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
When Fairness Matters: The Moderating Role of Organizational Justice in Strengthening the Impact of HR Practices on Employee Resilience
This study investigates how Human Resource Practices (HRP) influence Employee Resilience (ER) in the Business Process Outsourcing (BPO) industry, highlighting the moderating role of Organizational Justice (OJ). A quantitative design was employed using a survey of 523 BPO employees in the National Capital Region. Data were analyzed through Structural Equation Modeling (SEM) and Moderation Analysis to determine the direct and interaction effects. Results show that HRP significantly promotes ER, and this relationship becomes stronger when employees perceive fairness in organizational processes. The findings underscore the critical role of integrating fair and transparent HR systems to improve workforce resilience in high-pressure service environments, offering practical value for HR leaders aiming to sustain employee well-being and organizational performance.
Human Resource Practices, Employee Resilience, Organizational Justice, Business Process Outsourcing (BPO) Industry
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Techno-Economic Analysis of Digital Tools in Financial and Risk Advisory Services
The digital transformation of financial services has greatly revolutionized the way financial institutions evaluate, deal and counsel risk. With the process of financial advice and risk of credit assessment becoming more and more intertwined with the use of sophisticated analysis technologies, there is an ever-increasing necessity to comprehend the technological efficiency, as well as the economic proliferation, of these digital instruments. This study has provided a techno-economic analysis of machine-learning-based decision-support systems on the Credit Risk Loan Eligibility dataset. The experiment assesses the effectiveness of digital analytical applications using logistic regression, random forests, gradient boosting, and AutoML pipelines to improve the accuracy, speed, and consistency of assessing credit risk relative to traditional advisory processes. Technologically, this study investigates the accuracy of models, AUC-ROC, and dynamics of precision-recall, and feature contribution of risk prediction. On the economic front, the paper measures the economic consequences of better predictive performance by determining expected loss (EL), cost of misclassification, profit/loss-curves and decision thresholds that maximize the lending results. This study shows that digital tools contribute to the reduction of the losses associated with defaults, the enhancement of the quality of loan portfolios, and allow advisors to make more efficient and consistent decisions by connecting the model performance to the quantifiable financial gains. The results also indicate that the implementation of digital tools in the advisory processes not only improves risk prevention but also increases operational efficiency through the automation of redundant activities, minimization of human biases, and standardization of evaluation processes. To ensure that financial institutions evaluate their digital-tool investment based on the long-term economic benefits and ease of implementation, a techno-economic evaluation framework is suggested. Altogether, the paper demonstrates the strategic importance of digital decision-support systems in the contemporary financial and risk consultations and presents empirical data on how the system implementation leads to technological dominance and considerable economic benefits [2]. This study will add to the dynamic discussion on the topic of digital finance by providing an effective framework of assessing the influence of digital tools on performance, cost-effectiveness, and risk outcomes.
Digital Financial Tools, Techno-Economic Analysis, Credit Risk Assessment, Machine Learning Models, Financial advising service and Optimization of Economic Value
Copyright (c) 2019 https://creativecommons.org/licenses/by-nc/4.0
Machine Learning model for Enhancing Small Business Credit Risk Assessment and Economic Inclusion in the United State
The lack of accessible credit is a constitutive constraint on small businesses in the US. This paper proposes, evaluates, and establishes an interpretable, bias-conscious machine learning approach for small business credit risk assessment. Based on anonymized application, repayment, and organizational operational data, we compare gradient boost, regularized generalized linear models, and tree-based learning to industry-leading scorecards, leveraging monotonic constraints, fairness-conscious weight adjustments, and a SHAP explanation layer. The research hypothesis is to validate whether machine learning systems can strengthen default AUC/KS performance while decreasing disparities in group error rates, along with increasing approval rates at equal risk. Some uplift assessment measures incremental safe approvals, as well as expected loss subject to constrained decision rules. For more comprehensive implementation, the research includes model cards, feature management, WOE/IV, feature stability, as well as champion-champion comparisons. The findings of this research confirm the hypothesis, suggesting interpretable machine learning can achieve higher levels of risk differentiation (∅AUC > X), significantly close error gaps (∅gap > Y%), and achieve inclusivity gains at equal portfolio loss. The research aims to contribute a reproducible workflow, a set of metrics, as well as evidentiary validation of the applicability of transparent machine learning in credit markets.
Small-business credit; Explainable AI; Fairness; Risk modeling; Financial inclusion; Interpretable ML; Model governance; SBA; ECOA/FCRA compliance
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Leveraging Business Analytics to Enhance Supply Chain Resilience and Reduce Disruptions in Critical U.S. Industries
The growing complexity of world supply chains, alongside the growing demand instability and uncertainty in operations has intensified the necessity to use data-driven solutions that help to build resilience and minimize disruptions in critical sectors. The study analyses the potential of using business analytics to enhance supply chain resilience in America by basing on the DataCo Smart Supply Chain Dataset as the empirical evidence. The data, which is over 180,000 records at the transaction level, offers detailed information about logistics operation, consumer behavior, the use of payment modes, product categories and delivery performance. This type of multidimensional information provides a one of a kind chance to observe the correlation between delivery delays, operational risks, the change in demand, and frauds. The predictive modeling, clustering methods, and statistical analysis methods have been used to determine the important factors leading to supply chain fragility in the study. In particular, the demand forecasting, risk classification of late-delivery, and fraudulent orders are predicted with the help of machine learning models like Gradient Boosting, Random Forest, Logistic Regression, and LightGBM. These models allow finding the patterns that can be overlooked by traditional means of analysis, such as the correlation between the geographic distribution of the customers, the channel of payment, and the shipping delays. Moreover, clustering algorithms like K-Means and DBSCAN can give more information regarding a behavioral pattern of fraud and the inconsistencies of its operations. This study includes explainability tools, such as LIME and FairML, which makes the behavior of models more transparent such that predictive insights can be interpreted and made operationally feasible. The findings indicate that the key variables of critical concern include shipping mode, type of transaction, demand cycle seasonality and customer location that contribute greatly to resilience and risk. This study is a part of an increasing literature on digital supply chain transformation because it proves how business analytics facilitate proactive decision-making, operational planning optimization, and disruption relief in the U.S. industries. The results have strategic implications on supply chain managers, policymakers, and organizations that strive to develop flexible, information-driven, and resilient supply chain systems.
Supply Chain Resilience, Business Analytics, Machine Learning Models, Demand Forecasting, Fraud Detection and Operational Risk Management
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc/4.0
Opportunities and Challenges of Integrating Artificial Intelligence into Management Information Systems: Perspectives of King Khalid University Students
The rapid integration of artificial intelligence (AI) into management information systems (MIS) is reshaping decision-making, learning processes, and organizational efficiency across higher education and business environments. Recent studies highlight that AI-enabled MIS enhances decision quality, analytical capability, and strategic responsiveness while supporting personalized and adaptive learning experiences for students. The literature also shows that AI-driven tools strengthen student engagement, improve information management processes, and enable more agile managerial practices. Despite these benefits, challenges persist, including digital literacy gaps, ethical concerns, varying levels of AI readiness, and barriers to student adoption. Research further indicates rising expectations among students regarding AI’s role in their professional and educational development. As institutions increasingly incorporate AI into curricula, teaching strategies must adapt to balance human judgment with automated intelligence. Overall, the reviewed studies demonstrate that AI’s expanding presence in MIS offers significant potential for improved efficiency and learning outcomes, though careful implementation and skill development remain essential for success.
Artificial Intelligence; Management Information Systems; Student Perceptions;AI Challenges; AI Opportunities; King Khalid University
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
The Use of Artificial Intelligence in Supporting Managerial Decision-Making: Perspectives of Management Information Systems Faculty Members in Saudi Universities
This study examines Management Information Systems faculty perceptions of the effectiveness and adoption of artificial intelligence (AI) in supporting managerial decision-making in Saudi universities. Using a survey with a five-point Likert scale, the study explored two main areas: faculty views on AI effectiveness in enhancing decision-making and factors influencing AI adoption, including perceived benefits, implementation challenges, and demographic influences. Results show strong faculty support for AI integration, particularly when aligned with ethical standards, data security, and accountability mechanisms. Security and risk mitigation were top priorities, followed by monitoring, evaluation, and transparency measures. Faculty also recognized operational benefits, such as improved decision accuracy, efficiency, and strategic planning, while noting barriers including system compatibility, technical integration, and time requirements. Demographic factors, such as age, experience, and departmental affiliation, shaped perceptions of AI adoption. The findings highlight the need for clear policies, targeted training, technical support, and ongoing monitoring to ensure responsible and effective AI implementation. Limitations include the focus on a single faculty group and reliance on self-reported data, suggesting future research should expand the sample, employ qualitative methods, and explore longitudinal changes in perceptions.
Artificial intelligence, managerial decision-making, faculty perceptions, higher education, AI adoption, Saudi universities
Building Usable Management Knowledge: A Framework of Bridging Mechanisms Between Research and Practice
This article develops a conceptual framework for how management research becomes usable knowledge for practitioners. While the theory–practice gap has been widely acknowledged, existing discussions have focused primarily on diagnosing the divide rather than explaining the organizational and intellectual mechanisms through which research gains relevance. Integrating insights from organizational theory, knowledge management and institutional analysis, this narrative review identifies three categories of bridging mechanisms—interface mechanisms, interpretive mechanisms and institutional mechanisms—that shape the translation of scholarly insights into actionable managerial understanding. Interface mechanisms structure interaction between researchers and practitioners; interpretive mechanisms facilitate meaning-making across epistemic communities; and institutional mechanisms create the conditions for long-term knowledge integration. Together, these mechanisms provide a structured account of how management knowledge becomes usable without sacrificing conceptual rigor. The framework contributes to ongoing debates on research impact by reframing knowledge relevance as an outcome of organizational design and knowledge governance rather than as a trade-off with rigor. Implications highlight how intentionally structured interfaces and interpretive processes can enhance the strategic value of management research and support evidence-informed decision-making in organizations.
Copyright (c) 2025 Journal of Business and Management Studies https://creativecommons.org/licenses/by-nc/4.0
A Data-Driven Approach to Enhancing Project Management Efficiency through Machine Learning and Predictive Modeling
In the current fast-paced and complex project environments, the traditional methods of project management frequently struggle to manage the dynamic nature and growing complexity of the projects. This study examines the possibilities of the Machine Learning (ML) and Predictive Modeling technology to enhance the efficiency of project management through more intelligent decision-making, resource management and risk management in real time. A data-driven approach that takes ML techniques such as predictive analytics, regression models, and decision trees, greatly improves on the conventional approach by enabling actionable insights from huge datasets from the projects. This research reviews the existing applications of ML in project management, discovers the prevailing challenges associated with its integration, and recommends a framework used for the adoption of predictive modelling techniques. The study reveals the important benefits of using ML in better project forecasting, resource management, and risk prediction. It also looks at the barriers to adoption such as poor quality of data, system integration, and specialized skills. The results indicate that the data-driven methodology can not only streamline the project execution but provide long-term benefits in terms of project improvements, controlling the cost and following the timeline.
Machine Learning; Predictive Modeling; Project Management Efficiency; Data-Driven Approach; Risk Mitigation; Resource Optimization; Decision-Making; Predictive Analytics; Regression Models; Project Forecasting
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
Risk Analysis of Material Procurement in Oil and Gas EPC Projects in Sudan
The rapid expansion of the Sudanese oil and gas industry, driven by rising demand, relies heavily on the Engineering, Procurement, and Construction (EPC) project model. Given that material procurement accounts for over 60% of EPC contract value and faces diverse risks, effective risk management is crucial for Sudanese enterprises engaged in international projects. This paper investigates and assesses the material procurement risks in oil and gas EPC projects, specifically focusing on improving risk management and procurement capabilities for Sudanese firms. The methodology involved first defining EPC concepts and risk analysis methods (identification, assessment, response). A comprehensive risk list for material procurement was constructed and subsequently assessed using the Risk Matrix Method, classifying risks into four levels (A to D). Finally, these steps were applied to a case study project to identify, assess, and propose specific risk-response measures. The results categorize procurement risks into two major groups: external risks (natural, political, social, market, owner, supplier) and internal risks (decision-making, plan, orders, transportation, inspection, inventory). The study concludes that Sudanese oil enterprises face significantly higher external risks than internal risks during material procurement in international oil and gas EPC projects.
EPC (Engineering, Procurement, and Construction)
Customer Behavior and Factors Influencing the Patronage of Thrift Banks in China
The study investigates the factors influencing client patronage of a thrift bank in China over the last five years. Thrift banks serve their communities by offering affordable financial services, with a focus on low-cost mortgages and competitive interest rates. The research analyzed the demographic profiles of 100 clients, revealing that a typical client is a female, college-educated individual aged 34 to 42 with a middle-income level. Key factors affecting client retention include accessibility, reputation, service delivery, and customer relations. Despite marketing strategies, the bank experienced a decline in deposits and loan approvals, indicating a need for improvement. The research highlights the importance of innovation in product offerings and targeted client retention strategies to adapt to a competitive financial market. An action plan was proposed to enhance client loyalty and satisfaction.
Thrift Bank, accessibility, reputation, delivery of service, products and services, customer relation and maintaining balance, attracting/acquiring new clients, retaining clients, deposit generation and loan approvals
Contract Management Practices, Contractor Relationships, and Performance at Beijing JCZ Technology Company
JCZ Technology Co. upholds the highest standards of corporate governance as it continues to benchmark against recognized global best practices and monitors developments in corporate governance to elevate the company’s corporate governance structures, processes, and standards to international levels. This study aimed to examine the practices in contract management, the quality of relationships with contractors, and the performance of these contractors within the Beijing JCZ Technology Company and develop an action program to strengthen contract management practices, enhance contractor relationships, and improve contractor performance within the JCZ Technology Company. The study focused on contract management practices, contractors’ relationship and performance. The study used 80 respondents divided into two groups, 40 accredited contractors with contract under JCZ Technology Company and 40 engineers of JCZ Technology Company. A researcher-designed questionnaire, validated by experts to measure the level effectiveness of contract management practices at Beijing JCZ Technology Company as rated by respondents. The researcher used a four-point response Likert Rating Scale. Statistical tools such as weighted mean, and Pearson’s r correlation and T-test were employed for analysis. Findings revealed that with an average weighted mean of 3.27, JCZ Technology Company contract management procedures were highly effective. Also, the results indicated a p-value of 0.000, which is below the 0.01 significance level; thus, a significant relationship was noted between the effectiveness of contract management practices and the relationship with accredited contractors.
Relationship, management practices, job performance
Assessing the Performance of Micro-Enterprises in Guangdong, China
Micro-enterprises, crucial for economic development, rely heavily on government support. This study assessed the performance of micro-enterprises utilizing micro-financing in Guangdong Province, China, addressing their profiles and challenges. Key findings include: most businesses are sole proprietorships in retail, operational for 6–10 years; they reported sales of 100,001 – 500,000, ROI of 21–40, and cash flow of 50,001 – 100,000 with liquidity ratios between 1.51:1 and 3.00:1. Performance metrics showed no significant differences based on business form or years of operation, though sales varied by type, favoring service enterprises. Common issues identified included insufficient cash for purchases, high competition, lack of management training, and natural disasters. An action plan is recommended for enhancing micro-enterprise operations in the region.
Micro-enterprises, micro-financing, sales, cash flow, liquidity, and Return of Investment (ROI)
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.
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.
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
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