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
Enhancing U.S. Financial Compliance and Risk Management through Data-Driven Automation and Anomaly Detection
The growing complexity of the U.S. financial ecosystem has heightened the demands of smart and data-driven compliance tools. These tools must detect emerging risks and identify fraud in various regulatory environments. The study proposes an integrated framework that automates anomaly detection and cross-silo data association. The goal is to improve financial compliance and risk management capacity. The analysis draws on several reproducible and publicly available datasets, such as the U.S. Small Business Administration (SBA), Paycheck Protection Program (PPP), loan information (2020 - 2021) and the Office of Foreign Assets Control (OFAC) Specially Designated Nationals (SDN) list (2021), the U.S. Securities and Exchange Commission (SEC) EDGAR Company Filings data (2021), the Home Credit Loan Default data (2021), and the ULB Credit Card. The suggested framework uses a combination of supervised and unsupervised learning models to assess behavioral deviations, financial irregularities, and entities that are related to sanctions. The study also provides integrity, privacy protection, and reproducibility of data through cryptographic hashing and tokenization. The Cross-silo linkage results indicate that regulatory, transactional and loan datasets improves the accuracy of fraud detection and false- positive alerts. This fusion exhibits hidden relationships between public funds, corporate filings and violation of sanctions that are often overlooked when datasets are analyzed separately. This study presents practical implications to enhance the transparency of audits and financial integrity in the country by mapping findings against the Anti-Money Laundering Act of 2020 (AMLA-2020) and the national financial priorities of FinCEN in 2021.
Financial Compliance Automation, Anomaly Detection, Cross-Silo Data Integration, Regulatory Risk Management, Fraud Detection and Prevention, FinCEN Compliance and U.S. AMLA-2020.
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Using Predictive Analytics to Enhance Productivity and Innovation in the Advanced U.S. Manufacturing Sectors
The intersection of predictive analytics, Industrial Internet of Things (IIoT), and cloud-based systems is also changing sophisticated manufacturing in the United States. With increased global competition and the emergence of operational resilience as a strategic goal, manufacturers are turning toward data-driven systems, increasing productivity and driving innovation. This paper investigates the operational performance and innovation of advanced sectors of the U.S. manufacturing industry with the help of predictive analytics by using the Smart Manufacturing IoT-Cloud Monitoring Dataset which includes 100,000 real-time sensor observations of 50 industrial machines. The theoretical framework of Industry 4.0 and resource-based perceptions of technological capability make use of the notion of machine learning models that forecast the maintenance demand and approximate the remaining useful life dependent on sensor variables and indicators of anomalies such as temperature, vibration, humidity, pressure, energy consumption, and the risk of downtime. The algorithms of supervised learning, which are Random Forest and Gradient Boosting, are applied to assess the predictive accuracy based on ROC-AUC, F1-score, and RMSE values. The results of empirical studies indicate that predictive analytics can substantially increase the effectiveness of failure detection and minimize unexpected downtime, which improves the effectiveness of devices in general, in addition to enhancing operational reliability. The findings also reveal that predictive maintenance systems are not only efficiency-enhancing systems, but also innovative enablers, as predictive maintenance systems enable real-time decision-making, optimizing processes and resources in an adaptive manner. Using the ideas of turning reactive maintenance systems into proactive, information-driven approaches, the advanced manufacturers will be in a better position to enhance production continuity, cut expenses on the operation, and become more technological. The research is relevant to academic literature because it forms a quantifiable connection between AI-based predictive maintenance and productivity increase and innovation abilities in the advanced manufacturing ecosystems. In terms of policy and management, the results can be used practically to guide the U.S. manufacturers who want to use predictive analytics to improve industrial competitiveness and maintain their long-term growth in the dynamic digital economy.
Predictive Analytics, Advanced U.S. Manufacturing, Industrial Internet of Things (IIoT), Predictive Maintenance, Machine Learning in Manufacturing and Innovation and Productivity
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Sustainability claims on social media in relation to brand trust in teenagers
Concerns have arisen about the environmental impact of the fashion industry, which is facing exponential growth. In recent times, as a result, brands are increasingly prompted to utilise social media platforms for sustainable messaging, and findings show that Instagram and TikTok are the primary platforms for exposure. However, it has been noticed that a major portion of respondents encounter environmental messaging only occasionally despite the abundance of social media accessibility worldwide. Therefore, this study examines how this communication influences brand trust and consumer behaviour amongst the adolescent target demographic aged 13-19 in Singapore. Additionally, throughout the research, there is a deeper focus on authenticity and greenwashing awareness, which have been deemed as important factors to consider when discussing how teenagers and the Gen Z audience place their trust in brands today. An online survey was conducted via the process of convenience sampling, and a sample size of 50 adolescents aged 13-19 was requested on a voluntary basis to fill out the survey. The specific structure of the questions asked enabled the collection of both qualitative and quantitative data. Additionally, as a result of the survey results, it was overall noticed that authenticity forms a robust link with transparency, consistency, and third-party validation in place of influencers and celebrity endorsements, which have become increasingly prevalent in recent years, and therefore, this suggests that for brands to engage a teenage consumer base, they must prioritise credibility, consistent communication and integrate environmental mindedness alongside competitive product appeal to bridge the gap between awareness, intention and action.
Cognitive dissonance, Greenwashing, Sustainability, social media, Brand perception, Adolescents, Consumer behaviour
Impact Evaluation in TD&E: Limits of ROI Against Investment Alternatives
This theoretical essay examines impact evaluation in Training, Development and Education (TD&E) as support for organisational decisions. Based on established evaluation models and Mincer’s human capital approach, it proposes indicators such as time to proficiency, application rate and productivity variation, which allow results to be measured over time and compared across programmes. Return on investment (ROI) is treated as a consequence of measurement quality rather than as a design in itself. The accounting treatment of training under CPC 04 (R1)/IAS 38 is examined, highlighting the asymmetry between immediate recognition as expense and benefits that extend beyond the reporting period. As a conditional proposition, selected TD&E outlays may be recognised as an intangible asset only when identifiability, control, probable future economic benefits and reliable measurement are all met; otherwise expenditure should be recognised in profit or loss. The accounting angle remains ancillary to the decision problem in work and organisational psychology, since accounting reflects evidence generated by evaluation. It concludes that the dissemination of evaluation methodologies strengthens competitiveness and legitimises investment in TD&E.
In-service Training; Program Evaluation; Return on Investment; Human Capital; Professional Development
Determinants of Online Shopping Behavior: An Analysis of Perceived Benefits, Reputation, and Consumer Experience
Online shopping in the Philippines is growing significantly because of technological advancements and increased internet usage. The purpose of this study is to determine what factors influence online shopping behavior among Filipinos by examining the areas of perceived benefits, store reputation, customer experience, and lifestyle. A multi-method approach was used, where surveys were distributed to 338 online shoppers in Santiago City, allowing for the subsequent analysis of the data collected using descriptive and correlational statistical analyses. While consumers generally have positive ratings of both their perceived experience (M = 3.02) and store reputation (M = 3.00), overall ratings for perceived benefits are significantly lower than either of these two variables (M = 2.85). The study also found strong positive correlations between store reputation and customer experience (r = 0.681, p < .001) and between lifestyle and perceived benefits (r = 0.608, p < .001). In addition, a discussion highlighted a "perception gap" resulting from consumers' risk-averse nature when assessing the security of online shopping websites and the quality of products purchased online, which has prevented these companies from reaching the number of online sales that was anticipated. To reduce this gap, it is recommended that retailers provide additional transparency by ensuring product quality, using secure methods for payment processing, and utilizing targeted marketing techniques.
online shopping behavior, perceived benefit, store reputation, consumer experience, lifestyle factor
AI- Driven On-Chain Behavioral Pattern Discovery for Whale Sentiment in US Crypto Markets
This study investigates the use of artificial intelligence to uncover behavioral patterns of whale participants in US cryptocurrency markets and to infer their impact on market sentiment. By leveraging on-chain transaction data and exchange activity, the research constructs behavioral features capturing transaction frequency, transfer volume, and wallet clustering. Machine learning models, including tree-based learners, recurrent neural networks, attention-enhanced architectures, and ensemble frameworks, are employed to identify distinct whale archetypes and to translate their activity into predictive sentiment signals. The findings reveal differentiated behavioral strategies among whales, with high-frequency accumulators, occasional large movers, and directional distributors exerting unique influences on market dynamics. Furthermore, attention mechanisms and feature importance analysis enhance the interpretability of model predictions, enabling insights into the timing and nature of whale-driven market movements. Overall, the study demonstrates the potential of AI-driven on-chain analytics to provide actionable intelligence for traders, exchanges, and regulators, bridging the gap between raw blockchain data and meaningful behavioral insights.
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AI-Driven Consumer Behavior Insights and Human-Centered Marketing Strategies for Enhancing Engagement and Innovation
Artificial intelligence (AI) in marketing has in the past few years been spreading very rapidly across the world. The world AI in marketing market grew by about US 17.48 billion in 2022, to US 22.64 billion in 2023, implying that AI in marketing market has a compound annual growth rate (CAGR) of about 29.5%. This academic article explains why the insights of the AI-driven consumer behavior collected through the support of big data analytics, machine learning, recommendation algorithms, and sentiment analysis could be used along with the human-centered-based marketing strategy to improve the customer engagement, innovation, and brand loyalty in the long-term. Empirical research indicates that AI-based personalization and data-based targeting could significantly drive up the conversion rates, utilize the marketing funds more efficiently, and enable more personalized and relevant user experiences. According to the recent surveys, companies, which exploit AI to learn more about their clients, report that the processes of product innovation are 45% more rapid, and the production of the campaign returns is increased 33%, as compared to companies, which apply traditional tools of analytics. These outcomes contribute to the idea that AI is not another personalization tool but a cause of business growth and creative thinking. Finally, the study finds some recommendations to be feasible that should be implemented by marketers: invest in good data governance, concentrate on customer privacy and transparency, merge feedback loops, and automate but not neglect human empathy. Those findings indicate that the exploitation of AI as the source of additional knowledge of consumers, and not its destruction, is a viable opportunity available to every business wishing to achieve success in the competitive online markets.
Artificial Intelligence; Consumer Behavior Insights; Human-Centered Marketing; Customer Engagement; Marketing Outcomes; Marketing Innovation
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AI-Driven Resilient Supply Chain Architectures: Machine Learning Frameworks for Risk Anticipation, Disruption Mitigation, and Adaptive Decision-Making
Global supply chains face various disruptions related to demand volatility, geopolitical tensions, weather-related disruptions, and operational failures, making conventional rule-based and reactive resilience approaches inadequate for addressing these disruptions. Recent developments in artificial intelligence (AI) and machine learning (ML) offer promising opportunities for transforming supply chain resilience from conventional reactive approaches to proactive, adaptive, and learning-enabled decision-making systems. However, the prevailing literature primarily focuses on standalone AI-related activities without discussing the architectural aspects of integrating AI for end-to-end supply chain resilience under uncertainty. This study proposes a unified resilient supply chain architecture with the incorporation of machine learning technologies in the processes of anticipation, mitigation, and adaptive decision-making. The research methodology used in the study is the design science research approach, which resulted in the development of a multi-layered framework consisting of the data intelligence layer, the risk anticipation layer, the prescriptive decision and mitigation layer, and the adaptive learning feedback layer. The framework ensures the integration of various machine learning algorithms with supervised learning, unsupervised learning, and reinforcement learning techniques with various categories of supply chain risks. In order to test the theoretical robustness of the proposed architecture, a scenario-based evaluation approach is followed, and various disruption scenarios, which cover failures from the suppliers, disruptions in the logistics network, and demand disruptions, are analyzed. New evaluation criteria, which are classified under the term "resilience-oriented evaluation," are also proposed in the paper, which move beyond the traditional evaluation criteria of costs and services. This paper contributes to the literature in the fields of supply chain and operations management, as an integrative AI architecture for building resilience is proposed, and the conceptualization of adaptive supply chains is advanced, while also providing guidance for managers and policymakers who are interested in leveraging AI as a strategic tool for building resilience in an ever-increasingly volatile environment.
Supply Chain Resilience, Machine Learning, Disruption Mitigation Frameworks, Intelligent Supply Chain Architectures
Decoding Customer Satisfaction and Loyalty in the Era of Smartphone Shopping Applications in India– Moderating Effect of Experience
The widespread adoption of smartphones has revolutionized individual activities, enabling users to engage in diverse tasks at any time and from any location. By recognizing this shift, marketers are increasingly tailoring their strategies through a mobile-first approach to enhance the user experience. This trend is particularly prominent in emerging markets like India, where mobile shopping applications are experiencing rapid growth. In the face of intensified market competition and evolving customer expectations, understanding the determinants of customer satisfaction and loyalty is paramount for marketers in mobile shopping applications. This study delves into the post-adoption behavior of customers, presenting a research model integrating the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model (ECM). Drawing data from 535 current users of mobile shopping applications, the empirical validation of the research framework reveals that perceived trust, perceived usefulness, confirmation, and attitude emerge as pivotal factors influencing satisfaction. Additionally, loyalty is shaped by satisfaction, perceived usefulness, attitude, and perceived trust. The study further elucidates the indirect effects of mobile shoppers' beliefs on satisfaction and loyalty. Implications and avenues for future research are discussed, shedding light on the intricate dynamics of customer interaction in the mobile shopping landscape.
Mobile shopping application, loyalty, satisfaction, price saving orientation, multi-group analysis
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Inside the School Ecosystem: Examining the Relationship Between School Culture and Teachers’ Job Satisfaction Through Stoll and Fink’s Framework
This research investigated how school culture influences teachers’ job satisfaction across 19 secondary schools within the DepEd Schools Division of Siquijor. Anchored in Stoll and Fink’s School Culture Framework and Herzberg’s Two-Factor Theory, the study explored the impact of various school culture types namely Moving, Cruising, Strolling, Struggling, and Sinking on teachers’ levels of job satisfaction. The findings underscore the critical role of school culture in shaping key organizational outcomes within the educational setting. The primary goal of this study was to assess the extent to which different cultural environments affect teachers’ overall job satisfaction. A descriptive correlational design was utilized, drawing data from teachers through a school culture assessment tool and a modified job satisfaction survey. Descriptive and inferential statistics were used to analyze the data. Results show that Moving Culture stood out with a strong positive correlation to teachers’ job satisfaction. In contrast, Cruising Culture, Strolling Culture, and Struggling Culture all showed non-significant negative correlations. Finally, Sinking Culture shows the strongest negative correlation with teachers’ job satisfaction. These findings highlight the importance of school culture in cultivating supportive and motivating work environments for teachers. The study recommends targeted interventions concerning culture enhancement strategies for schools exhibiting less desirable cultural characteristics to elevate teacher morale and institutional performance.
school culture, teachers’ job satisfaction, DepEd
Perceived Academic Self-Efficacy of Department Heads at Majmaah University and Its Relationship to Crisis Management Performance
This study aimed to examine the degree to which department heads at Majmaah University practice perceived academic self-efficacy and to explore its relationship with the development of their performance in crisis management. The study adopted a descriptive survey design and was conducted on a sample of 30 department heads. To achieve the study objectives, the Perceived Academic Self-Efficacy Scale was administered after establishing its psychometric properties in terms of validity and reliability. The results indicated that the level of perceived academic self-efficacy among department heads was high, as was their level of performance in crisis management. The findings also revealed no statistically significant differences in perceived academic self-efficacy attributable to gender, while statistically significant differences were found according to years of experience. Furthermore, the results demonstrated a strong, positive, and statistically significant correlation between perceived academic self-efficacy and crisis management performance. In addition, perceived academic self-efficacy was found to be predictable based on crisis management performance, with an explanatory power of 45%. In light of these findings, the study recommends greater attention to professional development programs aimed at enhancing the perceived academic self-efficacy of department heads, with particular emphasis on integrating crisis management skills into such programs. Moreover, perceived academic self-efficacy may serve as a valuable indicator for predicting the capacity of academic leaders to effectively confront and manage crises.
Perceived academic self-efficacy, crisis management, department heads, academic leadership, higher education, professional development
What Do We Know So Far About the Dark Triad at the Workplace? A Socio-Analytic Adaptationist Perspective
In this research, we reviewed how people with “dark” personalities adapt to their workplace. Although often viewed as socially undesirable for violating norms of cooperation and social exchange, behaviors associated with the “dark side” of personality may reflect a “predator” adaptation, which enables individuals with “dark” personalities to thrive in competitive situations. To date, little is known about how these individuals adapt to their workplace. Nevertheless, we strongly believe that 1) the predatory approach they use in their relationships during their time off is also manifested in their workplace relationships and 2) understanding how these individuals operate is important because it may help practitioners know how to approach them. Considering the existing literature, it seems that individuals with “dark” traits seek influential professional positions, such as leaders, managers, politicians, and entrepreneurs, which they try to occupy through opportunistic and exploitative behaviors often linked with detrimental organizational outcomes, such as low job performance and counterproductive work behaviors. This orientation, especially in the case of those high in psychopathy and Machiavellianism, may reflect their fast life history strategy manifested in getting ahead by getting against, in contrast to getting along. On the other hand, narcissists are initially perceived as good employees and also as good leaders due to their charisma. However, as soon as they spend enough time in the workplace, they are rated as questionable colleagues and managers by their co-workers and followers respectively. Variability in organizational outcomes between the Dark Triad traits, along with key issues and controversies are addressed.
Dark Triad, organizational behavior, job performance, leadership, life history theory
Nation Branding through Tourism in Qatar: An Exploratory Review and Future Research Agenda
As countries compete for tourism, investment, and global recognition, nation branding has become an important topic for researchers and decision-makers. Tourism is a visible and significant part of nation branding, shaping how international audiences view a country. This study explores how tourism fits into Qatar’s nation-branding strategy, with a focus on institutional collaboration and ongoing tourism projects rather than short-term events such as mega sporting events. This study uses a review-based exploratory method, drawing on academic research, policy papers, official tourism websites, and user-generated travel content. It describes and maps the main tourism initiatives and organisations involved in branding Qatar as a destination. The analysis looks at the roles of Qatar Tourism, Visit Qatar, Discover Qatar, and Qatar Airways, and how they promote tourism and shape experiences for international audiences. The study also reviews independent travel vlogs from 2025 to show how international visitors experience and present Qatar. This study does not aim to measure branding effectiveness or test theories. Instead, it provides an initial overview of tourism-led nation branding in Qatar and identifies areas that require further research. By explaining current practices and examples, the paper sets the stage for future research on tourism-driven nation branding in Qatar and the Gulf Cooperation Council region.
Nation Branding, Tourism Governance, Tourism Promotion, Qatar, Gulf Cooperation Council (GCC)
Impact of Digital Governance Initiatives on Citizen Participation in Institutional Processes: A Case Study of the Citizens App of Ghana Gov
Digital governance is changing the dynamics between citizens and their governments. Initiatives in digital governance, like Ghana’s Citizens App, strive to boost citizen involvement and enhance the delivery of public services. This research evaluates the influence of the Ghana Gov Citizens App on citizen participation and service efficiency, utilising a cross-sectional survey method with 310 valid responses collected from the Greater Accra Region. The study applies the Technology Acceptance Model (TAM) to analyse accessibility, engagement, and perceived effectiveness. Key results indicate that 55.16% of participants have downloaded the app, with higher rates of adoption among males (51.90%) and users in the middle age group (50.00% for those aged 25–34). Educational levels also affected usage, as users with secondary education (56.9%) and Master’s degree holders (61.7%) demonstrated greater engagement. The app's ease of downloading showed a strong correlation with the frequency of use (r = 0.433, p < 0.01) and satisfaction with its features (r = 0.576, p < 0.01). Nonetheless, trust in the app was closely linked to perceptions of efficiency (r = 0.929, p < 0.01), emphasizing the significance of dependable performance. Participants rated the app as being more efficient than traditional approaches (mean = 1.6 on a 5-point scale) and noted that they could complete transactions without needing to visit in person (mean = 3.04). In spite of these improvements, the moderate satisfaction ratings (mean = 2.35) suggest that there is still room for enhancement in terms of responsiveness and usability. The research concludes that, although the Citizens App has made notable progress in Ghana’s digital governance, issues such as the digital divide, concerns about data privacy, and inconsistent service quality still exist. Recommendations include focused outreach to marginalized groups, optimizing backend processes, and increasing transparency to build trust. These findings highlight the capacity of digital tools to reshape interactions between citizens and the state while stressing the need for inclusive and user-friendly design.
Digital governance, citizen participation, Ghana Gov Citizens App, Technology Acceptance Model, Public service delivery
From Static Canvas to Dynamic Flow: A Grounded Theory–Based Integration of Literary Evidence with Process-Oriented Business Models
Business model research over the past two decades has generated a fragmented landscape of component-based taxonomies. While these taxonomies document static structural elements of business models, including value creation, customer segments, key resources, revenue streams, and partner ecosystems, the dynamic interaction between components remains conceptually underdetermined. This article pursues a Grounded Theory-based literature analysis through selective coding to identify a core category—Flow—that relationally integrates all previously identified components. The analysis examined 927 publications and six thematic research clusters, demonstrating that business models are implicitly operationalized as processes yet visually represented as static boxes. The resulting Business Model Flow Chart is presented not as a conceptual innovation but as a deliberate explication of latent logics present in existing literature. The article accomplishes a methodically grounded synthesis of two separate literature analyses through selective coding and establishes foundations for subsequent empirical validation.
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
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.
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
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
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
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
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
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