Fair East Publishers: E-Journals
Not a member yet
1902 research outputs found
Sort by
Developing a framework for business analysts to bridge stakeholder and technical team gaps: Enhancing collaboration and project success
Get PDFIn today’s dynamic business environment, projects often fail due to misalignment between stakeholders’ needs and technical implementation. Business Analysts (BAs) play a crucial role in bridging the gap between business stakeholders, who focus on strategic objectives, and technical teams, who emphasize implementation details. However, without a structured approach, BAs may struggle to facilitate effective communication and collaboration. Developing a robust framework for BAs can significantly enhance project outcomes by ensuring clear alignment between stakeholder expectations and technical deliverables. A critical component of this framework involves stakeholder identification and analysis. BAs need to clearly understand the interests, expectations, and influence levels of different stakeholders to prioritize requirements effectively. By utilizing tools such as stakeholder matrices and engagement plans, BAs can foster trust and manage expectations, ensuring that business objectives are translated into actionable technical specifications. Effective requirements gathering and documentation is another key element. Techniques such as workshops, interviews, and process mapping can be leveraged to capture detailed business needs. Translating these into clear, structured documentation such as user stories and process flow diagrams ensures technical teams have a precise understanding of project goals, reducing the likelihood of misinterpretation. The use of collaborative tools is essential for enhancing communication between stakeholders and technical teams. Platforms like JIRA and Confluence, along with Agile methodologies, promote iterative feedback loops, ensuring that both parties remain aligned throughout the project lifecycle. Regular check-ins and sprint reviews enable stakeholders to provide real-time feedback, allowing technical teams to adjust solutions to evolving business needs promptly. Additionally, continuous training and upskilling of BAs is vital for the framework’s success. As projects become more complex, BAs must be proficient not only in communication and documentation but also in leveraging data analytics and emerging technologies to drive insights. By developing a comprehensive framework that encompasses stakeholder engagement, clear documentation, and collaborative practices, organizations can optimize project success rates, streamline workflows, and foster a culture of continuous improvement. Ultimately, the role of BAs is becoming increasingly strategic as organizations strive to remain agile and responsive in a fast-evolving market landscape.
Keywords: Business Analysts, Agile Methodologies Framework, Review
Social media and entrepreneurial competence: the impact of Linkedin on boosting entrepreneurial engagement of Gen-Z
No full textResearch Objective: This study examines the impact of LinkedIn usage on entrepreneurial competence and engagement of Gen-Z aged between 18-27. Motivation: This study is motivated by the increased dependence young entrepreneurs have on social media as a channel for professional development and networking. The study builds on works of Banerji & Reimer, (2019) on start-up founders and their LinkedIn connections, Franc, Stampfl, & Geyer, (2024) worked-related learning on LinkedIn, and Vitelar (2019) on Gen-Z and the use of social media. Idea: This research tests the direct effect of the usage of LinkedIn on Gen-Z individuals' entrepreneurial competence and engagement. It assumes that high engagement in learning and networking activities on LinkedIn will be followed by an equivalent improvement in their entrepreneurial outcomes. In this research, the independent variables are the patterns of usage on LinkedIn, while the dependent variables are entrepreneurial competence and engagement. Data: The quantitative research methodology was adopted, and the survey was conducted through Google Forms among 350 Gen-Z users on the professional networking site LinkedIn. Tools: Descriptive statistics, Pearson's correlation, and multiple regression analyses were used for the collected data. Findings: The results indicated that the different usage patterns—such as the frequency of login and engagement on LinkedIn Learning—had a significantly positive correlation with both entrepreneurial competence and engagement. Regression analysis pointed out entrepreneurial competence as one of the chief predictors for entrepreneurial engagement. Therefore, it is concluded that LinkedIn is quite instrumental in enhancing the entrepreneurial capacity of Gen-Z; it therefore gives relevant insights to entrepreneurs and the developers of this particular platform. Contribution: The paper thereby provides empirical evidence on the specific impact of LinkedIn on Gen-Z entrepreneurs, which enriches the literature of social media and entrepreneurship.
Keywords: LinkedIn, Gen-Z, Entrepreneurial Competence, Social Media, Professional Networking
Enhancing financial reporting and management efficiency through enterprise resource planning (ERP) systems: A theoretical review for large-scale energy operations
No full textEnterprise Resource Planning (ERP) systems have emerged as essential tools in streamlining financial reporting and improving management efficiency across various industries, including large-scale energy operations. This theoretical review explores how ERP systems enhance financial reporting by integrating real-time data, automating financial processes, and fostering transparency. In energy operations, where financial activities are complex and data-intensive, ERP systems centralize financial data, reducing errors, improving compliance, and supporting decision-making through enhanced visibility and accuracy. By automating tasks such as budgeting, forecasting, and financial statement generation, ERP systems enable energy companies to efficiently manage their financial resources, thus optimizing cost control and profitability. Additionally, ERP systems provide significant benefits for management efficiency, particularly in large-scale operations. The integration of financial, operational, and managerial processes within a single platform allows for better resource allocation, improved communication across departments, and faster response times to market fluctuations. These capabilities are crucial in the energy sector, where companies face volatile market conditions, regulatory pressures, and high operational risks. ERP systems enhance managerial decision-making by providing real-time insights into key performance indicators (KPIs), enabling executives to make data-driven decisions that support long-term strategic goals. However, implementing ERP systems in large-scale energy operations presents challenges, including high costs, lengthy implementation periods, and the need for employee training. This review also examines these barriers and suggests strategies for successful ERP adoption, including phased implementation, stakeholder engagement, and customization to industry-specific needs. In conclusion, ERP systems hold the potential to revolutionize financial reporting and management efficiency in large-scale energy operations. By leveraging ERP's real-time data integration and process automation, energy companies can significantly improve their financial management, enhance decision-making, and adapt more quickly to market changes.
Keywords: ERP Systems, Financial Reporting, Management Efficiency, Large-Scale Energy Operations, Process Automation, Real-Time Data Integration, Decision-Making, Cost Control, Market Fluctuations, Energy Sector
Artificial intelligence in healthcare forecasting: Enhancing market strategy with predictive analytics
No full textThis research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting.
Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare Strateg
Advancing pharmaceutical care in rural and underserved communities: Strategies for improving global healthcare access
No full textPharmaceutical care in rural and underserved communities faces significant challenges, including limited access to healthcare services, geographic isolation, and infrastructure deficiencies. This review explores innovative strategies to optimize patient care delivery and expand access to essential medications in these areas. It highlights the potential of telepharmacy and digital health solutions in bridging the gap between patients and pharmacists, ensuring timely consultations, and enhancing medication adherence. Additionally, community-based healthcare initiatives and pharmacist-led interventions are examined as practical solutions for improving care delivery at the grassroots level. The review further discusses necessary policy reforms and incentives that can support healthcare professionals in underserved regions, along with long-term strategies such as localized drug production and international collaboration to sustain improvements in global healthcare access. By integrating these approaches, healthcare disparities can be reduced, leading to improved outcomes for vulnerable populations worldwide.
Keywords: Telepharmacy, Rural Healthcare. Medication Access, Pharmacist-led Interventions, Policy Reforms
AI-Powered customer experience optimization: Enhancing financial inclusion in underserved communities
No full textThe integration of Artificial Intelligence (AI) into customer experience optimization holds transformative potential for advancing financial inclusion in underserved communities. This review explores how AI-driven tools can enhance personalized banking services, streamline customer engagement, and improve access to financial products for individuals who are often marginalized by traditional banking systems. By leveraging AI algorithms, financial institutions can analyze vast amounts of customer data to identify unique needs, preferences, and behaviors, enabling the delivery of tailored financial solutions. These AI-powered systems can also automate routine tasks, such as account management and customer support, ensuring that banking services are more accessible, efficient, and cost-effective. A key benefit of AI in this context is its ability to overcome barriers such as geographic isolation and lack of financial literacy, which often hinder financial inclusion. AI-driven chatbots, for instance, can provide 24/7 support in local languages, guiding users through complex financial processes. Moreover, AI enables the creation of personalized financial literacy programs, tailored to the specific needs of underserved populations, fostering a deeper understanding of financial products and encouraging greater participation in the financial system. Additionally, AI-powered fraud detection systems enhance trust and security in financial transactions, a critical factor in encouraging participation from communities traditionally skeptical of formal banking systems. The use of predictive analytics further supports the development of micro-loans and credit products suited to the financial realities of low-income individuals, reducing default rates and improving access to capital. In conclusion, AI-powered customer experience optimization offers a powerful strategy for financial institutions seeking to expand their reach into underserved communities. By providing personalized, efficient, and secure banking experiences, AI can significantly contribute to financial inclusion efforts, driving economic empowerment and reducing inequalities. This study highlights the critical role of AI in reshaping financial services and creating inclusive opportunities for underserved populations.
Keywords: Artificial Intelligence, Financial Inclusion, Customer Experience Optimization, Underserved Communities, Personalized Banking, AI-Powered Tools, Financial Literacy, Fraud Detection, Predictive Analytics, Economic Empowerment
The influence of supply chain agility on FMCG SME marketing flexibility and customer satisfaction
No full textIn the fast-paced Fast-Moving Consumer Goods (FMCG) sector, small and medium-sized enterprises (SMEs) must adapt swiftly to changing market conditions and consumer demands to stay competitive. This review explores the influence of supply chain agility on marketing flexibility and customer satisfaction in FMCG SMEs. Supply chain agility, characterized by responsiveness, adaptability, and real-time decision-making, enables SMEs to respond quickly to market shifts, optimize operations, and deliver products efficiently. As a result, agile supply chains directly impact the marketing flexibility of SMEs by enabling faster product launches, customization, and rapid adjustments to promotional strategies based on market feedback. This also highlights how marketing flexibility, supported by agile supply chains, enhances customer satisfaction by meeting expectations related to product availability, quality, and service delivery. Agile supply chains allow FMCG SMEs to reduce stockouts, improve order fulfillment times, and adapt to customer preferences, creating a seamless experience that fosters brand loyalty. Furthermore, this review presents case studies of FMCG SMEs that have successfully implemented agile supply chains to boost marketing responsiveness and customer satisfaction, demonstrating real-world applications of these strategies. By examining the interrelationship between supply chain agility, marketing flexibility, and customer satisfaction, this review emphasizes the importance of an integrated approach to operations and marketing in FMCG SMEs. The findings suggest that investing in supply chain agility is critical for SMEs seeking to enhance their market adaptability and customer-centricity. The study concludes by offering recommendations for FMCG SMEs to develop agile supply chains that enable them to thrive in a competitive and ever-evolving marketplace, where customer satisfaction and rapid market response are key drivers of success.
Keywords: Supply Chain, FMCG SME, Marketing Flexibility, Review
ERM strategies for navigating financial stress: Lessons from US commercial banks
No full textThis study explores the Enterprise Risk Management (ERM) strategies employed by U.S. commercial banks during times of financial stress, with a focus on the 2008 financial crisis and the COVID-19 pandemic. It investigates how ERM frameworks enable banks to make risk-adjusted decisions and effectively manage credit risk, ensuring both operational resilience and long-term value preservation. The research highlights the critical role of ERM in guiding banks through periods of market volatility, emphasizing the need for a structured approach to risk identification, assessment, and mitigation. Through a detailed analysis, the study examines how commercial banks have adjusted their risk appetite and governance structures in response to economic disruptions, balancing short-term stability with long-term growth objectives. Key ERM components such as capital adequacy, liquidity management, and stress testing are reviewed to demonstrate their effectiveness in safeguarding financial institutions. Additionally, the research discusses the importance of leadership and governance in enhancing risk oversight and fostering a culture of risk awareness across banking operations. The findings offer valuable lessons for financial institutions on how to navigate future financial stresses by leveraging robust ERM frameworks. By examining these strategies, the paper provides insights into the adaptability and resilience of U.S. commercial banks in maintaining financial stability and shareholder value in the face of uncertainty..
Keywords: Enterprise Risk Management (ERM), Financial Stress, Credit Risk Management, Operational Resilience, U.S. Commercial Banks, Governance Structures
Streamlining budgeting and forecasting across multi-cloud environments with dynamic financial models
Get PDFThis review paper explores the complexities of financial management in multi-cloud environments, emphasizing the need for streamlined budgeting and forecasting processes. It begins by addressing the challenges inherent in managing finances across multiple cloud platforms, such as integration difficulties, data silos, and cost management issues. The paper then provides an overview of dynamic financial models, detailing their components and benefits in overcoming these challenges. It highlights the role of automation, real-time data utilization, and predictive analytics in enhancing financial accuracy and efficiency. Furthermore, the paper discusses the importance of collaborative platforms in fostering stakeholder communication and coordination. Finally, strategic planning, best practices for multi-cloud financial management, and emerging trends and prospects are outlined. The review underscores the significance of aligning financial models with organizational goals and leveraging advanced technologies to achieve optimal financial outcomes in multi-cloud settings.
Keywords: Multi-Cloud Financial Management, Budgeting and Forecasting, Dynamic Financial Models, Automation in Finance, Predictive Analytics
The intersection of financial modeling and public health: A conceptual exploration of cost-effective healthcare delivery
No full textThis review paper explores the intersection of financial modeling and public health, highlighting the importance of cost-effective healthcare delivery in contemporary health systems. Financial modeling serves as a crucial tool for policymakers, providing evidence-based insights that inform decision-making and optimize resource allocation. Key financial models, including cost-benefit analysis and predictive modeling, are examined for their applications in public health initiatives, particularly in areas such as preventive care and chronic disease management. The paper discusses core public health priorities—prevention, access, and quality—and the economic constraints faced by healthcare systems. Recommendations are provided for policymakers and healthcare professionals to enhance the application of financial models, emphasizing the need for training, investment in data infrastructure, and fostering cross-sector collaborations. By leveraging financial modeling, public health systems can achieve improved health outcomes while maximizing resource efficiency, ultimately contributing to a more sustainable and equitable healthcare landscape..
Keywords: Financial Modeling, Public Health, Cost-Effectiveness, Healthcare Delivery, Resource Allocation, Preventive Care