Abstract:In an uncertain economic climate, a large global retailer used AI-powered spend intelligence to move $220 million from indirect operational costs toward high-impact R&D. In a difficult recession, this decisive step boosted…
ed revenue by 11%, demonstrating the transformative impact of effective capital management. This achievement contrasts with "spend blindness," where industry studies show most financial leaders struggle to link expenditure patterns to strategic growth outcomes and resort to reactive cost-cutting. This study addresses this crucial gap. A thorough mixed-methods approach including a global survey of 400 CFOs, longitudinal case studies of ten multinational organizations, and advanced predictive modeling substantiated a new paradigm. Research shows that companies that understand AI-driven spend orchestration develop 7.3% faster than competitors. This premium comes from a 37% improvement in the Growth Efficiency Ratio (GER), a critical statistic for translating savings into innovation, and 5.8 times more strategic investment opportunities than standard financial approaches allow. The Spend Intelligence Quotient (SIQ), a groundbreaking statistic that assesses financial agility through integrated spend monitoring, predictive analytics, and rapid capital reallocation, is key to this advantage. This paper introduces the empirically based Spend Orchestration Framework and the requirements for the 2025 AI Finance Stack to obtain SIQ >80, the empirically proven threshold for sustainable competitive advantage. The message is clear: finance chiefs must go beyond oversight. Today's CFO may use predictive contracting and algorithmic governance to turn spend data into strategic leverage, ensure resilience, and capture disproportionate value in.
Abstract:The digital technology revolution has catalyzed a fundamental paradigmatic transformation in the global education ecosystem, creating a new era where technology integration has become a strategic imperative in effective…
and sustainable learning management. One of the most disruptive technological innovations that has become the primary focus in educational transformation is Artificial Intelligence (AI), which has demonstrated extraordinary potential in revolutionizing various aspects of learning management through sophisticated adaptive capabilities, deep learning personalization, and comprehensive administrative automation. This research aims to comprehensively and systematically examine the multidimensional role of AI in enhancing the quality of learning management in the digital era, with in-depth analytical focus on three fundamental pillars: strategic learning planning, pedagogical process implementation, and learning outcome evaluation. The research methodology employs a rigorous Systematic Literature Review (SLR) approach, analyzing over 150 high-quality scientific articles from leading national and international journals published between 2014-2024, utilizing a thematic synthesis framework to identify emergent patterns and significant trends in AI implementation in education. Research findings reveal that AI implementation in learning management produces multifaceted transformative impacts, including significant improvements in pedagogical decision-making quality through predictive analytics and data-driven insights, strengthening the strategic role of teachers as adaptive and responsive learning facilitators, and expanding democratic learning accessibility through adaptive learning systems and intelligent tutoring systems technologies. AI has also proven effective in optimizing educational resource allocation, enhancing student engagement through intelligent gamification, and facilitating inclusive and equitable learning.
However, this research also identifies complex challenges that must be addressed in AI implementation, including significant technological infrastructure gaps particularly in remote areas, limitations in educators' digital literacy affecting technology adoption, and ethics and data privacy issues requiring comprehensive regulatory frameworks. The implications of this research emphasize the need for a holistic and structured approach in integrating AI into educational systems, considering technological, pedagogical, and socio-cultural aspects in a balanced manner to achieve sustainable and positively impactful educational transformation.
Abstract:Digital transformation has fundamentally reshaped healthcare service delivery worldwide, particularly in tertiary hospitals that rely heavily on integrated digital systems such as Electronic Health Records (EHRs), telemedicine,…
dicine, clinical decision support systems, and data-driven healthcare technologies. Despite rapid technological advancement, limited empirical evidence explains how healthcare professionals’ digital competence contributes to healthcare service performance within the context of healthcare transformation in Saudi Arabia. Previous studies have primarily focused on technological adoption or technical outcomes, while the psychological and organizational mechanisms underlying digital healthcare performance remain underexplored. Drawing upon the Job Demands–Resources (JD-R) Theory and Resource-Based View (RBV), this study investigates the influence of Digital Health Competence (DHC) on Healthcare Service Performance (HSP), examining the mediating role of Work Engagement (WE) and the moderating role of Organizational Support (OS). This study employed a quantitative cross-sectional explanatory design using Structural Equation Modeling–Partial Least Squares (SEM-PLS). Data were collected from 312 healthcare professionals at King Abdullah Medical City (KAMC), Saudi Arabia, selected through stratified random sampling. The study included physicians, nurses, pharmacists, and allied healthcare professionals actively utilizing digital healthcare systems in clinical practice. Measurement instruments were adapted from internationally validated scales, including the European Digital Competence Framework for Health Professionals, Utrecht Work Engagement Scale (UWES), and Perceived Organizational Support Scale. Data analysis included assessment of the measurement model, structural model evaluation, mediation analysis, moderation analysis, effect size (f²), predictive relevance (Q²), and model fit indices. The findings demonstrated that Digital Health Competence had a positive and significant effect on Healthcare Service Performance (β = 0.328, p < 0.001) and Work Engagement (β = 0.541, p < 0.001). Work Engagement significantly influenced Healthcare Service Performance (β = 0.462, p < 0.001) and partially mediated the relationship between Digital Health Competence and Healthcare Service Performance (β = 0.250, p < 0.001). In addition, Organizational Support significantly moderated the relationship between Digital Health Competence and Work Engagement (β = 0.217, p < 0.001). The structural model demonstrated substantial explanatory power (R² HSP = 0.683) and satisfactory predictive relevance. This study contributes theoretically by extending the application of JD-R Theory and RBV within the context of digital healthcare transformation in tertiary hospitals. The study proposes an integrated model demonstrating that digital competence functions not only as a technical capability but also as a strategic personal resource that enhances work engagement and healthcare service quality. Practically, the findings emphasize the importance of strengthening digital competency development, supportive organizational climates, and adaptive digital infrastructures to improve healthcare professionals’ performance and accelerate sustainable healthcare transformation in Saudi Arabia.
Abstract:This study examines consumer behavior towards sustainable products, specifically investigating the influence of environmental attitudes and green product attributes on green purchase intention among Tanimbar Ikat Weaving…
consumers in Tanimbar Islands Regency. Employing a quantitative approach with Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis through SmartPLS software, data were collected from 385 respondents selected via purposive sampling based on consumers who knew and had purchased Tanimbar woven products. The research tested direct relationships and moderating effects of premium price, education, and gender variables. Results revealed that environmental attitudes (β = 0.349; t = 5.306; p = 0.000) and green product attributes (β = 0.207; t = 3.014; p = 0.003) significantly and positively influence green purchase intention, with the model explaining 60.7% of the variance (R² = 0.607) and demonstrating strong predictive relevance (Q² = 0.539). However, the three moderating variables—premium price, education, and gender—did not significantly strengthen or weaken these relationships, indicating that environmental consciousness and product perception remain dominant factors regardless of demographic or economic considerations. These findings provide practical implications for traditional craft entrepreneurs in developing sustainability-based marketing strategies and empowering the local creative economy, while contributing to the sustainable development goals through cultural preservation and environmental conservation in Indonesia's eastern region.
Abstract:This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications,…
applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.
Abstract:This study examines the influence of firm ethicality, green marketing, and corporate social responsibility (CSR) on brand loyalty toward Tokopedia, it also explores the moderating roles of brand reputation, environmental…
concern, and social influence. A survey of 56 Tokopedia users was analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that firm ethicality significantly and positively influenced brand loyalty. In contrast, green marketing and CSR did not show significant effects, nor did any of the hypothesized moderating relationships. Interestingly, social influence, although not proposed as a direct predicator, demonstrated a significant direct effect on brand loyalty, highlighting the roles of normative and informational influence in e-commerce. Assessment of the measurement model confirmed satisfactory construct reliability, convergent validity, and discriminant validity. The structural model exhibited predictive relevance. The findings indicate that corporate ethics and social influence are key factors in fostering loyalty within Indonesia’s digital marketplace.
Abstract:This study examines the implementation of customer-driven excellence strategy in the Umrah travel industry in Depok City by investigating the relationships between service quality, brand credibility, perceived value, and…
customer-driven excellence outcomes. Using a quantitative research approach with Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 105 customers who have utilized Umrah travel services within the past two years. The measurement model demonstrated excellent reliability and validity, with all constructs meeting stringent psychometric criteria for convergent and discriminant validity. The structural model reveals that all three hypotheses are strongly supported, with brand credibility emerging as the most influential factor (β = 0.398, p < 0.001), followed by perceived value (β = 0.356, p < 0.001) and service quality (β = 0.285, p < 0.001). The integrated model explains 74.2% of variance in customer-driven excellence, demonstrating strong explanatory power and large predictive relevance (Q² = 0.486). These findings provide empirical evidence that successful customer-driven excellence strategy requires integrated approaches prioritizing brand credibility development while simultaneously enhancing perceived value and service quality. The study offers valuable insights for Umrah travel operators seeking to optimize competitive advantage through comprehensive customer excellence strategies that address both functional and emotional dimensions of religious tourism services.
Abstract:Education is the backbone and main tool for a country’s development, because it emphasizes the quality of human resources (HR). Education aims to brighten the nation’s future with an intelligent and characterful generation.…
ration. Learning, as an interaction between teachers, students, and learning resources, is a systematic process involving interrelated components to achieve optimal results. This literature review aims to provide a comprehensive understanding of learning theory and learning in educational practice. In this research, the researcher used a literature review method which aims to identify, find and analyze documents related to the research problem. The results of the research show that this consistency has a crucial role in creating a predictive learning environment, facilitating the continuity of curriculum and strategy development. teaching, facilitates the transfer of learning, and forms a strong foundation for an effective and sustainable learning experience for students. Consistency in applying learning and learning theories, such as behaviorism, cognitivism, constructivism, and humanism, helps educators in designing structured and directed learning. This allows students to develop a deep understanding of the subject matter and skills that are important for learning. In conclusion, the consistency of learning and learning theories in education is an important factor for achieving educational goals. By consistently applying learning theories, an intelligent and characterful generation can be created that is ready to build a brighter future for the nation.
Abstract:Stunting remains a crucial public health issue in Indonesia as it has multidimensional impacts on physical growth, cognitive development, productivity, and the long-term quality of human resources. This study aims to analyze…
lyze the potential application of predictive Artificial Intelligence (AI)-based Wearable Health Technology in preventing child stunting, with an emphasis on the integration of technology into the health system, socio-cultural interrelations, and ethical dimensions that need to be anticipated. The research employed a qualitative approach through a systematic literature review, analysis of national health policies, and examination of reports on the implementation of digital health technologies related to child health. The results indicate that wearable devices are effective in recording health indicators in real-time, while predictive AI algorithms enable early detection of stunting risks, allowing interventions to be carried out quickly, accurately, and in a personalized manner. However, the success of implementation is highly dependent on the support of the digital health ecosystem, including adaptive regulations, infrastructure readiness, digital literacy among communities, as well as multi-stakeholder collaboration involving the government, medical professionals, the technology industry, and families. This study affirms that the integration of Wearable Health Technology and predictive AI is not merely an additional tool in medical services, but rather a representation of a health system transformation towards a more preventive, predictive, precise, and participatory paradigm. Thus, the utilization of evidence-based digital innovations has the potential to strengthen the effectiveness of stunting prevention programs in Indonesia while accelerating the achievement of national health development targets within the context of technological disruption and global dynamics.
Abstract:Abstract: Motorcycle sales are one of the key indicators of growth in Indonesia’s automotive industry. CV. Honda Karya Utama, as an authorized Honda motorcycle dealer, faces challenges due to unstable monthly sales fluctuations.…
ctuations. This uncertainty complicates stock planning, marketing strategy formulation, and may lead to potential losses caused by overstocking or understocking. To address these issues, this study aims to analyze the relationship between time and sales volume and to develop a predictive model that can assist management in making more effective business decisions.
The research was conducted using a quantitative method and a machine learning approach by applying a linear regression algorithm, implemented through the Python programming language and a MySQL database. The dataset used consists of monthly sales data over one year, analyzed to predict future sales trends. The results show that the linear regression algorithm can predict sales trends with a good level of accuracy, achieving evaluation values of MAPE 1.72%, MSE 1.76%, and RMSE 0.57. The developed model assists management in formulating marketing strategies, optimizing inventory planning, and minimizing financial risks. Therefore, linear regression can serve as an effective analytical tool to support strategic business decision-making at CV. Honda Karya Utama.
Keywords: motorcycle sales; linear regression; prediction; sales analysis
Abstrak: Penjualan sepeda motor merupakan salah satu indikator penting dalam pertumbuhan industri otomotif di Indonesia. CV. Honda Karya Utama sebagai dealer resmi sepeda motor Honda menghadapi tantangan berupa fluktuasi penjualan yang tidak stabil setiap bulannya. Ketidakpastian ini menyebabkan kesulitan dalam perencanaan stok, penentuan strategi pemasaran, serta berpotensi menimbulkan kerugian akibat kelebihan atau kekurangan persediaan. Untuk mengatasi permasalahan tersebut, penelitian ini bertujuan untuk menganalisis hubungan antara waktu dan jumlah penjualan serta membangun model prediksi yang dapat membantu manajemen dalam pengambilan keputusan bisnis yang lebih efektif. Penelitian dilakukan dengan metode kuantitatif dan pendekatan machine learning menggunakan algoritma regresi linier, dengan implementasi berbasis bahasa pemrograman Python dan basis data MySQL. Data yang digunakan berupa data penjualan bulanan selama satu tahun, yang kemudian dianalisis untuk memprediksi tren penjualan pada periode berikutnya. Hasil penelitian menunjukkan bahwa regresi linier mampu memprediksi tren penjualan dengan tingkat akurasi yang baik, dengan nilai evaluasi MAPE sebesar 1,72%, MSE 1,76%, dan RMSE 0,57. Model ini membantu manajemen dalam menyusun strategi pemasaran, mengoptimalkan perencanaan stok, serta meminimalkan risiko kerugian. Dengan demikian, regresi linier dapat menjadi alat bantu yang efektif dalam mendukung keputusan bisnis di CV. Honda Karya Utama.
Kata kunci: penjualan sepeda motor; regresi linier; prediksi; analisis penjualan