scheduling, the improvement of diagnostic accuracy in radiology and pathology, and personalization of treatment plans in fields like oncology and chronic disease management, and so on along with the engagement of patients through chatbots and wearable technology. Moreover, the article has critically assessed the operational efficiencies obtained in the areas such as supply chain management, resource allocation, and clinical workflow automation among others, thus highlighting the importance of alive and kicking in the healthcare. On the downside, the author pointed out the main hurdles which have the power to put a brake on the adoption of these advanced technologies in medical practices like data privacy issues, algorithmic bias, the "black box" problem in clinical decision-making, and the moral dilemmas of using autonomous systems in life-or-death situations. By studying the whole journey from basic to very advanced applications, this review argues that the future of healthcare is still. It should be a collaborative portraithuman-oriented whereby the AI becomes a partner of the clinician instead of a competitor thus not only creating more robust, effective and patient-activated care systems paving the way for better health outcomes.