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
Logistics Operations of Customs Brokerage Firms in Sta. Clara, Batangas: Basis for Business Strategy
This study examined the logistics operations of Customs Brokerage Firms in Brgy. Sta. Clara to check the efficiency and service quality from the perspectives of Customs Brokerage Employees and Customs Brokerage Clients. The study employed a quantitative research design to analyze the ServQual dimensions such as Reliability, Responsiveness, Assurance, Empathy and the Delivery of Service factors such as Service Quality, Timeliness and Cost of Service from the Customs Brokerage employees and clients. Results revealed that most people rated operations as “Very Efficient” to “Excellent” with Assurance and Reliability earning the highest scores and it shows that the company has good compliance practices and technical skills. The results from ANOVA revealed that there were no significant differences in the perceptions of employees and clients when grouped according to their respective profiles, implying consistent service performance across respondent categories. Overall, the findings demonstrated that customs brokerage firms in Sta. Clara, Batangas operated effectively and maintained satisfactory logistics performance. The study concluded that further enhancements in digitalization, communication systems, and continuous skills development are necessary to strengthen competitiveness and sustain client satisfaction. Based on these findings, the study recommended the adoption of technology-enabled solutions, structured training programs, and process optimization strategies as a basis for improving logistics operations and supporting long-term business growth.
Customs Brokerage, Logistics Operations, SERVQUAL, Client Satisfaction, Business Strategy
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A Proposed Financial Feasibility Plan in Managing Wastewater among Selected Mixed-Use Establishments in Mandaluyong City
This study evaluated the effectiveness of wastewater management practices among selected mixed-use establishments in Barangay Wack Wack, Mandaluyong City, as a basis for proposing a financial feasibility plan. The research aimed to assess existing wastewater generation characteristics, treatment and maintenance practices, and the level of adoption of sustainable wastewater management measures. It also examined whether differences existed in wastewater management practices when establishments were grouped according to business or household size, type of operation, and water usage patterns.A descriptive-quantitative research design was employed. Data were gathered through survey questionnaires administered to residents and employees of selected establishments, supported by document review and basic financial analysis. Statistical tools were used to determine significant differences in wastewater management practices across establishment profiles. Financial feasibility indicators were applied to evaluate the practicality of implementing wastewater system improvements. The results showed that most establishments had existing wastewater treatment systems in place; however, gaps were identified in preventive maintenance, stakeholder awareness, and the consistent application of sustainable practices. Large commercial and large residential establishments demonstrated stronger capacity to support wastewater system improvements, while small and medium-sized establishments showed financial and operational constraints. Statistical analysis indicated no significant differences in wastewater management practices across most establishment profile variables, suggesting generally similar levels of implementation regardless of size or type of operation. Based on the findings, a proposed financial feasibility plan was developed to guide establishments in improving wastewater management practices. The plan integrates regulatory compliance, operational efficiency, financial sustainability, and stakeholder participation. The study concludes that effective wastewater management in mixed-use establishments can be achieved through financially viable strategies that balance environmental protection, economic practicality, and social responsibility.
Wastewater management; wastewater practices; sustainable development; water quality; regulatory framework; community involvement; financial feasibility; mixed-use establishments; sewage treatment systems; environmental sustainability
Financial Transparency and Its Effect on Income Utilization and Infrastructure Development Towards the Design of a Policy Manual among State Universities and Colleges in Region II
This study explored the effect of financial transparency on income utilization and infrastructure development among State Universities and Colleges (SUCs) in Region II with the aim of enhancing financial policy and governance mechanisms. Applying a mixed method design using criterion sampling approach, quantitative data were collected through structured survey administered to 111 respondents while qualitative data were gathered from open-ended survey responses, and documentary analysis of Budget and Financial Accountability Reports (BFARs) further substantiated the findings. Descriptive results reveal that financial transparency was rated very high indicating strong compliance and accountability, and income utilization was perceived as very efficient. Nonetheless, though infrastructure development was assessed as very developed, documentary verification disclosed limited budget allocation and moderate to low utilization and disbursement rates portraying implementation delays and sustainability concerns. Multiple linear regression results further reveal that financial transparency significantly affected income utilization but did not predict infrastructure development. This implies that infrastructure outcomes rely more on budget prioritization, procurement efficiency, absorptive capacity, and long-term capital investment planning, rather than transparency alone. Moreover, qualitative insights stressed the need for need-based allocation, strengthened monitoring and evaluation, continuous capacity development, and stricter compliance with financial policies. And it further concludes that transparency reinforces income utilization.
Financial Transparency, Income Utilization, Infrastructure Development, State Universities and Colleges.
Customer Expectations on Electric Vehicles in the USA Market: A Comprehensive Analysis of Shifting Preferences and Purchase Intentions
As the electric vehicle (EV) sector in the United States undergoes extraordinary expansion, comprehending customer expectations has become essential for manufacturers, politicians, and marketers. This study investigates the present state of customer expectations about electric vehicles in the U.S. market, analyzing primary factors influencing purchase intention, perceived obstacles, and shifting preferences. A thorough look at existing data and polls of consumers shows that range anxiety, charging infrastructure, and pricing are still the key concerns. On the other hand, environmental awareness and cheaper operating expenses are what drive adoption. Our research shows that there is a big difference in how different generations feel about buying electric vehicles. Younger people are more likely to want to buy one, even though they are worried about how much it would cost. This study offers practical recommendations for stakeholders aiming to expedite electric vehicle adoption in North America.
electric vehicles, consumer expectations, purchase intention, sustainable transportation, market adoption
Accelerating the Transition: Ultra-Fast Charging Infrastructure and U.S. Global Competitiveness in Electric Mobility
The global electric car revolution has reached a point where charging infrastructure, especially ultra-fast charging capacities above 350 kW, is the most important factor in determining which country is the best place to buy an electric car. This comprehensive study examines the current state of ultra-fast charging infrastructure deployment in major global markets, emphasizing a comparative analysis among the US, China, and Europe. This study's quantitative analysis of infrastructure deployment rates, market dynamics, and technological advancement trajectories shows that the United States is eight times slower than China and less densely populated and more evenly distributed than Europe, even though the federal government has invested $7.5 billion through the Infrastructure Investment and Jobs Act. As of December 2024, China has set up more than 3.2 million public charging stations, including 1.78 million DC fast chargers. In contrast, the US has only about 204,000 public chargers, and only 51,000 of those are DC fast chargers. This study demonstrates a direct correlation among EV adoption rates, economic competitiveness, technical sovereignty, and ultra-fast charging infrastructure. The results show that quickly building 350 kW+ ultra-fast charging networks is not only a challenge for the infrastructure, but also a strategic necessity for the U.S. to stay competitive in the global economy. This study offers empirical evidence underscoring the critical necessity for synchronized federal-state infrastructure enhancement, standardization protocols, and public-private collaborations to achieve competitive equivalence for the U.S. in the burgeoning electric mobility sector, anticipated to attain $257 billion by 2032.
Ultra-fast charging, electric vehicle infrastructure, national competitiveness, 350kW charging systems, DC fast charging, electric mobility, infrastructure deployment, energy policy
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Designing Human–AI Collaborative Decision Analytics Frameworks to Enhance Managerial Judgment and Organizational Performance
The fast spread of artificial intelligence (AI) in the United States organizations has radically altered the managerial decision-making process, but on the other hand, it has augmented the complexity and uncertainty in decision, and the accountability stresses. Despite the high-level predictive and prescriptive potentials of AI-based analytics, most organizations have difficulties converting algorithmic results into sustainable managerial decisions. Low levels of trust, lack of explanation, and poor integration between AI systems and human judgment have been caused by over reliance on automation, weak explain ability, and poor organizational outcomes. Current literature has majorly focused on automation-based views of decision support, with a severe lack of insight into the coordinated manner in which human experience and AI intelligence can be systematically integrated with the assistance of analytics. This paper fills this gap by outlining a Human-AI Collaborative Decision Analytics Framework that could be beneficial to improve managerial decisions and organizational performance. Following a conceptual research design, the study integrates interdisciplinary literature in the field of managerial decision-making theory, business analytics, and governance of AI in its attempt to establish an integrative framework where analytics becomes the focal interpretive intercession between AI outputs and human decision-makers. The framework has five overlapping layers such as data, AI analytics, business analytics interpretation, human judgment, and feedback learning that combine to facilitate transparency, accountability, and contextual decision-making. The framework is depicted in the most important areas of the organization with the main focus on the strategic management and workforce decision-making and the secondary focus on the finance, operations, and marketing. The framework minimizes the effects of the algorithmic bias, automation bias, and enhances workforce confidence through embedding managerial control and ethical reasoning and contextual evaluation frameworks into the workflows of AI-assisted decision-making. The contribution of the study to the theory is that it develops human-grounded decision analytics and to practice by providing practical advice to executives and analytics leaders. The presented framework contributes to the responsible use of AI, productivity, and economic competitiveness in the United States in the long term.
Human–AI Collaboration, Decision Analytics, Managerial Judgment, Business Analytics, Responsible Artificial Intelligence and Organizational Performance
Synthesizing the Green Value Chain: A Systematic Review of Vetiver Bioengineering, Coastal Entrepreneurship, and Consumer Valuation in the U.S. Green Economy
The accelerating trajectory of climate change, characterized by rising sea levels, intensifying precipitation events, and coastal erosion, has exposed the critical vulnerabilities of traditional "gray" infrastructure in the United States. While the paradigm shift toward Nature-Based Solutions (NbS) offers a promising pathway for resilience, the commercial and operational integration of these technologies remains fragmented. This research paper presents an exhaustive systematic review of the "Green Value Chain" a conceptual framework linking the biotechnical engineering performance of the Vetiver System (VS), the emerging business models of coastal entrepreneurship, and the socio-economic dynamics of consumer valuation. Focusing specifically on the sterile, non-invasive 'Sunshine' genotype of Chrysopogon zizanioides, the analysis synthesizes geotechnical data confirming Vetiver’s capacity to increase soil shear strength by 30–40% and stabilize slopes in high-plasticity clays. Economically, the review establishes that while green infrastructure often entails higher initial stewardship costs compared to the "build-and-forget" model of gray infrastructure, it delivers superior benefit-cost ratios over decadal lifecycles through avoided capital replacement and ecosystem service generation. However, the market penetration of Vetiver bioengineering is severely constrained by "aesthetic friction"—a deep-seated consumer preference for manicured landscapes and hardened shorelines, driven by social norms and risk misperceptions. By integrating findings from U.S. Army Corps of Engineers studies, coastal homeowner surveys, and bioengineering field trials, this paper proposes a "Nurse Crop Succession" framework. This model positions Vetiver not as a climax monoculture but as a temporary biological scaffold that facilitates the establishment of native dune and riparian ecosystems, thereby reconciling immediate engineering requirements with long-term ecological restoration goals. The findings suggest that unlocking the U.S. green economy requires a tripartite evolution: the standardization of bio-technical certification to unlock FEMA Community Rating System (CRS) incentives, the maturation of "Resilience-as-a-Service" business models, and a targeted cultural shift in how coastal protection is visualized and valued.
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Predictive analytics U.S. Global Marketing Competitiveness using AI
The competitiveness in global marketing is gradually being driven by the use of data-driven decisions and the capability of businesses to foresee the consumer needs in dynamic market conditions. The United States is well-established in the global trade and marketing segment, but there are leading-edge applications of artificial intelligence (AI) and predictive analytics emerging around the globe, and which require more facilitation of digital transformation in order to maintain and augment this leadership. What this study aims to explore is how predictive analytics using AI can help the U.S. become more competitive in the global market, by considering consumer purchasing behavior and decision-making patterns in relation to marketing. Based on the available dataset related to consumer behavior and shopping habits, obtained on Kaggle, the research examines the demographic factors, how often people buy a product, the type of product, seasonal patterns, sensitivity to discounts, and satisfaction with the review to determine the main marketing drivers. Classification models and clustering are used as machine learning methods to forecast consumer-related outcomes like the high-value spending behavior and the likelihood of loyalty. Categorical encoding, normalization, and the exploratory analysis are the steps of data preprocessing that guarantee the model accuracy and generalization. The results show that pricing, promotional involvement, and product type preferences play a major role in determining purchasing decisions and age and frequency of shopping are good predictors of high value customer segments. The information based on the segmentation of consumers and predictive patterns shows that marketing activities in the U.S. can be enhanced by focusing on localized customer requirements, better personalized marketing of products, and product positioning strategies. The paper finds that the adaptation of predictive analytics allows American companies to become more competitive in the global market due to better consumer satisfaction, more revenue-generating possibilities, and the maintenance of a strategic position in the market. The future research can incorporate more international data sets to generalize competitiveness study on international consumer markets. This study offers a viable and evidence-based method of using AI-based predictive analytics to promote the strategic development of American companies in competitive global markets.
Predictive Analytics, Artificial Intelligence (AI), Consumer Behavior, Global Marketing Competitiveness, Machine Learning and U.S. Market Strategy
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The Influence of Collaborative Procurement on Innovation and Competitive Advantage: Perspectives of Agriculture Stakeholders in Guyana
This research project investigated the perceptions of Agriculture stakeholders on the influence of Collaborative Procurement on Innovation and Competitive Advantage. The advancement of technology leads to rapid innovation, and increased competitiveness. This has resulted in agricultural organizations facing greater competition from local, regional and international markets. The agricultural sector in Guyana is plagued with ineffective procurement practices that resulted from fragmented sourcing strategies, inconsistent supplier partnerships, and lack of available low-cost procurement approaches. This study aims to provide practical tools based on best practices that Guyanese agriculture enterprises can use to improve their collaborative procurement practices in a more efficient manner, resulting in more strategic supplier relationships and more long-term sustainability. This study employed a Qualitative Approach with a Case Study Design. A Digital Structured Interview Schedule was the research tool that was utilized to gain insights from Agriculture stakeholders from eight different agriculture organizations. The participants included procurement officers and suppliers. Findings revealed that some organizations were already engaged in collaborative procurement; some organizations were at the initial stage of the implementation of collaborative procurement while two organizations were not in a state of readiness and had not considered collaborative procurement. Collaborative procurement influences innovation and competitive advantages by aligning technological advancements through networking, adaptation to change, increased organizational recognition and profit generation. The enabling factors to collaborative procurement and its influence on innovation and competitive advantage are; collaborating with organizations that are actively engaged in collaborative procurement, leveraging the purchasing power of organizations, provision of high-quality goods and services and human capacity building. It is recommended that there should be more training and collaboration with agricultural organizations that are actively involved in collaborative procurement and the opportunity for collaborative procurement should be open to all agricultural organizations.
Collaborative Procurement, Competitive Advantage, Innovation
A Practical Positive Approach to Critical Thinking
This article focuses on helping business majors understand critical thinking skills linked to negative thinking such as psychological context, Imposter Experiences. It begins with discussing a business communication course within the scope of theoretical frameworks linked to critical thinking skills and Imposter Experiences. The next section reports findings from how cross-disciplinary units in business departments collaborations were beneficial to students as they prepare for professional life. The final section discusses how learner-centered interactions can help business majors recognize and use practical critical thinking skills to circumvent negative thinking, i.e., Imposter Experiences they might encounter in both settings (academic and workplace).
Business Communication, Collaborations, Critical Thinking, Imposter Experiences, Learner-Centered Interactions, Workplace Readiness
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Impact of Digital Transformation of Logistics and Supply Chain in the Automotive Manufacturing Industry
With the continuous promotion of digital transformation of logistics and supply chain in the automotive manufacturing industry, the demand for new technologies is increasing day by day. This article empirically analyzes the impact of digital transformation of logistics and supply chain in the automotive manufacturing industry on the adoption of blockchain technology, based on research data from domestic automakers and third-party automotive logistics companies. The research results found that the digital transformation of logistics and supply chain in the automotive manufacturing industry has promoted employees' adoption of blockchain technology as a whole. The impact of digital transformation of logistics and supply chain in the automotive manufacturing industry on the adoption of blockchain technology varies in different application scenarios. The research results will help promote the innovative application of blockchain technology in the digital transformation of logistics and supply chain in the automotive manufacturing industry, providing new paths and opportunities for industrial upgrading and transformation.
Automobile manufacturing industry, logistics and supply chain management, digital transformation, blockchain technology
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
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
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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
Circular Economy Metrics: A Systematic Bibliometric Mapping
The circular economy has emerged as a key concept for sustainable development, yet measurement frameworks remain inconsistent. In this bibliometric mapping, we conducted an analysis of 701 publications from Web of Science and Scopus to identify current models for evaluating circularity. Results show a concentration of research in China and Europe, with eleven thematic clusters emerging: waste management, resource efficiency, and lifecycle assessment, among others. Despite increasing attention, significant gaps remain in standardized metrics and comprehensive evaluation tools. This review highlights the need for unified methodologies to effectively assess circular economic performance across sectors and geographies.
Benchmarking, Bibliometric analysis, Circular economy, Measurement framework, PRISMA 2020
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
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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
Emotional Intelligence Abilities and Work Engagement among Salespersons: A Conservation of Resources Theory Perspective from Germany’s Pharmaceutical Industry
Sustaining salespersons’ work engagement has become a critical concern amid intensifying professional demands and increasing turnover within the German pharmaceutical B2B sector. Guided by the Conservation of Resources (COR) theory, this study examines how four distinct abilities of emotional intelligence—self-emotion appraisal, others’ emotion appraisal, emotion utilization, and emotion regulation—affect salespersons’ work engagement. Employing a quantitative design, data were collected from 340 pharmaceutical sales representatives in North Rhine-Westphalia and analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal that all four emotional intelligence abilities exert significant positive effects on work engagement, confirming the hypothesized relationships. Among these, the utilization and regulation of emotion emerged as the most influential predictors, underscoring their pivotal role in sustaining engagement within emotionally demanding sales environments. Overall, the four abilities of EI explained 68% of the variance in work engagement, affirming its value as a psychological resource that enhances motivational energy and mitigates emotional strain. This study enriches the sales management literature by demonstrating the strategic importance of developing emotional competencies to foster work engagement of salespersons in competitive B2B markets.
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.
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.