Abstract: This study examines the impact of AI-driven HRM practices, workforce adaptability, and human–AI collaboration on employee performance in AI-enabled organizations, with a particular focus on the mediating role of organizational agility. It explores how AI-enabled HR systems, employees’ adaptive capabilities, and effective collaboration between humans and AI contribute to enhancing organizational agility and improving employee performance outcomes. Data were collected from 418 employees working in AI-enabled organizations using a structured questionnaire and analyzed using Structural Equation Modeling (SEM) via SmartPLS. The findings reveal that AI-driven HRM practices, workforce adaptability, and human–AI collaboration all have significant positive effects on both organizational agility and employee performance. In addition, organizational agility shows a strong positive effect on employee performance. Moreover, organizational agility was found to significantly mediate the relationships between AI-driven HRM practices, workforce adaptability, and human–AI collaboration with employee performance, confirming its critical role in translating digital HR capabilities into performance outcomes. All hypotheses were statistically supported. This study contributes to the HRM and digital transformation literature by clarifying how AI-enabled HR practices and human capabilities enhance performance through organizational agility. It also provides practical insights for organizations seeking to improve employee performance by integrating AI technologies with agile management practices and adaptive workforce strategies.

Abstract: The world supply chains are rapidly going digital and at the same time, there are growing demands of sustainability transparency and responsibility. The existing literature has mostly focused on the performance advantages of digital technologies, although it has paid relatively little attention to the fact that the latter help prepare sustainability reporting and to organizational mechanisms that support this process. Based on the Organizational Information Processing Theory and the dynamic capabilities perspective, the current study constructs a first-stage moderated mediation hypotheses and tests the hypothesis based on the role of AI-driven supply chain intelligence and supply chain digital integration in enhancing sustainability reporting preparedness through Organizational Information Processing Capability (OIPC) and the moderating role of Top Management Digital Orientation (TMDO). Based on the survey data obtained after surveying 268 manufacturing companies, applying the Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings show that OIPC is an intermediate mediating process whereby the digital technologies are transformed into the reporting readiness. Digital integration of supply chains has a high positive correlation with OIPC and reporting ready. On the other hand, AI-based intelligence also portrays a high level of negative direct and indirect impacts hence demonstrating a complex effect where higher levels of analytics can be limiting to information processing in situations where there is inadequate organizational alignment. TMDO reduces such negative AI effects and at the same time, reduces the marginal contribution of integration mechanisms. These results emphasize the fact that the process of digital transformation towards sustainability is not absolutely related to technology itself but rather depends on the capacity of the organization and digital leadership.

Abstract: This study examines the impact of artificial intelligence (AI) adoption on operational efficiency in tourism firms, incorporating implementation challenges as a mediating mechanism. Drawing on Diffusion of Innovations, Service-Dominant Logic, and Dynamic Capabilities theory, the research proposes an explanatory structural model linking chatbot use, service automation, and predictive analytics to operational performance outcomes. Data were collected from 121 tourism SMEs located in Cali, Colombia, and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AI adoption positively influences operational efficiency both directly and indirectly through implementation challenges, confirming partial mediation. Chatbot use emerges as the strongest driver of AI integration, followed by service automation and predictive analytics. The findings suggest that operational benefits derived from AI depend not only on technological deployment but also on organizations’ capacity to manage technical, regulatory, and human barriers. This study contributes empirical evidence from an underrepresented Latin American context and advances understanding of AI as a systemic organizational capability.

Abstract: Changes in post-pandemic work patterns have encouraged organizations to adopt remote work systems, thus demanding adjustments in human resource management. This study aims to analyze the effect of human resource planning and digital technology's availability on human resource management's effectiveness, with Work from Home (WFH) strategy as a mediating variable. This research uses an associative quantitative approach. Primary data was collected through online questionnaires distributed to permanent employees from various companies in Indonesia that have officially implemented the WFH work system. The sampling technique used was purposive sampling, with 200 respondents. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the help of SmartPLS version 4 software. The results showed that HR planning and the availability of digital technology positively affect the effectiveness of HR management. They also influence the formation of an effective WFH strategy, which is shown to partially mediate the relationship between the two independent variables and HRM effectiveness. The implication of this study confirms the importance of integration between HR planning, digital technology readiness, and flexible work strategy design to improve organizational performance in the digital work era.

Abstract: This study examines the relationship between appreciation of high achievement, punishment for high achievement, and addictive social media use among university students, with punishment for high achievement acting as a mediating mechanism. A quantitative, non-experimental, cross-sectional design was employed. The sample comprised 726 university students from Peru (n = 357) and Ecuador (n = 369). Data were analyzed using structural equation modeling (SEM) with maximum likelihood estimation in AMOS. The measurement model demonstrated adequate reliability, as well as convergent and discriminant validity. In the structural model, appreciation of high achievement had a positive and significant effect on punishment for high achievement (β = 0.424, p ≤ 0.001), which in turn positively influenced addictive social media use (β = 0.454, p ≤ 0.001). In contrast, the direct effect of appreciation of high achievement on addictive social media use was not significant (β = 0.084, p = 0.075). However, bootstrapping analysis confirmed a significant indirect effect (β = 0.184, p ≤ 0.001). These findings indicate that high academic achievement elicits ambivalent social responses that shape students’ digital behavior, highlighting punishment for high achievement as a key explanatory mechanism. This study contributes to the literature by proposing an integrative model of social ambivalence surrounding academic excellence, linking recognition, social sanction, and addictive social media use in university contexts.

Abstract: The rise of Internet of Things (IoT) technologies in smart solar energy systems has presented a serious cybersecurity issue, especially with the close interdependence between cyber infrastructure and physical functions. Traditional intrusion detection systems (IDS) are mainly based on the network level of abnormalities and do not consider the system level effects leading to poor mitigation. In this paper, we suggest an AI-based cyber-physical cybersecurity architecture, which combines an intrusion detection model in the form of CNNLSTM and a mitigation mechanism based on reinforcement learning. A decision fusion layer is added to integrate cyber threats scores with photovoltaic system conditions to respond in context. Experimental analysis on real-world data sets indicates that the framework proposed has a 98.3% detection rate, 0.97 F1-score, and a lower false positive of 0.02, which performs better than the current approaches by up to 2.1% accuracy. The framework enhances energy stability at the system level up to 96.8% and recovery time is shortened by about 58 percent when the system is under attack. Moreover, the model has a strong performance of more than 96% accuracy even when there is a high attack intensity situation. These results highlight the value of cyber intelligence and integration of physical system awareness. The proposed framework is holistic and realistic to provide a guarantee of the safety of smart solar energy systems to ensure not only high detection but also stability of the operations in real-life scenarios.

Abstract: Solar tracking systems using IoTs are being implemented in efforts to enhance solar photovoltaic efficiency, but are susceptible to unsecure communications, data modification and low flexibility to changing environmental scenarios. Current solutions usually focus on either control optimization or communication security but not both resulting in sub optimal system reliability. This research paper aims to propose a unified blockchain-based network-adaptive architecture to offer secure communication and enhance real-time operation of a solar tracking in distributed IoT systems. It provides a new Adaptive Blockchain-Assisted Secure Tracking (L-ABAST) model that includes blockchain based validation, trust-based decision making, and learning-based control. This system was tested using large and standard datasets and was offered on various environment and network conditions. The analysis of the performance was conducted based on the statistical values including the mean and standard deviation, the confidence interval, and the significance test. The proposed model demonstrated a reduction in tracking error of 5.8° to 1.9° and energy output was 39.0 % higher than that of conventional systems. The accuracy detected 94.7% and the false positive was 0.05. Latency was restricted to 108 ms and this indicates effective trade-off between security and efficiency. The proposed system provides a flexible, reliable, and safe framework to intelligent solar tracking, which can be successfully used in future IoT-enabled photovoltaic systems.

Abstract: This research paper explores the effect of AI-enabled social media marketing practices on the online buying intentions of Generation Z. The authors gathered 314 answers, with only 299 responses being usable for analysis. The research population includes Gen Z individuals who utilize social media. The research paper uses a quantitative, descriptive method and convenience sampling. Structural Equation Modelling (SEM) through SPSS software was used to test hypotheses. The study findings demonstrate that using AI-based social media applications in marketing positively influences intention to purchase online and perceived personalization. However, perceived personalization negatively affects the intention to make an unexpected purchase. This suggests that AI-generated content focused on personalization may be perceived as highly targeted and can raise concerns about privacy and manipulation, ultimately reducing the likelihood of purchase. Concerning the dimensions, the purchase intention is significantly influenced by the interaction, ad. Personalization and AI-driven e-WOM. At the same time, for entertainment and trendiness, the effects are null and negative, respectively. Privacy concerns and AI trust are of particular significance in enhancing the model's explanatory power, with AI trust being a powerful predictor of effective personalization. AI-driven social marketing remains an effective marketing tool, particularly for Gen Z, when it is interactive, socially innovative, and perceived as trustworthy and protective of privacy.

Abstract: Wind turbine power generation for any nation is always a priority since its impact on power grids and imbalance the sustainability parameters. This research work captures the variation in wind resource variability by implementing advanced machine learning models for short-term power forecasting using SCADA data. Here, a fresh framework was developed to focus on parameters such as aerodynamic behavior, temporal patterns, and overall regime. This information was fed to the model to analyze actual and theoretical power output. With the strong literature study few models were shortlisted such as Extreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), and a novel hybrid stacking ensemble to fulfill the criteria. From the generated data hybrid model was the top choice as its value for RMSE came down to 0.0103, while it moves as high as 0.9992 in case of R². With the need to optimize the present work, multiple algorithms were selected and merged to get the desired output so that accuracy can be maintained without compromising on the grid stability and performance. The models were so selected that the outcome can give lower reliance on carbon-intensive power. This work shows a better data driven based farmwork which only addresses energy forecasting parameters by relating to renewable penetration and sustainable energy systems.

Abstract: AI chatbots are becoming essential within contemporary education, significantly influencing student engagement and learning processes. They provide personalized support by delivering instant resources designed for individual learning styles and needs, enabling students to study subjects at their own pace. Therefore, this study seeks to investigate the impact of AI chatbots on self-reliance, their role in enhancing educational outcomes, and the factors influencing their adoption. A random sample of 384 questionnaires was collected from students enrolled in Jordanian universities. Data were collected through an online survey conducted from June to September 2025. The study's hypotheses were tested using SPSS for regression analyses of sub-hypotheses and PLS-SEM for the primary hypotheses. The results reveal that various dimensions of AI chatbots positively impact both self-reliance and the adoption of these tools, both individually and collectively, suggesting that their effective implementation can enhance learning outcomes and student engagement in educational settings. These results emphasize the significance of AI chatbots in fostering self-reliance and their adoption. The study advises integrating AI tools into the educational process while maintaining a balance with traditional teaching methods, as this approach can enhance learning outcomes and ensure that students benefit from both innovative and established educational strategies.

Abstract: Telecommunications companies manage the digital backbone of the global economy; however, it remains unclear which dimensions of IT governance truly determine their operational success. Therefore, this study aimed to analyze the determining dimensions of IT governance in telecommunications companies operating under the COBIT 2019 framework. Using a quantitative approach, a survey was administered to 241 IT professionals. The results of the regression analysis using Structural Equation Modeling (SEM) reveal that the significant predictors of IT Governance (GOV IT) are, first, Performance (PE), which emerges as the most robust predictor of the model (β = 0.329), followed by Responsibility (RS) with a considerable effect (β = 0.266). Third, Compliance (CO) shows a moderate-to-high effect (β = 0.256), while Human Behavior (HB) exhibits a moderate effect (β = 0.188). Finally, the predictor with the least predictive power is Strategy (ST) (β = 0.083). These findings suggest that technical (performance), ethical (responsibility), regulatory (compliance), and behavioral (human behavior) factors exert a significant and determining influence on the effectiveness of IT Governance in organizations. Thus, it is concluded that telecommunications companies can build an essential foundation through performance and strategic alignment, thereby driving continuous digital transformation.

Abstract: Zero-day and emerging cyber threats are difficult to detect in digital infrastructure due to evolving attack patterns, class imbalances, and the limited interpretability of traditional intrusion detection systems (IDSs). As such, the primary objective of this research is to develop an agentic intelligence-driven visual analytics framework for detecting zero-day cyberthreats in digital infrastructure as a basis for future economic development. The framework combines long short-term memory (LSTM)-based temporal modeling with threat severity scoring, entropy-based uncertainty estimation, and embedding-based structural analysis to move beyond conventional class-label prediction. It further incorporates orchestrated decision support and human-in-the-loop reasoning to help analysts interpret suspicious traffic, prioritize high-risk events, and validate uncertain cases. Experimental results on NSL-KDD, where R2L and U2R were treated as unseen zero-day attacks, achieved 96.40% accuracy. Additional cross-dataset evaluation on ToN-IoT, using MITM, Backdoor, and Ransomware as unseen attacks, confirmed the framework’s applicability to modern IoT environments. The findings demonstrate that integrating machine intelligence with analyst-guided reasoning improves zero-day detection, interpretability, and adaptive cyberthreat analysis.

Abstract: The evolution of audit technology is changing the profession, but studies conducted in Kuwait and the GCC have remained ad hoc, tending to treat traditional audit software and AI analytics as two separate entities rather than as a unified technological trajectory. This research addresses that gap by analysing the interaction of these tools as a sequential process of improving audit quality. A mixed-methods design was employed, combining a structured survey of 219 auditors and finance executives with semi-structured interviews. Analysis proceeded through hierarchical descriptive tabulation, factor validation testing, structural equation modelling (PLS-SEM), bootstrapped mediation and moderation testing, incremental value analysis, and multi-group comparison. The findings demonstrate that audit software and AI analytics are complementary rather than competing technologies: software improves process efficiency and compliance foundations, while AI analytics extends these foundations through predictive risk capabilities and fraud detection maturity. Auditor expertise, targeted training, and organisational readiness significantly moderate both pathways. Adoption of both tools in combination produced the strongest gains in audit quality, and multi-group analysis revealed contextual differences across GCC firms. This paper makes three contributions. First, it provides empirical validation of a Sequential Adoption Model, demonstrating that audit software and AI analytics are complementary and sequentially ordered phases of a single audit technology trajectory. Second, it identifies auditor expertise, targeted training, and organisational readiness as key moderators of both pathways, and documents significant contextual differences between Kuwaiti and broader GCC firms. Third, it establishes a planning-phase boundary condition: AQF-based evidence from the GCC indicates that the planning dimension (AQF 2) remains the least effectively technology-supported phase even after sequential adoption, pointing to a phase-specific gap whose mechanisms are examined in complementary conceptual work.

Abstract: Accurate Remaining Useful Life (RUL) prediction is essential for reliable battery management and cost-effective maintenance in lithium-ion energy storage applications. This study proposes a leakage-safe learning framework for battery RUL prediction from numerical cycling features, emphasizing realistic generalization to unseen batteries through group-aware splitting. The core contribution is a proper out-of-fold (OOF) stacking ensemble trained under GroupKFold to prevent information leakage across samples from the same battery while enabling an effective meta-learner to combine diverse learners. Experiments are conducted on a public Battery RUL dataset from Kaggle, and performance is evaluated using complementary regression metrics (R², MAE, RMSE) and robust percentage errors (sMAPE, WAPE). Results show that the proposed stacking approach (OOF GroupKFold with Meta-XGBoost) achieves the best overall performance (R² = 0.999550, MAE = 5.297907, RMSE = 6.834146, sMAPE = 3.080569, WAPE = 0.958618), outperforming strong baselines including XGBoost and Random Forest. These findings confirm that leakage-safe group-aware stacking can significantly enhance accuracy and stability for battery RUL prediction in practical deployment settings.

Abstract: BC innovation is increasingly recognized in the accounting domain as a transformative technology with significant implications for accounting practices. This research aims to examine the potential digital transformations in Saudi businesses regarding the integration and adoption of BC technology into accounting practices. To analyze the associations between variables, the research design employs a quantitative method using PLS-SEM by Smart-PLS. Data were collected from a convenience sample of 208 respondents employing a standardized questionnaire. The results reveal that budgeting processes, transaction recording, and accounting adjustments, as well as BC’s awareness, are positively influenced by the adoption of BC innovation. The findings, however, indicate that accounting operations should further support their innovative capabilities to remain aligned with rapid technological advancements. These results contribute to the existence of a stock of knowledge on the use of BC in accounting and auditing, also delivering empirical evidence of its benefits in an emerging nation The study has practical implications for accountants, businesses, policymakers, and researchers. The results can help businesses in efficiently gaining an advantage from BC developments to promote their accounting processes. Policymakers can adopt these outcomes to establish supporting regulations and frameworks that motivate the adoption and integration of BC innovation in the domain.

Abstract: This study develops an explainable ensemble machine learning framework for sustainable credit risk assessment in peer-to-business (P2B) lending, a rapidly expanding FinTech model that enhances access to financing for small and medium-sized enterprises (SMEs). The increasing reliance on algorithmic decision-making underscores the need for transparent and interpretable credit evaluation, particularly in environments characterized by information asymmetry and reliance on borrower-provided disclosures. To address these challenges, a heterogeneous ensemble model is proposed, integrating Random Forest (RF), Light Gradient Boosting Machine (LGBM), and deep learning classifiers within a soft-voting architecture. Feature selection and class balancing are guided by RF importance scores and resampling techniques, resulting in a compact and interpretable 12-feature set comprising pricing, contractual, and transaction-level variables derived from borrower disclosures. Using real-world transaction-level data from a UK platform, the proposed model achieves improved predictive performance (ROC-AUC = 0.767) compared to a neural network baseline (ROC-AUC = 0.717) under severe class imbalance. SHAP-based explainability analysis identifies Maturity Days, Annualised Gross Yield, Advance Rate, and Discount Rate as the most influential predictors, highlighting how disclosed information is translated into pricing and contractual terms in digital lending markets. The findings demonstrate that disclosure-informed features can enhance both predictive accuracy and interpretability, supporting more transparent, robust, and accountable credit risk assessment in FinTech lending environments.

Abstract: Recent developments in single-cell and high-throughput sequencing technologies have enabled the collection of genomic data at multiple time points; therefore, the investigation of dynamic processes and the observation of disease prognosis, progression, and therapy effects are now possible. Yet, challenges, including biological variability, high dimensionality, noise, and temporal resolution in these datasets, need to be addressed. This review aims to categorize the machine learning (ML) methods used to create models that analyse the temporal dynamics of genomic data. In addition, the best way to apply either the classical or modern deep learning approaches to analyse temporal genomic and multi-omics datasets is being discussed. ML methods used in time-series genomics analysis have been organized by data type and modeling approach to compare their efficiency in handling sparsity and nonlinear temporal relationships, thereby helping to understand the biological mechanisms underlying them. Furthermore, we focus on the recent frameworks that combine temporal learning models with long-read and single-cell sequencing. While ML provides robust tools to reveal temporal dynamics, data standardization, scalability, interpretability, and benchmarking remain challenges. We summarize best practices for model evaluation and outline future directions, including multi-omics integration, interpretable artificial intelligence, and large-scale, reproducible benchmarks. Via combining computational ingenuity and biological insight, we enable a deep understanding of the complexity of biological processes and pave the way for applying precision medicine through ML-based time-series genomics.

Abstract: Spectral methods are among the most powerful approaches to recovering global structure in network data, but the vast majority of existing theory is developed for crisp graphs, in which vertices and ties observed are unambiguous. Both the vertices and edges are uncertain in terms of existence, strength, and reliability in many network-science contexts, such as social-media interaction data (friendship networks etc), trust networks, recommendation systems and partially observed relational databases. This research introduces a mathematically principled spectral theory for undirected fuzzy graphs, which is based on vertex-normalized fuzzy adjacency matrix and the corresponding Laplacian operators. The construction has symmetry, obeys the fuzzy constraint μ_ij≤min(σ_i,σ_j) and when all vertices belong to one membership class reduces down to classical weighted-graph adjacency and Laplacian. Under this paradigm several structural outcomes are proven: the fuzzy Laplacian is positive semidefinite, its nullity matches the number of connected components of the support graph, and the second Laplacian eigenvalue measures fuzzy algebraic connectivity, along with explicit upper and lower spectral bounds. A cut-based inequality and perturbation theorem are subsequently obtained to characterize community separability, as well as robustness against membership noise. The theory is exemplified on a six-node fuzzy network, and then the new method is applied to the well-known Zachary karate club benchmark after equitable fuzzification of vertices and edges. In the empirical study, we present that with this method the canonical split is recovered with 94.12 % accuracy, a fuzzy modularity of 0.3645 is produced and bridge-like actors are identified based on Perron fuzzy centrality score along with very high stability under multiplicative perturbations of edge memberships. The paper thus provides a rigorous spectral toolkit for uncertainty-aware network analysis, filling an important bridge between fuzzy mathematics and modern data and network science.

Abstract: 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.

Abstract: The increasing complexity and frequency of cyberattacks have made accurate and reliable intrusion detection systems (IDSs) essential for modern network security. In this study, an explainable triple-hybrid deep learning framework is proposed for binary intrusion detection using the CICIDS2017 dataset. The proposed architecture integrates three complementary branches, namely a Transformer encoder, a bidirectional long short-term memory (BiLSTM) network, and a multilayer perceptron (MLP), to capture global feature interactions, sequential dependencies, and nonlinear discriminative patterns from network traffic data. To enhance adaptive representation learning, the framework employs a branch-gating mechanism and a fusion-gating module before final classification. The model was evaluated in a Benign-versus-Attack setting using 5-fold stratified cross-validation and assessed through accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices, and explainability analysis. Experimental results showed strong and stable performance across folds, with a mean validation accuracy of 97.18%, a best-fold accuracy of 97.43%, and a mean ROC-AUC of 0.9975. LIME-based explanations further improved transparency, confirming the framework as an effective and interpretable solution for binary intrusion detection.

Abstract: This study investigates the determinants of online learning application effectiveness in Vietnam using a structural equation modeling (SEM) approach. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and extended with psychological constructs, the proposed model integrates performance expectancy, effort expectancy, social influence, facilitating conditions, learning habits, intrinsic motivation, technostress, and feeling of isolation. Data were collected from 501 online learners and analyzed using SPSS and AMOS. The results reveal that performance expectancy, effort expectancy, learning habits, intrinsic motivation, and technostress significantly influence behavioral intention, while social influence and facilitating conditions are not significant. Behavioral intention strongly predicts actual usage behavior. Notably, technostress emerges as the most influential factor, suggesting that technological pressure may act as both a barrier and a driver of engagement depending on user adaptation. The study contributes to the literature by integrating enabling and inhibiting factors into a unified framework and offers practical implications for improving online learning systems in developing countries.

Abstract: With the constantly growing interaction of artificial intelligence and green financial technology, the emerging possibilities introduced new possibilities to optimize organizational performance in the energy sector as business entities were being pressured to be more financially efficient and strive towards environmental sustainability. Although earlier research explored the role of digital technologies in financial services and sustainable business practices, limited empirical attention was given to how artificial intelligence adoption affects Green FinTech performance and whether sustainable innovation mediates this relationship. The paper has explored the effects of the introduction of artificial intelligence on Green FinTech performance in the energy industry and explored the mediating effect of sustainable innovation. The research design used was quantitative and data was gathered in firms working in the energy industry. The relationships proposed were analyzed with the help of suitable statistical tools to evaluate the direct impact of artificial intelligence implementation on Green FinTech performance and the indirect impact via sustainable innovation. The results showed that the adoption of artificial intelligence significantly improved Green FinTech performance. The findings showed that sustainable innovation acted as a mediating factor, enabling firms to transform artificial intelligence capabilities into more efficient, environmentally sustainable, and cost-effective FinTech practices. These results indicated that energy sector firms that adopted artificial intelligence and invested in sustainable innovation were better positioned to improve Green FinTech performance and achieve long-term competitive advantages. The study offered important implications for managers, investors, and decision-makers seeking to accelerate the digital and sustainable transformation of the energy sector.

Abstract: This paper will explore how, as part of Orange Jordan, one of the largest telecommunication firms in the Jordanian market, the Marketing Analytics Capability (MAC) affects the Quality of Decision-Making (DMQ). Precisely, it examines the implication of the major dimensions of the marketing analytics capability such as data assimilation, analytics tools, analytical capabilities, and data-oriented culture with how organisational analytics usage is a moderating aspect to the quality of managerial decision-making. A stratified random sample of managerial and operational levels was chosen on the full-time employees of Orange Jordan which were asked to respond to a structured questionnaire. Data gathering was done between August and November 2024 and it was analyzed through partial least squares structural equation modeling (PLS-SEM). The empirical evidence shows that marketing analytics capability positively and significantly affects the quality of decision making. Nevertheless, the modifying impact of the analytics use was also identified to be of no significance. The study is also part of the growing literature on analytics-based marketing since it presents empirical data on an emergent market setting. Practically, the results are important to telecommunications managers since they highlight the need to intensify marketing analytics ability to improve decision-making performance. These endeavors aid the creation of more sustainable marketing approaches by way of better resource allocation based on data, better organizational learning, and strategizing, therefore, adding to the larger aims of sustainable development connected to innovation, digital infrastructure, and responsible organizational approaches.

Abstract: Unemployment is a major problem that needs to be addressed immediately by the government. One Employment Data shows that the unemployment rate in Indonesia for the 2025 period reached 7,278,307, of which 67 percent are filled by Generation Z who have just graduated. The future of the next generation of the Indonesian nation is at stake due to the difficulty in obtaining decent work. This study aims to determine the influence of perceived employability and psychological capital on career decisions (work volition) of college students. The selection of colleges in Bali as the research location is based on the fact that job competition in Bali is increasing, as evidenced by the continued increase in job seekers from outside Bali competing with Balinese residents in the labor market. College students are currently in a transition phase from the academic world to the professional world, so this study will be a relevant reference in their career decisions. Research method: Based on data from the Central Statistics Agency, the population of college students in Bali is 150,382. Using the proportional random sampling technique, the research sample size was 399 respondents. Data analysis used the Structural Equation Modeling-Partial Least Squares method, which consists of research instrument testing, structural model testing, and research hypothesis testing. The results show that perceived employability has a positive effect on digital literacy and work volition, psychological capital has a positive effect on digital literacy and work volition. Digital literacy, besides being found to have a positive influence on work volition and decent work, is also able to mediate the relationship between employability and psychological capital on work volition. The research results provide strategic recommendations for universities and the government in addressing the domino effects caused by unemployment, such as poverty, health problems, and unethical behavior.

Abstract: The ongoing wave of digitalization has transformed the competitive landscape of the banking industry. By embracing the Stimulus-Organism-Response (S-O-R) framework as a theoretical lens, this research explores how technology-driven marketing practices, i.e., mobile banking and social media marketing (SMM), shape customer satisfaction and loyalty. Data were obtained from 179 banking clients from Morocco via an online questionnaire, while PLS-SEM technique was applied to test the conceptual model. Results indicate that both digital marketing practises positively affect customer satisfaction, which is shown to be a strong predictor of loyalty. In contrast, direct impact on loyalty was found to be insignificant. The model explains 59.2% and 57.7% of the variance in satisfaction and loyalty respectively, showing considerable predictive f. Contributions to existing literature include the application of the S-O-R model. Practically, results imply that banks should invest in improvement of their mobile applications and use social media in an interactive way to increase customer satisfaction and loyalty.

Abstract: Stroke is a non-communicable disease that affects the brain and can lead to motor, sensory, and cognitive disabilities due to damage such as cerebral small vessel disease. The stroke, which consists of ischemic and hemorrhagic types, is one of the leading causes of death and disability worldwide. In this study, we model the risk of both ischemic and hemorrhagic stroke using a nonparametric binary logistic regression approach based on the least squares spline estimator. This model flexibly estimates odds ratios, enabling it to capture nonlinear relationships between risk factors and stroke occurrence. The analysis was conducted using secondary data from Dr. Drs. M. Hatta Brain Hospital in 2023, with risk factors including LDL, uric acid, triglycerides, blood glucose, sodium, and age. The results show that for age more than and equal to 50 years, both LDL and triglyceride levels more than and equal to 125 mg/dL, and blood glucose levels across all spline segments are associated with increased odds of ischemic stroke. Meanwhile, uric acid and sodium levels across all spline segments show a decreased tendency toward ischemic stroke risk. The model achieved an accuracy and AUC of 87.14% and 0.892, respectively, for in-sample data, and 90% and 0.911 for out-sample data. These findings demonstrate that spline-based nonparametric binary logistic regression provides a more flexible and accurate approach for modeling stroke risk. This study also supports the achievement of the SDGs by contributing to data-driven early detection and stroke prevention strategies.

Abstract: This study identifies the factors affecting user satisfaction with AI solutions in the Jordanian health sector, especially in the cities of Amman and Zarqa. It focuses on the usage of ChatGPT, which has emerged as one of the most common AI technologies. A descriptive analytical approach was adopted. Questionnaires were distributed to 584 ChatGPT users, and 541 valid questionnaires were completed for analysis. The results indicated a positive impact of anthropomorphism, perceived interactivity, electronic word of mouth, and performance expectations on ChatGPT user satisfaction in the Jordanian health sector. The study also found a negative impact of perceived trust, facilitating conditions, and perceived technological anxiety on user satisfaction to use ChatGPT in the Jordanian health sector. The results extend the UTAUT and advance understanding about the use of AI in the health sector. They inform both academics and practitioners on how to ensure user satisfaction with AI in the health sector.

Abstract: This paper provides a bibliometric analysis of AI and cloud computing in product revolution and development. It attempts to make sense of its evolution and styles of research to date. A systematic analysis based on the PRISMA protocol was regulated over journals rescued from Web of Science Core Current Contents Connect (CCC) and Web of Science Core Collection (WoSCC) related to 118 articles published between 2009 and 2025. Python-based Bibliometric performance through analytical tools and visualization using VOSviewer statistics and science mapping. The findings reveal an authoritative yearly growth trend and rising academic attraction in the subject, and especially since 2018. The big players were universal in co-occurrence analysis of keywords. AI-focused Product Design, Cloud-based Collaborative engineering, digital twin, data analytics, generative AI and open innovation ecosystems. Furthermore, temporal overlay analysis shows a progressive transition from the pioneer articles of digital infrastructures to intelligent innovation systems. The results increase existing research on innovation management literature by aiming at how AI technologies, cloud programs and digital innovation ecosystems converge, revealing rich avenues for further cross-disciplinary research.

Abstract: Telematics-based Internet of Things networks operate under severe energy constraints while facing continuous mobility, bursty traffic, and delay-sensitive data delivery. Routing inefficiencies in such environments directly shorten network lifetime and degrade service reliability. Most existing energy-aware routing approaches depend on reinforcement learning or centralized optimization, which introduce computational overhead, slow adaptation, and limited practicality in highly dynamic telematics scenarios. This study proposes a lightweight, mobility-aware energy-efficient routing framework that relies on localized decision metrics instead of learning-driven control. The routing strategy jointly considers residual energy, link stability, traffic load, and buffer occupancy to adapt paths in real time without global network state. Simulation results obtained over 30 independent runs show that the proposed framework improves packet delivery ratio by approximately 6-9% and extends network lifetime by about 12% compared to RLEAFS, LEA-RPL, and genetic algorithm–based routing schemes. End-to-end delay and routing control overhead are reduced by up to 15% under high- mobility and traffic- load conditions. The available solution provides a scalable and implementable routing system to the energy limited telematics IoT networks that strike a balance between efficiency, stability and operational simplicity.

Abstract: This paper examines the mediating role of Corporate Social Responsibility (CSR) in the relationship between digital marketing tools, specifically Social Media and Electronic Customer Relationship Management (Social CRM), and two strategic marketing outcomes: Digital Marketing Capabilities and Innovation Orientation in digital services, within the Jordanian service industry. A quantitative, cross-sectional survey design was employed. The sample comprised 157 managers and employees working in marketing departments of Jordanian service companies, including banking, telecommunications, and travel agencies. A structured questionnaire was developed using validated scales from previous literature. The hypothesized relationships, including mediation effects, were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart-PLS software. The findings reveal that both Social Media and Social CRM have significant positive effects on Digital Marketing Capabilities and Innovation Orientation. Furthermore, CSR partially mediates the relationship between digital marketing tools (Social Media and Social CRM) and Digital Marketing Capabilities. However, CSR does not mediate the effect of digital marketing tools on Innovation Orientation. These results suggest that CSR serves as an important mechanism for building marketing capabilities in the digital environment but is not a prerequisite for fostering innovation. This study contributes to the marketing literature as one of the first empirical investigations to examine the mediating role of CSR in the link between digital marketing tools and strategic marketing outcomes within the Jordanian context. It extends prior research by integrating Social CRM and digital marketing tools into a unified mediation model.

Abstract: Generalized linear mixed models (GLMMs) have become a fundamental statistical framework for analyzing hierarchical and correlated data in epidemiology and related quantitative fields. By combining non-Gaussian response distributions with random effects and structured dependence, GLMMs allow researchers to represent complex data structures arising from spatial, temporal, and multilevel processes. Over the past two decades, numerous methodological extensions have been introduced, including developments in spatial modeling, hierarchical structures, and computational inference. Despite this growth, these contributions are often presented as separate methodological advances, making it difficult to understand how they relate within the broader architecture of the GLMM framework. This study addresses this issue through a systematic review of methodological developments in GLMM-based epidemiological research. Thirty-nine studies published between 2001 and 2025 were examined to identify recurring modeling patterns and structural extensions of the GLMM framework. Based on this synthesis, a four-layer mathematical taxonomy is proposed that organizes GLMM methodology according to probabilistic specification, hierarchical structure, structured dependence, and inferential strategy. The results indicate that most innovations arise from structural modifications within these layers rather than from entirely new modeling paradigms, providing a clearer perspective on methodological diversity in the GLMM literature.

Abstract: Virtual reality (VR) has emerged as a powerful tool for cultural heritage preservation; however, most existing VR museum studies rely on descriptive statistics for evaluation, lacking the predictive depth that data-driven approaches can offer. This paper addresses this gap by integrating machine learning (ML) techniques into the evaluation of a VR museum dedicated to traditional Jordanian women’s clothing, focusing on Bedouin garments from Wadi Rum, Ma’an, and Petra. Seven historically accurate 3D costumes were reconstructed using Blender based on ethnographic references and expert consultations, integrated into a navigable museum environment in Unity, and deployed on the Meta Quest 2 headset. A total of 150 university students participated in the evaluation, providing demographic data and Likert-scale ratings across four experience dimensions: engagement, cultural understanding, navigation intuitiveness, and visual realism. Six ML classifiers, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, and Logistic Regression, were trained using 5-fold stratified cross-validation to predict user satisfaction. Logistic Regression achieved the best performance (accuracy: 84.67%, F1-score: 0.7884), while Decision Tree performed the lowest (accuracy: 71.33%, F1-score: 0.5947). Random Forest feature importance analysis revealed that cultural understanding (0.189), visual realism (0.173), and navigation (0.168) were the strongest predictors, whereas demographic factors such as gender (0.023) and prior VR experience (0.034) had minimal influence. K-Means clustering identified three distinct user profiles: Passive Observers (27.3%), and two Selective Explorer groups (39.3% and 33.3%) with divergent interaction patterns. These results demonstrate that ML can effectively predict and profile user satisfaction in VR heritage environments, offering data-driven insights for designing adaptive and personalized virtual museums.

Abstract: The growing adoption of artificial intelligence (AI) in production systems has revolutionized quality assurance processes. Fault detection capability is a critical enabler of production performance and process variability reduction. This study explores the relationship between AI integration, represented by AI-supported decision-making, real-time processing, fault anticipation, and data integration, and production quality assurance. It also assesses the mediating role of fault detection capability in industrial production environments. A quantitative approach was used to gather data from staff in production, quality assurance, process monitoring, maintenance, and operational control units of industrial companies. Participants included production managers, quality assurance managers, process engineers, maintenance supervisors, data analysts, and operational specialists with experience in production process management and digital technologies. The results show that enhancing AI integration across the four dimensions positively impacts production quality assurance by increasing process visibility, facilitating real-time decisions, anticipating faults, and integrating production data across functional activities. The research also reveals that fault detection capability acts as a key mediator, allowing production units to detect anomalies early, prevent quality issues, and boost the stability of production outcomes. It concludes that AI integration positively impacts production quality assurance both directly and indirectly through fault detection capability, helping industrial organizations achieve higher quality standards, process reliability, and production control.

Abstract: The swift integration of intelligent technologies at higher institutions of learning has greatly improved efficiency of operations, learning conditions, and administration. But the evolution of cybersecurity issues presented by the integration of Internet of Things (IoT) devices, cloud-based systems, and interconnected systems has complicated and shifted the complexity of these issues, which old and standard methods of periodic auditing cannot effectively tackle. The present paper suggests an AI-based Cyber Risk Auditing Framework (AI-CRAF) of continuous and real-time risk assessment in smart educational campuses. The framework combines sophisticated machine learning and deep learning models to identify the threats, anomalies, and dynamic risk assessment with references to vulnerabilities to the system and their potential impact. The suggested model is tested on a big data set of 1,247,334 events within 12 months that contains various attack cases and regular operations. The experimental values indicate a high detection accuracy of 96.2 %, a true detection rate of 94.8 %, a low false positive rate of 2.1 % and an AUC-ROC value of 0.978. Also, the framework shortens 97.1 the time spent on an average incident response by 41.9 minutes to 1.2 minutes as compared to conventional methods.

Abstract: EEG is an important source of information about the activity of the brain and finds broad applications in brain-computer interface and cognitive state analysis. This study focuses on the classification of eye states using EEG signals recorded from multiple scalp electrodes. Several machine learning methods were used to differentiate between eye open and eye closed conditions using EEG recordings. The models were tested using various classification measures to have a complete measure of predictive power. The K-Nearest Neighbors model is the highest performing in terms of classification with an accuracy of 0.9609 and ROC-AUC of 0.9927. CatBoost, XGBoost, and Random Forest were also found to be good predictors by ensemble methods. To enhance the transparency of the model, explainable artificial intelligence (XAI) models, including SHAP analysis, Partial Dependence Plot (PDP) and Individual Conditional Expectation Plot (ICE) plots were used to analyze features contribution and model performance. The results of the interpretability show that the signals of certain EEG electrodes, especially in frontal and occipital brain areas are important in the process of classifying the eye state. The results indicate that machine learning with explainable algorithms can be successfully used to assist EEG-based eye state detection in addition to providing insightful information on the decision-making of the model.

Abstract: Marketing has shifted from transactional promotion to value-oriented engagement, where sustainability and environmental responsibility increasingly influence consumer decision-making; Artificial Intelligence (AI)-driven sustainable marketing leverages informatics, data intelligence, and ethical analytics to coordinate marketing approaches with green consumer expectations and long-term engagement. However, the effectiveness of AI-driven sustainable marketing is constrained by context-specific consumer perceptions and variations in ethical information governance practices. To observe how AI-driven sustainable marketing mechanisms influence green consumer engagement through informatics-based insights, eco-oriented personalization, green information transparency, and responsible data utilization. The data were collected using a structured questionnaire from environmentally conscious consumers; out of 587 responses, 465 valid samples were gathered. The conceptual framework includes AI-enabled sustainability cues, green information transparency, ethical data governance, and perceived environmental value as predictors of green consumer engagement, analyzed using reliability analysis, exploratory factor analysis (EFA), multiple regression, and structural relationship modelling via IBM SPSS and AMOS. The outcomes show that all AI-driven sustainable marketing factors, perceived environmental value, and green trust significantly influence green consumer engagement, with effects ranging from β = 0.203–0.319, t = 2.54–3.13, p < 0.011.AI-driven sustainable marketing, supported by informatics and ethical analytics, significantly enhances green consumer engagement and provides organizations with a strategic approach to integrate environmental responsibility with competitive marketing performance.

Abstract: Security and privacy are essential concerns for IoMT. Whilst existent research has investigated the associations between security and privacy with intention to use and acceptance, determinants related to formation of security and privacy for IoMT still remain largely unexplored. This study is an examination of security and privacy determinants of IoMT based upon UTAUT. Criterion sampling was employed in gathering 528 IoMT user datasets and revealed the following results: A) six determinants (authorization, availability, non-repudiation, data integrity, effort expectancy and performance expectancy) exerted an important and explicit positive effect upon the usage and acceptance of IoMT; B) six determinants (secondary usage, collection, improper access, errors, awareness and control) exerted a significant and direct negative effect upon acceptance and use of IoMT; C) three determinants (confidentiality, facilitating conditions and social influence) insignificantly affected behavioral intention in users within Jordan regarding use of IoMT. The integrated form of the model does predict 62.5% for privacy and security for IoMT.

Abstract: 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.

Abstract: This paper examines the complex interdependencies between the big data analytics capabilities, competitive intelligence, organizational agility, and organizational outcomes in the telecommunication industry in Jordan. Using a quantitative design and data gathered through 350 participants and processed by SmartPLS. The results show that big data analytics capabilities influence organizational outcomes in a positive manner both directly and indirectly via competitive intelligence. Although competitive intelligence does not influence organizational outcomes directly, it mediates the relationship between the big data analytics capabilities and organizational outcomes significantly. Moreover, organizational agility is identified to moderate positively between big data analytics capabilities and organizational outcomes, which underscores its significant contribution to boosting the success of analytics. These findings present important theoretical insights by clarifying the processes by which big data analytics competencies are transformed to generate better organizational performance, as well as have practical implications to telecommunication companies seeking to better exploit their data assets.

Abstract: With the rapid change in climate globally ebbing away at the earth’s resources, the vast array of businesses around the world is becoming more environmentally conscious. This hasn’t left the Telecommunications industry behind. This paper investigates the role of Green Supply Chain Management (GSCM) and the Internet of Things (IoT) in making telecommunication organizations technologically innovative. Going beyond the traditional supply chain activities, GSCM, through Internal Environmental Management (IEM), Green Purchasing (GP), Eco-Design & Packaging (ED), and Cooperation with Customers (CC) have been posited to add value to the process and operations in the whole supply chain which enhances organization’s performance to innovate technologically in terms of less waste production, reduction in manufacturing costs, reuse, and recycling of products, positive image building, the efficiency of assets and greater customer satisfaction. The hypothesized relationships have been tested on a sample of Telecommunication firms in a developing country. Based on 238 responses from telecom professionals analyzed through PLS-SEM in SmartPLS, results confirm that the GSCM practices positively impact the firm’s capability to innovate technologically, and the IoT exercises a moderating role in the relationship of the two main variables. The impact of GSCM robustly increases the technological innovation rate of firms in the presence of IoT. Finally, it is recommended that there is a need to have an appropriate policy to support the right implementation and adherence to the green supply chain as a form of strategy under IoT role toward greater competitive advantages beyond what technological innovation provides.

Abstract: Digital communication and automated control of smart grid networks are increasingly dependent on digital networks, which are susceptible to cyber-attacks that can be extended to physical disturbances of the power system. The current intrusion detection techniques are mostly based on the patterns of cyber traffic and can hardly differentiate between malicious and legitimate changes of operating variations in the dynamic grid settings. This paper presents a machine learning model grounded on energy to combine cyber-layer measurements with energy-space dynamics in a single learning representation. The method suggested uses physical consistency constraints in the classification process, which is not the case of the conventional cyber-only detectors. A simulation based smart grid dataset of 10,000 samples, and four operating classes. The proposed framework has a final detection accuracy equal to 95.8% and an F1-score of 0.95, which is 2-5% points higher than the typical baseline methods. The ablation analysis also proves the fact that the energy-domain features and limitations imposed by physical plausibility can add up to a significant increase of performance. The research results suggest that informed learning that considers physical considerations is an effective and viable way of achieving credible cyber security enhancement in smart grid networks.

Abstract: The rising malware threats in the past years are due to the proliferation of smart devices and resource-constrained systems, such as the Internet of Things (IoT) and mobile devices. This phenomenon creates great difficulties for conventional defense mechanisms, which frequently lag behind because of changes in the malware landscape. In this work, a lightweight and powerful network combining a single-head attention mechanism with LSTM or GRU for this task, based on the CIC-MalMem-2022 dataset, is introduced. The focus is on learning temporal features retrieved from memory data, considering model efficiency for resource-constrained devices. Results indicate that the generated model can retain an accuracy of 92%, and it can save training time by 30%, which is highly beneficial for time-critical tasks and efficient resource usage in real-life applications. This model contributes to the enhancement of cybersecurity by offering good practice against the growing range of new threats in today's technology-advanced environments.

Abstract: This study provides a bibliometric review of the legal-tech literature on AI-enabled administrative contracting, and it is focused on research published between 2021 and 2026 in the Web of Science Core Collection database. It used a structured search strategy and a screened dataset of 484 publications, the analysis maps publication patterns, source distributions, and citation performance. Bibliometric mapping in VOSviewer software was used to visualize the field’s conceptual structure through keyword co-occurrence networks and to examine international collaboration patterns. The results identify four thematic clusters that organize the literature: (1) machine learning for integrity and risk analysis (including collusion detection), (2) NLP and deep learning for contract text interpretation, (3) digital procurement ecosystems connecting e-procurement, big data and blockchain-based traceability, and (4) performance-oriented contract management and automation. The findings highlight an increasingly interdisciplinary field where technical innovation converges with governance, accountability and the public interest.

Abstract: Forecasting train passenger demand is essential for supporting strategic decision-making and optimizing resource allocation in the transportation industry. This study aimed to develop a predictive model for the number of passengers on the Surabaya-Jakarta train route using the Extreme Gradient Boosting (XGBoost) algorithm. Owing to the non-linear nature of the historical count time series data (January 2019 to December 2024) and the significant disruptive impact of the COVID-19 pandemic, traditional linear models such as ARIMA were considered less appropriate. To optimize the XGBoost model, we comparatively evaluated two distinct input approaches: significant Partial Autocorrelation Function (PACF) lag and the sliding window method. Hyperparameter tuning was conducted via grid search, and the models were rigorously evaluated using Time Series Cross-Validation to prevent information leakage. Furthermore, the study compared recursive and direct multi-step forecasting strategies to project passenger volumes for the next 12 months. The analysis revealed that the sliding window approach with a window size of 4 yielded the best performance on the testing data, achieving a Mean Absolute Percentage Error (MAPE) of 10.94% and significantly outperforming the PACF lag method, which was prone to overfitting. Additionally, recursive forecasting is more rational and effective at capturing complex seasonal patterns and short-term fluctuations than direct forecasting. The final 12-month projection for 2025 indicates clear seasonal fluctuations, with a low in March (10,319 passengers) and a peak in November (20,932 passengers), providing a data-driven foundation for the train company to proactively optimize capacity planning, operational scheduling, and human resource management in the future.

Abstract: This study investigated the role of artificial intelligence (AI) in improving knowledge management and organizational learning within Saudi Arabian educational institutions, with particular emphasis on the empirical statistical validation of the relationships between AI implementation and organizational outcomes. A descriptive-analytical approach was employed utilizing a stratified random sample of 213 faculty members from five Saudi government universities: Taiba University, King Faisal University, King Khalid University, Al-Jouf University, and Qassim University. Data were collected through a validated 20-item questionnaire (α = 0.978) measuring four dimensions: AI implementation in knowledge management, AI's role in organizational learning, implementation challenges, and effectiveness. Statistical analyses included descriptive statistics, Pearson correlations, multiple regression analysis, one-way ANOVA, independent samples t-tests, and structural equation modeling to test the hypothesized relationships. The results revealed high levels of AI implementation in knowledge management (M = 3.89, SD = 0.67) and organizational learning (M = 3.79, SD = 0.75). Multiple regression analysis demonstrated that AI implementation significantly predicted organizational learning outcomes (β = 0.624, p < 0.001, R² = 0.389). Significant differences in AI implementation effectiveness were found based on institutional affiliation (F(4,208) = 4.87, p = 0.001, η² = 0.086) and academic rank (F(3,209) = 3.42, p = 0.018, η² = 0.047). The primary challenges identified, lack of AI expertise (M = 3.64), staff resistance (M = 3.58), and infrastructure limitations (M = 3.55), were found to significantly moderate the relationship between AI and knowledge management (interaction effect: β = -0.187, p = 0.008). This study contributes robust empirical evidence to the limited body of quantitative research on AI integration in Saudi higher education, providing statistically validated insights for policymakers and institutional leaders working to advance Vision 2030 objectives.

Abstract: This paper discusses the effects of artificial intelligence (AI) on business practices in general, as well as its specific impacts on economic growth, environmental performance, and social performance at organizational and societal levels. A survey was conducted using a structured questionnaire administered to 250 randomly selected small and medium-sized enterprises (SMEs) operating in various key sectors in Saudi Arabia. The quantitative data were evaluated using partial least squares structural equation modeling (PLS-SEM). The findings suggest that, economically, AI contributes to productivity and efficiency gains, cost savings, and new business model opportunities, while also yielding ambivalent outcomes such as increased market concentration and job displacement in certain areas. Environmentally, AI aids in optimization, energy efficiency, criticality analysis, resource allocation, and predictive analytics aimed at sustainability, though it also presents mixed effects, including higher energy consumption, increased e-waste, and transparency deficits. Socially, AI helps augment the workforce, personalize customer experiences, and enhance cooperation and interaction, alongside mixed impacts such as growing inequality, job displacement, and the deskilling of low- and middle-skilled roles. This paper concludes that AI-based innovations constitute a key factor driving radical shifts in the economic, environmental, and social dimensions of institutions and society within the Saudi context. This study advances the literature by providing a novel framework that integrates artificial intelligence with economic, environmental, and social sustainable development, offering policymakers and SME managers in Saudi Arabia a pathway to adapt to sustainable development.

Abstract: The rapid digital transformation of healthcare organizations has increased the use of managerial applications to improve leadership effectiveness and service quality. This study examines the effect of managerial app use on the performance of nurse managers, with user satisfaction and technostress proposed as potential mediating variables. Grounded in the DeLone and McLean Information System Success Model and Task–Technology Fit theory, a quantitative cross-sectional design was employed. Data were collected from 308 nurse managers across five government hospitals in North Sumatra, Indonesia, and analyzed using SEM-PLS. The findings indicate that managerial app use has a significant positive effect on individual performance, user satisfaction, and technostress. Both user satisfaction and technostress also positively influence performance; however, neither variable mediates the relationship between system use and performance. These results suggest that performance improvements are driven primarily by direct system utilization. Notably, technostress may function as a challenge stressor that enhances performance under manageable conditions. Overall, the study emphasizes the strategic role of digital systems in strengthening managerial effectiveness, decision-making quality, and organizational outcomes. It also offers practical insights for hospital leaders managing digital transformation initiatives and optimizing human–technology interaction in complex healthcare environments to support sustainable healthcare performance.

Abstract: The rapid expansion of renewable energy systems has contributed to the extensive use of cloud-based monitoring applications that receive and process massive amounts of operational telemetry information. Nevertheless, the dynamic and complex nature of renewable energy systems makes it difficult to detect abnormal behaviors that can adversely impact the reliability and efficiency of energy generation. This paper proposes an artificial intelligence-based anomaly detection system designed for cloud-controlled renewable energy systems. The proposed solution integrates deep representation learning models with a contextual drift-based anomaly scoring mechanism to identify anomalous operation patterns in multivariate renewable energy telemetry data. Normal system behavior is learned using an autoencoder-based architecture, and the proposed Context-Aware Residual Drift Scoring (CARDS) algorithm enhances anomaly detection by identifying contextual anomalies in system performance. Experimental evaluation was conducted on multivariate renewable energy telemetry data under cross-validation setups consistent with those used for comparison models. The proposed framework achieved a detection accuracy of 97.4% and an ROC–AUC score of 0.98, outperforming baseline algorithms such as Isolation Forest, LSTM-based detection, and Transformer-based models. These findings demonstrate that the suggested AI-based system offers a viable and scalable approach to enhancing reliability and operational intelligence in cloud-based renewable energy monitoring systems.

Abstract: Malaria remains one of the most dangerous infectious diseases in tropical and sub-Saharan regions, requiring efficient, accurate, and interpretable diagnostic systems. This paper presents a hybrid system that combines machine learning and deep learning techniques to classify malaria-infected blood cells from microscopic images. The approach integrates artificial features such as texture features from the Gray Level Co-occurrence Matrix (GLCM), morphological features, and statistical features with deep features extracted from pre-trained convolutional neural networks like ResNet50 and VGG16. After preprocessing steps, including gamma correction, HSV color space transformation, and contrast-limited adaptive histogram equalization (CLAHE), the cells were segmented using local entropy thresholding and filtered to remove noise smaller than 125 pixels. The combined handcrafted and deep features were classified using Support Vector Machine (SVM), Random Forest (RF), and XGBoost classifiers, evaluated through 10-fold cross-validation. Results demonstrate that the hybrid model significantly improves performance over methods based on single features. The XGBoost classifier achieved the highest accuracy at 95.4%, with precision of 95.1%, recall of 94.8%, and an AUC of 0.99.

Abstract: It is essential to early detect skin cancer, especially melanoma since it significantly affects patients' survival. However, an experienced dermatologist still has difficulty distinguishing between a healthy lesion and a tumor; there are fine distinctions between benign and malignant tumors. In this research, we have developed a combined deep learning system, which uses Convolutional Neural Networks (CNNs) in combination with Vision Transformers (ViTs), to develop an automated diagnostic tool for detecting skin cancer based upon skin images. In developing this hybrid model, we utilized EfficientNet-B4 as a local feature extractor and a Vision Transformer as a global feature extractor. To combine these two feature extractors, we employed a special fusion module. This module used concatenation to merge the feature sets from each branch into a single layer and then processed them through a multi-layer perception. We were able to train and test the model using the ISIC 2020 data set, which contains 33,126 skin images, with successively improved training methodologies using a technique called 5-fold cross-validation. On the test set, the proposed hybrid model had an accuracy of 95.4%, a sensitivity of 90.7%, a specificity of 95.1%, and an AUC-ROC of 0.982. The above results show that the hybrid CNN-Transformer design performed better than the previous state-of-the-art EfficientNet-B4 + Attention model (accuracy of 92.1%) and had significant increases in both sensitivity (+1.8%) and specificity (+1.0%). These findings indicate that a hybrid CNN-Transformer design can provide a hopeful means of assisting physicians in diagnosing skin cancer, thus potentially improving physician decision making.

Abstract: Although destination publicity is widely used in tourism marketing, there is a lack of studies specifically examining its role in national park tourism. Thus, this study looks into the effects of publicity and electronic word, of, mouth (e-WOM) on tourists’ visit intention to Wakatobi National Park, an internationally recognized marine destination known for its commitment to sustainable tourism. Grounded in the stimulus–organism–response (S, O, R) framework, the proposed model includes five constructs: publicity, destination personality, e-WOM, destination preference, and visit intention. Responses from 208 potential tourists were gathered through an online survey and analysed through structural equation modelling. The findings indicate that publicity enhances destination personality significantly, which further enhances the intention to visit. E-WOM has a direct impact on destination preference and visit intention. Destination preference has no impact on visit intention, implying that the impact of affective evaluation may not be enough to trigger sustainable visits without the presence of cognitions and social validation. The findings emphasize the strategic application of non, commercial communication, particularly credibility, based publicity and peer, generated content, to generate symbolic, ethical, and desirable destination images. This work contributes to the literature on sustainable tourism by addressing how authenticity, based destination branding and community stories can contribute to the creation of environmentally friendly tourist conduct. The practical contributions indicate that the tourism authorities should spend on publicity and online interaction to co, construct destination personality as per the spirit of conservation.

Abstract: The growing adoption of communication and automation technology in smart energy grids has greatly amplified their vulnerability to cyber-attacks, and therefore, intrusion detection in a timely and reliable manner has become a pressing necessity. In this context, this paper will seek to fill this gap by introducing a hybrid fuzzy-deep learning framework, which combines uncertainty-sensitive feature modeling with spatiotemporal deep representation learning. A convolutional recurrent neural network is used to encode coordinated spatial and temporal patterns of attacks in the form of graded representations of raw grid and network measurements using the help of fuzzy logic. With this integration it is possible to have end to end learning in nonstationary and ambiguous operating conditions. The proposed framework achieves a detection accuracy of 97.6%, an F1-score of 0.96, and a false positive rate of 3.1%, outperforming representative machine learning and deep learning baselines. In addition, the average detection latency of 29.6 ms confirms its suitability for real-time monitoring applications. The primary value of the work is its ability to show that the systematic combination of the uncertainty modeling based on fuzzy with deep spatiotemporal learning can considerably increase the reliability of detection and the feasibility of operation, which can provide a viable way to achieve the goal of resilience in cybersecurity of smart energy grids.