Journal of Business and Management Studies
The Journal of Business and Management Studies (ISSN: 2709-0876) is a double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. It covers the latest developments in the broad areas of business and management. The journal offers readers free access to all new research issues relevant to business and management. While the journal strives […] Read More.
Al-Kindi Center for Research and Development
Influencer Credibility and Product–Influencer Congruence: Reassessing Their Roles in Shaping Purchase Intentions in Bangladesh’s F-Commerce
With the advent of social media live streaming and specially Facebook live, Bangladesh online market is extensively leaning towards influencer marketing without a clear knowledge of the critical mechanism of persuasion. A country where most internet traffic is dictated by Facebook, marketers are finding it appropriate to engage with customers using live streaming facilities. The study aims to understand the interplay among various source credibility variables such as attractiveness, trustworthiness and expertise with probable influence of product-influencer fit. It has been observed that, aligning with classic source credibility theory influencer’s attractiveness and expertise significantly influence consumer purchase attitude. Trustworthiness as a mediator has been found to have mediated attractiveness in consumer purchase intention. Interestingly, the study has found no perceived influence of product-influencer fit in moderating credibility dimensions and consumers’ purchase intent.
Social media influencer, F-Commerce, Persuasion, Source Credibility, Attractiveness, Expertise, Trustworthiness, Product-influencer fit, Match-up hypothesis.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Harnessing Artificial Intelligence and Big Data Analytics to Enhance Premium Optimization and Utilization Efficiency in Health Insurance Systems
The growing complexity of healthcare systems requires intelligent and data driven approaches to ensure fair, efficient, and sustainable insurance operations. This study titled Harnessing Artificial Intelligence and Big Data Analytics to Enhance Premium Optimization and Utilization Efficiency in Health Insurance Systems explores how modern technologies such as artificial intelligence, machine learning, and predictive analytics can transform the design and management of health insurance programs. By processing large volumes of health records, demographic information, clinical data, and behavioral indicators, artificial intelligence models can identify risk patterns, forecast medical costs, and develop personalized premium structures that reflect true risk levels. Big Data Analytics supports continuous monitoring of claims, detects irregular activities to prevent fraud, and improves resource allocation within healthcare provider networks. The research applies both quantitative and qualitative methods to evaluate how artificial intelligence and data analytics improve pricing accuracy, reduce administrative delays, and enhance the overall efficiency of insurance systems. Real world examples from the United States health insurance market illustrate how these technologies increase transparency, improve profitability, and promote equitable access to healthcare. Predictive utilization models also allow insurers to identify high risk groups and introduce early intervention strategies that lower long term expenses. The study concludes that the integration of artificial intelligence and Big Data creates a smarter, fairer, and more adaptive health insurance environment that benefits both providers and policyholders.
Artificial Intelligence (AI),Health Insurance, Premium Optimization, Utilization Efficiency, Predictive Modeling, Risk Assessment, Claims Management, Healthcare Economics
AI-Augmented Predictive Quality Control in Additive Manufacturing Supply Chains
Additive manufacturing (AM) has transformed the present-day supply chains with on-demand manufacturing, adaptive design, and digital inventory management. The distributed and heterogeneous nature of AM networks, however, induces inherent problems with stable product quality across distributed networks. Reactive and inspection-based quality control practices common with traditional quality control prove insufficient to handle variability in processes, material variability, and machine variability characteristic of AM networks. This paper proposes a conceptual framework of AI-augmented predictive quality control (PQC) applicable to additive manufacturing supply chains. The framework uncovers multi-faceted components: in-situ monitoring-based real-time data capture, AI-based analytics for defect prediction and anomaly recognition, decision support systems for adaptive intervention, and closed-loop continuous feedback facilities aided with digital twins. With the help of machine learning, deep learning, and reinforcement learning-based strategies, predictive frameworks are able to forecast defects, reduce scrap to a minimum, and transform supply chains with increased resiliency. The paper also elaborates on the theoretical benefit of AI-augmented PQC, including improved traceability, economy of costs, and increased congruity with just-in-time logistics. Data heterogeneity, scalability, cybersecurity, and workers' adaptability are also paramount challenges discussed in the paper. The future research directions are also enumerated in terms of hybrid AI-physics models, standardizable datasets, integration with blockchain, and human-AI teaming. This research enlists the transformative potential of AI-augmented PQC in making AM supply chains more reliable and viable.
Additive manufacturing, supply-chain, quality control, AI, blockchain
Real-Time Hybrid Optimization Models for Edge-Based Financial Risk Assessment: Integrating Deep Learning with Adaptive Regression for Low-Latency Decision Making
Financial institutions in the United States face mounting pressure to detect fraud and evaluate transaction risk in real time across highly distributed payment infrastructures, including mobile banking, point-of-sale devices, and ATM networks operating on resource-constrained hardware. Deep learning models deliver strong predictive accuracy for fraud detection but often exceed strict latency budgets when executed at the edge. Conversely, lightweight regression systems provide rapid decision-making but sacrifice accuracy under nonlinear transaction behaviors common in U.S. financial environments. This study develops a real-time hybrid optimization framework that dynamically integrates deep neural inference with adaptive regression, guided by an online controller that monitors latency, compute utilization, and confidence thresholds per transaction. Using a U.S. credit card fraud dataset structured as a streaming financial workload, we benchmark hybrid performance against standalone deep learning and regression baselines under simulated edge CPU and memory constraints. Experiments show that hybrid routing reduces inference latency by up to 55 percent compared to deep learning alone, while preserving high recall on fraudulent cases and improving transaction-level risk detection without overwhelming edge hardware. A latency-accuracy Pareto analysis highlights the system’s ability to maintain regulatory-aligned response times without destabilizing detection performance, demonstrating practical readiness for deployment in payment terminals and digital banking infrastructure. These findings suggest that real-time model optimization can significantly enhance operational compliance, fraud resilience, and customer experience across U.S. financial systems, which are increasingly dependent on edge-based decision intelligence.
Copyright (c) 2025 Journal of Business and Management Studies https://creativecommons.org/licenses/by-nc/4.0
Business Intelligence-Driven Cybersecurity for Operational Excellence: Enhancing Threat Detection, Risk Mitigation, and Decision-Making in Industrial Enterprises
The world of Industry 4.0 is becoming more interconnected, relying increasingly on data-driven processes and making more innovative use of automation by industrial enterprises. Even though these innovations boost productivity and operational visibility, they present vulnerable infrastructures with intricate and emerging cybersecurity threats. This paper examines the integration of business intelligence (BI) systems within cybersecurity activities with the aim of operational excellence. Using the power of BI to analyze and visualize raw security data, organizations can transform it into tangible insights, enabling them to detect threats proactively, mitigate them effectively, and make decisions based on data. The study adopts a mixed-method design that integrates quantitative research of industrial cybersecurity data sets with qualitative information from security experts. The research results show that BI-based cybersecurity systems enhance the speed and accuracy of incident detection, minimize exposure to vulnerabilities, and increase the accuracy of decisions on the managerial and operational levels. Moreover, the analysis presents an idea scheme that shows the synergistic interaction between BI instruments, predictive analytics, and industrial cybersecurity controls. This framework facilitates the creation of stronger and more adaptive defense mechanisms in the industry. Finally, this paper will have scholarly and practical implications on the industry by showing how BI can help transform cybersecurity management into proactive defense instead of reactive defense, guaranteeing operational excellence and sustainability in the digital age.
Applying Deep Learning and Generative AI in US Industrial Manufacturing: Fast-Tracking Prototyping, Managing Export Controls, and Enhancing IP Strategy
The work examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) can be strategically incorporated into the industrial manufacturing of the US to accelerate the process of product prototyping and improve levels of compliance with export control regulations and intellectual property (IP) strategy. With the manufacturing sector swiftly adopting the concept of digital transformation within the Industry 4.0 framework, the concepts of DL and GenAI technologies are reshaping the old forms of work processes, including automating the design-iteration process, cutting the production latency, and improving the process of innovation management. Nonetheless, their fast usage creates new issues with export-controlled technologies and ownership of IP among the outputs of an algorithm. The current paper utilizes a mixed-method design integrating model simulations based on data, case study, and policy framework analysis. Results indicate that GenAI-based prototyping has the potential to cut the design cycle by up to 40 percent while ensuring regulatory compliance by the incorporation of embedded model governance. Moreover, predictive maintenance accuracy can be increased with the help of DL, and patentable innovations can be facilitated using automated differentiation in the design. The paper also establishes new gaps in the policies regarding dual-use AI applications. It prescribes a systemized framework for synchronizing AI innovation with export control compliance and IP protection policies. The findings can be helpful to policy makers, industrial executives, and R&D strategists who want to use generative and deep learning systems responsibly in the US manufacturing environment.
Leadership Strategies for Navigating Regulatory Compliance in Fintech Innovation
The rapid expansion and creativity in fintech have disrupted the financial services space by providing faster, more accessible, and consumer-driven solutions. But the shift comes with growing, difficult and expensive regulatory hurdles that stifle fintech. Good leadership can make or break this regulatory maze in a way that doesn't stifle innovation. This article focuses on the leadership approaches needed to address regulatory compliance in fintech innovation. It stresses the importance of engaging regulators from the very beginning to anticipate, communicate and influence the development of regulation. Secondly, it emphasizes the merit of fostering a strong organizational culture that promotes compliance, ethical behavior, and risk consciousness at all levels within the firm. The adoption of innovative compliance technologies, including automated monitoring and AI/advanced analytics, is also assessed as a way to improve regulatory compliance and be agile. By way of case studies and best practices, the report offers tangible frameworks that fintech CEOs and leadership can utilize to balance innovation and regulatory needs thoroughly. In the end, investing in these leadership levers equips fintech to offset legal and reputational risks, maintain a competitive edge and secure long-term viability in a fast-changing financial world.
Leadership Strategies, Navigating Regulatory Compliance, Fintech Innovation
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Exploring the Impact of Artificial Intelligence on Business Decision-Making Processes
Artificial Intelligence (AI) is significantly reshaping the landscape of business decision-making by offering enhanced speed, precision, and data-driven capabilities. As organizations confront increasingly complex and competitive environments, the adoption of AI has emerged as a strategic imperative for gaining insights, minimizing uncertainty, and improving operational efficiency. This study explores the transformative role of AI in modern decision-making processes across diverse sectors such as finance, healthcare, and retail. Employing a mixed-methods approach, the research integrates qualitative case studies and expert interviews with quantitative survey data to present a comprehensive analysis. Key findings highlight the benefits of AI, including improved predictive analytics, streamlined workflows, and automated operations. Analytical tools like decision tree algorithms and sentiment analysis provide deeper insights into the effectiveness and perception of AI-driven decisions. Despite its advantages, the study also identifies significant challenges to widespread adoption, such as high implementation costs, ethical considerations, and resistance to change. The results underscore the importance of understanding AI’s evolving impact and call for ethically sound strategies to leverage its potential in shaping future-ready business models.
AI. Business Decision-Making, Predictive Analytics, Machine Learning, Ethical AI
Construction of an Adaptive Communication Model for Cross-border E-commerce AI Customer Service under Cultural Context Differences: A Case Study of the Russian Market
Against the backdrop of the rapid expansion of Sino-Russian cross-border e-commerce, AI customer service systems face significant challenges in adapting to high-context cultural environments. Based on Hall’s high-/low-context theory, this study analyzes typical user complaint cases from the Russian market and identifies a structural mismatch between the low-context communication patterns of existing AI customer service systems and the high-context expectations of Russian users. This mismatch manifests in three key dimensions: rigid language, poor contextual interpretation, and an inability to build relationships. To address these issues, this paper proposes an adaptive communication model comprising three integrated layers: a context perception layer, a strategy generation layer, and an interaction execution layer. Driven by a culturally sensitive communication strategy library, the model is designed to shift AI customer service from standardized responses to contextualized communication. By integrating mechanisms such as emotion recognition, indirect intent inference, and relationship-building dialogue, the model aims to enhance user satisfaction and trust in high-context markets. This study offers a structured framework for incorporating cultural theory into the design of cross-border AI customer service systems and suggests directions for future research, including expansion to other high-context regions and multimodal interaction.
Dove’s Real Beauty Campaign in India: Localizing a Global Purpose Brand
The Dove Real Beauty campaign is widely regarded as a landmark in purpose-driven marketing. Globally, it sought to challenge narrow beauty ideals and position Dove as an authentic advocate of inclusivity. This paper examines how the campaign was adapted in India, where cultural pressures around fairness, thinness, and marriageability create a unique context for beauty advertising. Using both original research (interviews, panels, creative exercises) and third-party sources (academic journals, industry trade press, and media coverage), this study investigates how Indian youth interpret the campaign, whether they view Dove as authentic, and why the campaign resonated emotionally but struggled to drive purchase intent. The findings reveal generational differences in reception, highlight tensions between empowerment and commercialism, and suggest that the credibility of purpose-driven campaigns depends on aligning brand messaging with broader corporate practices.
Dove’s Real Beauty Campaign; India; Localizing a Global Purpose Brand
Converging Artificial Intelligence, Big Data, and Biotechnology: A Transformative Framework for Next-Generation Drug Discovery and Global Health Intelligence
Global health and pharmaceutical innovation are undergoing a radical shift due to the convergence of biotechnology, wearable health devices, big data analytics, and artificial intelligence (AI). This article summarizes four core studies: biotechnology-driven strategy models for global competitiveness, wearables for real-time cardiovascular disease monitoring, AI-based drug discovery, and big data frameworks for AMR surveillance. Together, these studies show how biotechnology frameworks, generative algorithms, and predictive analytics may improve preventative healthcare, accelerate drug discovery, and strengthen global health security. Supported by more than 30 scientific publications, this article offers a comprehensive conceptual framework for next-generation pharmaceutical innovation that integrates biomedical data streams, computational intelligence, and biotechnology market strategies. The findings highlight the need for strategic policymaking, ethical governance, and interdisciplinary cooperation to transform technological breakthroughs into long-term global health solutions.
Artificial Intelligence, Big Data Analytics, Biotechnology, Drug Discovery, Wearable Health Devices, Antimicrobial Resistance, Global Health Surveillance
Copyright (c) 2020 https://creativecommons.org/licenses/by-nc/4.0
Human-Centered Visualization Interfaces for Sustainable Supply Chains
The rising complexity of global supply chains has raised the necessity of sustainable practices integrating economic performance and environmental and social accountability. Though data-centric tools provide great insights, the effectiveness of such systems depends upon the exposition of information to the decision-maker. Human-centered visualization interfaces were found to be the enablers par excellence for user augmentation, enhancement of interpretability, and sustainable decision-making among different stakeholders' groups. The paper discusses the conceptual foundations and the design fundamentals of human-centered visualization for sustainable supply chains through usability, accessibility, and transparency. By integrating sustainability metrics such as carbon footprint, energy efficiency, and waste minimization into user-centric dashboards and interactive programs, organizations can bridge the gap between the complexity of the technique and the depth of human understanding. The conceptual framework presented herein depicts the possibility of tailored visualization strategies ranging from dashboards up through immersive AR and VR systems enabling adaptive planning, real-time monitoring, and informed trade-offs within the parameters of the supply chain operation. Though the present work is conceptual and makes no use of empirical data, it provides an established portal for academicians and practitioners for designing interfaces enabling decision-makers, reducing the barriers of cognition, and increasing trust within sustainability reporting. The paper ends with an outline of the future avenues for investigating personalization, multi-modal interaction, and cross-cultural usability for sustainable supply chain management development.
Human-Centered Design, Visualization Interfaces, Sustainable Supply Chains, Interactive Dashboards
Sustainable Business Strategies and Global Competitiveness in the Peruvian Coffee Sector
This study explores the impact of sustainable business practices on the international competitiveness of Peruvian coffee exporters, with a particular focus on small and medium-sized enterprises. As global consumers increasingly prioritize ethical sourcing and environmental responsibility, sustainability has become a critical factor for market differentiation in the coffee industry. Through in-depth analyses of industry practices and pertinent literature, this research examines how economic, environmental, and social sustainability strategies—alongside digital tools such as traceability platforms—enhance export potential. It also identifies key obstacles, including high certification costs, limited access to finance, and technical support gaps, that hinder the widespread implementation of sustainability initiatives. The findings highlight that while sustainability presents real opportunities for branding and value creation, targeted support mechanisms and inclusive strategies are essential for smallholder adoption. This study adds to the literature on sustainable value chains and export development in the Global South, providing practical recommendations for exporters, cooperatives, and policymakers to improve sustainability performance and leverage it for long-term competitive advantage in the global coffee trade.
Sustainable Coffee Trade; Ethical Sourcing; Smallholder Inclusion; Peruvian Coffee Sector
Patient-Centric Marketing and Retention Strategies in Healthcare: A Strategic and Technological Framework
In a time of digital change, more customer expectations, and more competition, healthcare providers are rethinking how they sell their services. This study examines the nascent paradigm of patient-centric marketing and its impact on enhancing retention within both public and commercial healthcare organisations. Traditional healthcare marketing has focused on clinical expertise and the reputation of the institution. However, there is a shift towards methods that put personalisation, engagement, trust, and value co-creation first. Patient-centric marketing aims to regard individuals not solely as users of services but as active players in their healthcare journey. This study examines the theoretical underpinnings of relationship marketing, technological adoption, and service-dominant logic to contextualise patient-centered practices. Based on a thorough analysis of over 50 academic sources and institutional case studies, we pinpoint essential techniques such as Customer Relationship Management (CRM) systems, AI-driven personalisation, mobile health (mHealth), loyalty programs, and behavior-based content distribution. A mixed-methods conceptual framework was employed to integrate qualitative insights and quantitative standards for the assessment of marketing effectiveness. The results indicate that healthcare providers utilising digital engagement tools see retention rates that are up to 30% greater than those of traditional models. Furthermore, personalised and proactive communication enhances patient satisfaction and outcomes. The research also looks closely at ethical issues linked to patient autonomy, digital equity, and data privacy. In conclusion, a strategic roadmap is suggested to assist healthcare organisations in shifting from provider-centric to patient-centric marketing models, with ramifications for policy, technology implementation, and future research.
Chapter 11 Bankruptcy Impact on Earnings Forecasts
Studies in the area of management forecasts contain a common characteristic: they assess voluntary earnings disclosures during normal operating periods, when the incentive structure is generally routine and on-going. This research tests whether voluntary earnings disclosures released during Chapter 11 protection are significantly different from those forecasts released after emergence from Chapter 11 protection. From a biased perspective, results indicate that managers exert greater upward earnings management on the forecast during Chapter 11 protection periods. From an information content perspective, results indicate the presence of information content during both of these periods. When firms are engaged in Chapter 11 protection, the information content is significantly negative indicating that investors have a tendency to discount the forecast. After emergence from Chapter 11, the information content is significantly positive, indicating that investors have a tendency to not discount the forecast. With the number of bankruptcies steadily rising today, these findings have practical implications for not only users of forecast information, but also for the managers generating the forecasts.
Chapter 11, Earnings forecasts, Voluntary disclosures
Performance in higher education institutions: A theoretical approach
The concept of performance is central because it represents an indicator of efficiency, quality and success. It allows us to assess whether objectives are being met, processes are effective and efforts are producing the expected results. Analysing performance allows us to identify areas for improvement in order to develop and remain effective. Through this contribution, we seek to highlight the role that governance can play in the performance of higher education institutions. We have explored the various elements that influence performance, particularly university governance. To understand the relationship between performance and governance, we have examined several theories, namely stakeholder theory, agency theory, transaction cost theory, institutional theory and neo-institutional theory. This has provided a conceptual framework for our study. The aim of our article is to present a review of the literature on the issue of performance in higher education institutions. In the first section, we will present a review of the literature on the concept of performance, and in the second section, its theoretical framework.
| KEYWORDS Keywords: Public performance - Higher education - Indicators – University
The Effect of Value Creation, Value Delivery, and Value Capture on Digital Retail Performance in the Philippine Clothing Industry: Basis for Digital Retail Business Model
The Philippine clothing industry is undergoing a rapid shift toward digital transformation; however, small and medium-sized enterprises (SMEs) face challenges in sustaining their competitiveness. This study examines how value creation, value delivery, and value capture shape digital retail performance, addressing gaps in empirical evidence for emerging markets. The study employed a descriptive–correlational design and quantitative analysis, surveying 94 purposively selected SME respondents in Metro Manila. Data were analyzed using multiple regression and mediation–moderation tests. The theoretical framework integrates Service-Dominant Logic, Value Chain Theory, and Transaction Cost Economics to explain the dynamics of digital business models. Findings reveal that value creation, value delivery, and value capture each have significant positive effects on digital retail performance. Mediation analysis highlights the role of customer satisfaction, loyalty, and perceived quality, while moderation analysis confirms the influence of firm size, capitalization, and technological readiness. The results validate a contextualized Digital Retail Business Model tailored to the Philippine clothing sector. The study contributes to business model innovation literature by providing empirical evidence from a developing economy. Practically, it provides SMEs and policymakers with actionable insights for strengthening digital competitiveness, enhancing customer engagement, and promoting inclusive industry growth.
digital retail performance, value creation, value delivery, value capture, business model innovation, Philippine SMEs
Toward Ethical AI: Strategies for Responsible AI Governance
This paper explores the complex ethical dilemmas associated with AI-driven decision-making, providing a robust framework for the responsible and transparent use of AI. Through comprehensive case studies, it investigates the practical implementations of techniques and best practices for tackling significant concerns such as deepfake manipulation, algorithmic bias, fairness, accountability, transparency, and data protection. These case studies elucidate how firms effectively execute ethical AI governance, emphasizing actionable strategies for risk mitigation and trust enhancement. The study highlights the essential function of corporate leadership in fostering ethical AI cultures and offers evidence-based recommendations for companies operating in this evolving environment. This work addresses significant gaps in existing research, therefore enhancing academic debate and outlining a prospective direction for future study. Ultimately, it enables stakeholders to develop and execute AI systems that protect human values, enhance societal trust, and foster sustainable innovation.
Artificial intelligence; Ethical practices; Case studies
Exploring the Impact of Internal Organizational Factors on Strategic Management Accounting
Strategic Management Accounting (SMA) represents a significant evolution in the field of management accounting, integrating both financial and non-financial information to enhance strategic decision-making. Unlike traditional management accounting, which primarily focuses on cost control and internal reporting, SMA adopts a broader perspective by incorporating external market dynamics and long-term strategic insights. The adoption and effectiveness of SMA techniques, however, are not uniform across organizations. Instead, they are influenced by a variety of internal factors, including organizational strategy, culture, structure, information system quality, and firm size. While prior research has extensively examined external factors such as market competition, regulatory changes, and technological advancements, the role of internal factors remains underexplored. This literature review critically evaluates existing studies on internal organizational factors and their impact on SMA adoption. The findings suggest that firms must align SMA practices with their internal characteristics to optimize decision-making and performance. Additionally, understanding how these internal factors interact can provide deeper insights into SMA implementation. Future research should explore the dynamic relationships between internal organizational factors to develop a more comprehensive framework for SMA adoption.
Strategic Management Accounting, Contingency Theory, Organizational Strategy, Culture, Structure, Information system quality, Internal Factors, Management Accounting
Risk Governance Ecosystem in Information Communications Technology Industry using the Three Lines of Defense (3LOD) Framework Approach in the Philippines
Several frameworks have been developed to ensure organizational compliance and resilience amid the increasing complexity of risks in the Information and Communication Technology (ICT) industry. This study explores the risk governance ecosystem of the Philippine ICT sector through the lens of the Three Lines of Defense (3LOD) framework, which comprises operational management, risk oversight, and independent assurance. These components are known for offering a structured approach to risk management and internal control. Using a quantitative research design, data were collected from 225 ICT professionals in the National Capital Region (NCR), including compliance officers, risk managers, financial executives, and internal auditors. The study examined the extent to which risk culture, regulatory environment, and ICT infrastructure influence the implementation of the 3LOD framework. Descriptive findings revealed that all three predictors were perceived to have a “very high effect,” with ICT infrastructure (????̄ = 3.53) ranking highest in its perceived influence. However, inferential analysis showed that ICT infrastructure had the most significant effect across all 3LOD components operation management (β = 0.4861, p = 0.003), risk management and compliance (β = 0.6449, p < .001), and internal audit (β = 0.7715, p < .001). Regulatory environment had a significant impact on operation management (β = 0.4007, p < .001), but its influence on risk and audit functions was statistically non-significant. Despite being rated highly, risk culture exhibited no significant effect on any governance components, suggesting a gap between cultural awareness and operational integration. The study contributes theoretically by reinforcing socio-technical systems theory, emphasizing that technological systems are foundational to risk governance. It challenges traditional views that prioritize risk culture without operational mechanisms and partially validates institutional theory by showing that regulatory influence is more apparent in external-facing functions. The proposed framework highlights ICT infrastructure as the central enabler, regulatory environment as a partial driver, and risk culture as an underutilized foundational element. Ultimately, this research offers empirical support for enhancing the 3LOD framework’s implementation in the ICT industry. It provides insights for refining governance strategies, informing policy formulation, and guiding future research across diverse ICT environments to promote more resilient and adaptive risk management practices in the Philippines.
Risk Culture, Regulatory Environment, ICT Infrastructure, 3LOD Framework
Ai Revolutionizes Inventory Management at Retail Giants: Examining Walmart’s U.S. Operations
AI has gone from small pilot projects to widespread use in U.S. retail. Walmart is often mentioned as an early adopter of predictive analytics, computer vision, automation, and generative tools to help with inventory management. This article brings together peer-reviewed research on AI-enabled forecasting and inventory management with information from practitioners about Walmart's recent efforts, such as AI assistants for store associates and merchants, RFID-AR item location, digital twins, quality inspection automation, and route/pack optimization. We put Walmart's program in an evidence-based framework that connects data, models, and workflows to key performance indicators (KPIs) that can be measured. We talk about how stockouts and forecast errors have gone down, how excess inventory has gone down, how maintenance has been found sooner, and how task productivity has gone up. Next, we look at ethical and organizational issues like data quality, bias, explainability, human–AI teaming, and change management in a critical way. There are two main parts to the contribution: (i) a practice-oriented synthesis that links academic evidence to a large-scale retail deployment, and (ii) an analytic template for figuring out how valuable AI is in inventory operations. We finish with a research agenda that includes causal evaluation of AI interventions, multi-objective optimization that includes sustainability, and the creation of explainable tools for use by people on the front lines.
Artificial Intelligence, Inventory Management, Walmart, Retail, Predictive Analytics, Machine Learning, Computer Vision, Supply Chain Optimization, Robotics, Customer Experience
Impact of Green Banking Practices on Sustainable Bank Performance: A Study on Private Commercial Banks in Bangladesh
The main objective of this research is to ascertain the impact of green banking practices on sustainable bank performance of private commercial banks in Bangladesh. This explanatory research was conducted in Bangladesh using a survey-based design. Data were collected from 300 employees of private commercial banks through online platforms. A structured questionnaire measured green banking practices and sustainable bank performance. Data were analyzed using Structural Equation Modelling (SEM) with reliability and validity tests. The results show that all five green banking strategies positively and significantly impact the sustainable bank performance. Green Investment shows the most important component among these practices, followed by Green Lending, Green Innovation, Green Processes and Procedures, and Green or Sustainable Products or Services. This study advances understanding of green banking’s role in sustainability, offering empirical evidence for researchers, while guiding banks and policymakers to prioritize green investments, lending, innovation, processes, and products to enhance environmental performance and efficiency.
Assessment the Knowledge, Attitudes, Education, Knowledge, Attitude and Practices Toward Artificial Intelligence
This mixed-methods study investigates the knowledge, attitudes, and implementation perspectives regarding artificial intelligence (AI) in education among key stakeholder groups. Quantitative surveys (n=842) and qualitative interviews (n=48) were conducted with K-12 educators, higher education faculty, educational administrators, students, and parents. Results revealed significant knowledge disparities across stakeholder groups, with higher education faculty demonstrating the highest understanding of AI (M=14.8/20) and parents the lowest (M=9.4/20). Generally positive attitudes toward AI in education were observed (M=3.56/5), though with notable variations; students exhibited the most positive attitudes (M=3.81/5), while parents and K-12 educators reported the lowest (M=3.36/5 and M=3.41/5, respectively). Implementation concerns were highest for privacy protocols (M=4.31/5) and training needs (M=4.14/5). Cluster analysis identified four distinct stakeholder profiles: Enthusiastic Adopters (23.8%), Cautious Implementers (31.5%), Skeptical Observers (18.2%), and Knowledge-Seeking Pragmatists (26.5%). Qualitative findings revealed five themes: Navigating the AI Knowledge Landscape, Balancing Promise and Peril, Resource Realities, Ethical Guardrails, and Evolving Professional Identities. Structural equation modeling demonstrated knowledge as both a direct (β=0.31) and indirect predictor (β=0.18) of implementation perspectives, with attitudes partially mediating this relationship. These findings highlight the need for targeted interventions that address knowledge gaps, ethical concerns, and resource limitations while acknowledging the diverse perspectives of educational stakeholders. The study contributes to theoretical understanding of educational AI adoption by revealing complex interrelationships between knowledge, attitudes, and implementation perspectives that challenge linear models of technological integration.
Artificial Intelligence; Educational Technology; Stakeholder Perspectives; Implementation Barriers; Technological Knowledge; Professional Development; Educational Policy; Mixed-Methods Research; Digital Transformation; Educational Equity
Personalization and Consumer Privacy: Balancing Targeted Marketing with Trust
The high rate of growth of digital commerce has changed the way businesses relate with consumers, making personalization a key strategy to generate engagement, customer satisfaction, and brand loyalty. In the cosmetics e-commerce industry, personalization is based on the analysis of buyer behavior, including product views, adding to carts, and purchase behavior to provide personalized recommendations and marketing campaigns. In this study, the connection among personalization, consumer participation and the concerns on privacy is explored based on the history of eCommerce events in Cosmetics Shop dataset. The quantitative research method was applied, which included the systematic cleaning of data, data transformation, and statistical analysis to reveal patterns and relationships among the user interactions. Visualization software, specifically Tableau, were used to show the results in graphic form, which made it easy to interpret the impact that personalization has on the conversion rates, retention, and the general sales performance. The findings indicate that individualized marketing approaches considerably increase consumer activities and buying intentions, which proves that personalization is an effective business expansion tool in very competitive markets. This study explores consumer concerns regarding the privacy of their data are also on the rise, which should be viewed as a sign that there is an ethical risk of misusing personal data and the international legislation, including GDPR and CCPA. These results illustrate a twofold dilemma to companies: on the one hand, it is necessary to use personalization to achieve maximum performance, and on the other hand, it is important to maintain open, privacy-aware data practices that will lead to long-term consumer loyalty. This study will add value to both the theoretical and practical literature by incorporating technical, behavioral and ethical aspects of personalization. It points out the need to come up with strategies that have a balance between innovation and responsibility, and in the end steer the businesses to sustainable marketing practices. The future research directions involve broadening the analysis to industries, examining the trends in changing consumer expectations and how the new technologies such as AI and block chain can help facilitate secure and ethical personalization. The holistic view would be of great help to researchers, practitioners and policymakers, who are trying to find their way through the changing digital marketing environment.
Personalization, Consumer Engagement, Data Privacy, E-commerce, Ethical Marketing, Digital Transformation
Big Data Analytics in Supply Chain Ecosystems: Emerging Innovations and Strategic Pathways
To improve predictability, hazard of risks, and conservation activities, this investigation aims to explore the function of Big Data Analytics (BDA) in contemporary logistics habitats. The goal of the investigation is to comprehend how BDA facilitates tactical flexibility and informed choices in more intricate international production networks. The study is based on the concept of Industry 4.0 principles and fueled by data control of oversight, chains, which include forecasting, IoT-enabled surveillance, and powered by AI analysis. The versatility of imagery processing, grouping, and machine learning approaches for optimizing supply chains is demonstrated by earlier research in cross-domain areas including medical and farming. This study adopts a mixed-method approach, combining literature synthesis of 52 peer-reviewed articles (2015–2024) with a quantitative analysis of the “Global Supply Chain Data 2023” Kaggle dataset. The dataset comprises supplier lead times, demand variability, and transportation costs for 500 suppliers and 200 customers. Data processing and visualization were conducted using Python (pandas, sklearn) and Tableau to assess predictive accuracy and cost reduction. According to the findings, implementing BDA may improve timely fulfillment from 85% to 93%, lower predicted errors (MAPE) from 18% to 11%, and save storage expenses by 12% and 15%, respectively. These results show how BDA may improve productivity and adaptability, which is consistent with cross-domain data from recognized patterns apps, powered by AI BI instruments, and IoT surveillance. The investigation offers firms useful advice on how BDA principles can increase taking decisions rapidity, savings, along with supply system robustness. The investigation supports prospective creative routes and deployment methods by emphasizing uptake barriers that include expenses lack of skills, along with information reliability. By illustrating the factual effects of BDA in manufacturing habitats and the applicability of cross-domain AI and Internet of Things approaches for enhancing supply network processes, this study adds to the body of study. The study offers a strategic outlook for building intelligent, adaptive, and future-ready supply chains, supporting both academic research and practical decision-making.
Large Facts Coherent; Reservoir Sequence Stewardship; Prophetical Sensible; Decisive Supervisory; Arising Innovations; Statistics-Motivated Environments.
AI-Driven Business Continuity and Disaster Recovery in Financial Services: Minimizing Downtime through Predictive Intelligence and Autonomous Response Systems
In today's increasingly digitized financial environment, even moments of downtime can translate into serious financial losses, regulatory fines, and long-term reputational harm. As institutions become increasingly interconnected and dependent on digital infrastructure, traditional business continuity and disaster recovery processes—often manual, static, and reactive—are insufficient. This article shows how artificial intelligence (AI) is redefining resilience for financial institutions. From early anomaly detection on systems to autonomous decision-making during disasters, AI technologies bring new speeds, new accuracies, and new adaptabilities. The article also provides a practical, step-by-step guide for adopting AI-powered continuity strategies, including predictive analytics, automatic orchestration, and cognitive risk interpretation. It also touches on key performance indicators and why aligning with shifting regulatory expectations is important. For financial institutions looking to keep disruption to a minimum and construct future-proof continuity programs, AI is no longer merely a technological advantage—it's an operational imperative.
AI-Driven Business Continuity and Disaster Recovery
Evaluating the Impact of Ghana's Labelling Law (LI 1541) on the Compliance Behaviours of Packaging Design Professionals
This study tests whether Ghana’s Labelling Law (LI 1541) influences the compliance behaviour of packaging design professionals. A cross-sectional survey of designers in Accra and Kumasi (n = 327) was analysed using confirmatory factor analysis and structural equation modelling (SPSS/AMOS). The measurement model met conventional thresholds (CFI = 0.937; SRMR = 0.066; standardised loadings ≥ 0.60; Cronbach’s α/CR ≥ 0.70). Perceived enforcement and requirements under LI 1541 showed a positive, significant association with compliance behaviour (β = 0.782, p = 0.005; R² = 0.509). Only 19.3% of respondents reported prior training on LI 1541, indicating a capability gap that may blunt compliance even when intent exists. We recommend pairing enforcement with targeted training, designer-facing checklists/templates, and clear guidance on mandatory label elements (e.g., ingredient order, nutrition panels, disposal cues), alongside cost-easing measures for SMEs. The paper contributes Ghana-specific, management-oriented evidence on how a national labelling statute translates into practice within design workflows.
Ghana; Labelling Law (LI 1541); Packaging design professionals; Compliance behaviour; Policy implementation; Designer training
Artificial Intelligence and Machine Learning in Project Management: A Conceptual Framework for Future Integration
Modern projects' increasing complexity and dynamism have accelerated the demand for wiser, data-centric decision-making within project management. Conventional project management techniques, usually based on static planning with human judgment, find it challenging to respond to timeliness uncertainties and high-volume data inputs. Here, Artificial Intelligence (AI) and Machine Learning (ML) come to the forefront as disruptive technologies with the potential to complement project management processes across sectors. The paper delves into the evolving confluence of AI, ML, and project management by suggesting a conceptual blueprint for future integration. Relying on prior literature and existing applications within the field, the analysis pinpoints primary contact points where AI and ML can intervene throughout the project lifecycle—from initiation through to planning, execution, monitoring, and closure. The framework enlists predictive analytics, intelligent automation, natural language processing, and self-learning algorithms to assist functions like resource optimization, risk reduction, dynamic scheduling, and communication with the stakeholder community. The paper, further, discusses the prospective advantages of integration, such as increased accuracy, agility, and efficiency, but also discusses primary inhibitors like information privacy, knowledge gaps, and technology diffusion resistance. By developing a future-ready model based on theoretical understanding as well as practical applicability, this scholar work contributes towards the foundational knowledge of AI/ML-based project management. The suggested blueprint functions as a roadmap for scholars, project managers, and companies seeking to leverage emergent technologies to redefine project success within the digitalized, complex world order of the future.
Artificial Intelligence in Project Management, Machine Learning Integration, Predictive Project Analytics, Intelligent Decision Support, AI-Driven Project Lifecycle
The Level of Knowledge on Financial Literacy among the Members of Selected Cooperatives in China
This study examines the financial literacy of members from certain cooperatives in China, concentrating on their knowledge, attitudes, and behaviors about personal finance and cooperative management. This study recognizes the critical role of financial literacy in improving personal financial well-being and optimizing cooperative efficiency, employing a quantitative approach via a structured survey distributed to cooperative members. The survey evaluates various dimensions of financial literacy, including knowledge, skills, attitudes, and behaviors, to provide a comprehensive understanding of members' competencies. The results demonstrate that people have varied levels of financial literacy. The largest disparities are in how well they understand sophisticated financial ideas and how well they use financial information. The results reveal that things like age, education, and income level can change how much cooperative members know about money and how well they make decisions. These results demonstrate that cooperatives need to offer particular financial education programs to help their members learn how to handle their money better, make better choices, and, in the end, improve the financial health of both the cooperative and its members.The research enhances the current literature by focusing on cooperative members in China, providing empirical information that can inform policymakers, cooperative leaders, and financial educators.
Financial literacy, cooperatives, financial knowledge, financial skills, financial attitudes, financial behaviors, and financial education
Role of Data Analysis and Integration of Artificial Intelligence
This research explores the convergence of data analysis and artificial intelligence integration methodologies, presenting a novel hierarchical fusion framework that significantly enhances analytical capabilities across multiple domains. Our approach combines multimodal data integration, interpretable AI architectures, and cross-domain knowledge transfer to address complex analytical challenges that resist traditional methods. Experimental evaluations demonstrate substantial performance improvements over baseline approaches, with a 19.8% increase in classification accuracy, 54.8% reduction in error rates, and up to 87.3% effectiveness in cross-domain knowledge transfer. The integrated framework demonstrates favorable computational scaling properties (O(n^0.83)) and decreasing per-prediction costs at scale, facilitating deployment in resource-intensive environments. Real-world implementations in healthcare diagnostics, supply chain optimization, and environmental monitoring yielded significant improvements (27.4%, 23.4%, and 18.9% respectively) over existing methodologies. These findings highlight the transformative potential of artificial intelligence for integrated data analysis while identifying important directions for future research, including enhanced privacy preservation techniques, more sophisticated knowledge transfer mechanisms, and deeper integration with emerging computational paradigms. This work contributes to the evolving landscape of AI-augmented scientific discovery by demonstrating how the synthesis of diverse data sources and analytical approaches can reveal insights that remain inaccessible to single-modality methods.
Analysis Integration, Artificial Intelligence, Single-Modality Methods