Chikezie Paul-Mikki Ewim’s scientific contributions

In today's competitive business landscape, optimizing workforce productivity is paramount for organizations striving to achieve operational excellence and maintain a competitive edge. This explores the significance of leveraging data-driven metrics to enhance workforce productivity, drawing insights from Agile teams' practices. The review discusses the importance of workforce productivity in today's business environment and highlights the role of data-driven metrics in achieving this goal. It provides an overview of Agile methodology and its relevance in improving productivity, emphasizing its principles of collaboration, adaptability, and iterative improvement. This delves into the concept of workforce productivity metrics, distinguishing between traditional and data-driven approaches. It identifies key performance indicators (KPIs) used to measure productivity and discusses the challenges associated with measuring and interpreting these metrics effectively. Furthermore, the review outlines the application of Agile principles in project management and team collaboration, showcasing its benefits in enhancing productivity and efficiency. It emphasizes the importance of identifying relevant metrics for Agile teams, collecting and analyzing productivity data, and using data visualization techniques for insights and decision-making. Case studies of successful implementation, illustrating how Agile teams leverage data-driven insights to improve sprint planning, retrospectives, and overall project delivery. It discusses challenges such as resistance to change and data accuracy, along with best practices for overcoming them. Additionally, the abstract explores future trends and opportunities in workforce productivity measurement, including emerging technologies such as artificial intelligence and machine learning. It concludes by summarizing key insights and recommendations from Agile teams and offering final thoughts on the future of workforce productivity optimization through data-driven approaches.

In today's competitive business landscape, Small and Medium Enterprises (SMEs) face unique challenges in building and maintaining strong customer relationships. An AI-driven personalization framework offers a transformative solution by enabling SMEs to deliver highly targeted and individualized customer experiences, improving both engagement and retention rates. This review outlines how artificial intelligence (AI) can empower SMEs by integrating data-driven insights with customer interaction processes to revolutionize business practices. AI-driven personalization leverages machine learning algorithms, natural language processing (NLP), and predictive analytics to analyze customer behaviors, preferences, and feedback. By aggregating data from various sources-such as online interactions, purchase histories, and social media activity-AI can generate personalized recommendations, offers, and communication strategies that resonate with individual customers. The framework facilitates dynamic customer segmentation, allowing SMEs to tailor marketing efforts and enhance service delivery. The personalization process also extends beyond marketing by optimizing customer support through AI-powered chatbots and recommendation systems, which provide real-time solutions and advice. This level of personalization fosters stronger emotional connections, increasing customer satisfaction and brand loyalty. Additionally, AI-driven insights enable SMEs to anticipate customer needs, predict churn rates, and proactively address potential issues, thereby boosting retention rates. For SMEs, the implementation of an AI-driven personalization framework is not only cost-effective but scalable, making it accessible even to businesses with limited resources. As SMEs increasingly adopt digital tools, the integration of AI-based personalization becomes essential for staying competitive in a rapidly evolving market. This review highlights the potential of AI in transforming customer engagement strategies for SMEs by offering a tailored, efficient, and sustainable approach to enhancing customer experiences.

Socioeconomic inequities persist as formidable barriers to progress in underserved communities, fueled by a lack of financial literacy and limited access to legal resources. Addressing these systemic challenges requires innovative and integrative solutions. The present study introduces the Legal Empowerment and Financial Literacy (LEFL) Framework, a transformative and scalable model designed to bridge these gaps. By combining comprehensive financial education with accessible legal empowerment initiatives, the LEFL-Framework equips marginalized populations with the tools to build resilience, achieve economic stability, and assert their legal rights. The framework emphasizes inclusivity and accessibility, fostering meaningful engagement through community-based programs, digital tools, and culturally relevant curricula. It leverages collaborative partnerships among non-governmental organizations, financial institutions, and legal professionals to deliver cohesive and impactful education. Case studies and evidence-based strategies illustrate the framework’s success in reducing poverty, enhancing financial decision-making, and increasing access to justice. Key benefits of the LEFL-Framework include improved economic resilience, heightened legal awareness, and measurable progress toward equity. However, challenges such as resource constraints and cultural stigmas must be addressed for effective implementation. This study explores these issues, offering actionable insights for policymakers, practitioners, and researchers in the field. As a replicable model, the LEFL-Framework holds potential for nationwide and global application, serving as a cornerstone for addressing systemic inequities and fostering sustainable development. By integrating financial literacy and legal empowerment, this transformative approach provides a roadmap to uplift underserved communities, enabling them to navigate and overcome structural barriers in pursuit of equitable and inclusive growth.

The convergence of blockchain and artificial intelligence (AI) is poised to revolutionize energy trading in the oil and gas industry by 2025. These technologies are enhancing transaction efficiency, improving market transparency, and enabling more secure and automated trading processes. Blockchain, through decentralized ledgers and smart contracts, ensures immutable transaction records, reducing counterparty risks and enabling real-time settlements. AI-driven predictive analytics optimize energy trading strategies by processing vast datasets, detecting market patterns, and forecasting price fluctuations with high accuracy. Smart contracts, self-executing agreements embedded in blockchain networks, eliminate intermediaries, reducing transaction costs and mitigating fraudulent activities. These digital agreements automate trade settlements based on predefined conditions, expediting contract execution while ensuring compliance with regulatory requirements. Additionally, AI-powered algorithmic trading enhances market responsiveness, enabling energy companies to make data-driven decisions in real-time. By leveraging machine learning and deep learning models, traders can optimize portfolio management, anticipate supply chain disruptions, and manage price volatility more effectively. Moreover, blockchain fosters trust among stakeholders by providing a tamper-proof audit trail of transactions, enhancing regulatory compliance, and reducing operational inefficiencies. The integration of AI with blockchain-enabled trading platforms allows for automated risk assessment, fraud detection, and enhanced liquidity management. These innovations collectively contribute to a more resilient and adaptive energy trading ecosystem, accommodating the growing complexity of global oil and gas markets. Despite these advantages, challenges such as regulatory uncertainties, interoperability issues, and cybersecurity risks persist. The successful implementation of blockchain and AI in energy trading requires standardized frameworks, industry-wide collaboration, and robust cybersecurity measures. Nonetheless, early adopters stand to gain a competitive edge by capitalizing on these technologies to optimize trading strategies, improve asset utilization, and enhance operational efficiency. As the oil and gas industry embraces digital transformation, blockchain and AI are becoming indispensable tools for modernizing energy trading. Their potential to foster transparency, streamline operations, and mitigate risks underscores their role in shaping the future of energy markets. Companies that integrate these technologies effectively will be better positioned to navigate evolving market dynamics and achieve sustained growth in a rapidly digitalizing energy sector.

In the evolving U.S. retail and manufacturing sectors, integrating digital tools into project management enhances efficiency, collaboration, and customer experiences. This framework provides a structured approach built on four key components: assessment, tool selection, change management, and continuous improvement. Organizations begin by evaluating current project management processes to identify gaps and digital enhancement opportunities. Selecting the right tools—such as project management software, collaboration platforms, and data analytics—ensures alignment with organizational goals. A phased implementation approach minimizes disruption and maximizes adoption. Effective change management, including stakeholder engagement and training, fosters a culture of digital transformation. Continuous evaluation establishes performance metrics, enabling adjustments to optimize tool effectiveness. By systematically assessing, selecting, and integrating digital tools while managing change and maintaining adaptability, organizations enhance project management capabilities. This framework drives innovation, streamlines processes, and strengthens customer focus, positioning businesses for success in a competitive landscape. Keywords: Framework, Digital Tools, Integration, Retail, Manufacturing, Project Management.

Small and Medium-sized Enterprises (SMEs) are crucial to Nigeria’s economy, driving employment, innovation, and growth. However, they face significant challenges such as limited technology access, inadequate infrastructure, and regulatory barriers. This concept paper proposes a theoretical framework for empowering Nigerian SMEs through strategic technology adoption to enhance resilience and sustainable development. Grounded in the Technology Acceptance Model (TAM), Diffusion of Innovations (DOI) theory, and the Resource-Based View (RBV), the framework examines factors influencing technology adoption. Key drivers include perceived usefulness, ease of use, cost, and accessibility, while barriers encompass financial constraints and regulatory complexities. The framework highlights government policies, financial incentives, and training programs as critical enablers of technology adoption. Technology adoption enhances SME resilience by improving operational efficiency, reducing costs, and expanding market reach through digital platforms and e-commerce. Policy recommendations include investing in ICT infrastructure, offering financial incentives, providing digital training, and streamlining regulations to foster innovation. Public-private partnerships and business incubators play a vital role in facilitating digital transformation. Robust monitoring and evaluation mechanisms are essential to assess the impact of technology adoption on SME performance. Continuous feedback will ensure that initiatives remain effective and responsive to SME needs. This framework provides a roadmap for leveraging technology to strengthen Nigerian SMEs, fostering economic growth, and positioning them competitively in the digital global economy.

Service quality in the banking sector is a critical determinant of customer satisfaction, loyalty, and competitive advantage. As banks strive to meet the evolving expectations of customers and navigate an increasingly complex regulatory landscape, the role of data analytics in enhancing service quality has become paramount. This review explores how data analytics can be leveraged to improve service quality in the banking sector, offering insights into the methods, benefits, and practical applications of this approach. The review begins by outlining the importance of service quality in banking, emphasizing its impact on customer retention and the overall success of financial institutions. Traditional methods of assessing and improving service quality, such as customer surveys and manual audits, are often limited by their reactive nature and the inability to handle large volumes of data effectively. In contrast, data analytics provides a proactive and comprehensive approach, enabling banks to identify patterns, predict trends, and make data-driven decisions that enhance service delivery. Data analytics encompasses various techniques, including descriptive, predictive, and prescriptive analytics, each offering unique benefits for service quality improvement. Descriptive analytics allows banks to gain insights from historical data, identifying key areas for improvement. Predictive analytics uses statistical models and machine learning algorithms to forecast future customer behavior, enabling banks to anticipate needs and address potential issues before they escalate. Prescriptive analytics goes a step further by recommending specific actions to optimize service quality, based on the analysis of past and predicted data. Key areas where data analytics can significantly enhance service quality in banking include customer relationship management (CRM), operational efficiency, and risk management. In CRM, data analytics enables banks to personalize services, segment customers effectively, and predict their needs with greater accuracy. This personalized approach not only enhances customer satisfaction but also fosters loyalty and long-term relationships. Operational efficiency is another critical area where data analytics can drive improvements. By analyzing transaction data, banks can optimize processes, reduce waiting times, and improve the overall customer experience. For instance, data-driven insights can help banks streamline branch operations, optimize ATM placements, and manage workforce allocation more effectively. Risk management, particularly in the areas of fraud detection and credit risk assessment, also benefits from data analytics. Advanced analytics techniques can detect unusual patterns and flag potential fraud in real-time, reducing the risk of financial losses and enhancing trust. Similarly, predictive models can assess credit risk more accurately, ensuring that banks make informed lending decisions and maintain a healthy loan portfolio. The adoption of data analytics in banking is not without challenges. Issues such as data privacy, security, and the need for skilled personnel to interpret and act on data insights are significant considerations. However, with the right strategies and technologies in place, these challenges can be effectively managed, paving the way for substantial improvements in service quality. Data analytics offers a powerful toolset for banks aiming to enhance service quality. By leveraging data-driven insights, banks can deliver more personalized, efficient, and secure services, ultimately leading to greater customer satisfaction and competitive advantage. As the banking sector continues to evolve, the integration of data analytics into service quality improvement strategies will be essential for staying ahead in a competitive market.

In today's digital age, financial services institutions are increasingly turning to data-driven strategies to revolutionize customer experience management (CEM). This Review explores how these strategies are reshaping the industry landscape, focusing on the key trends, challenges, and opportunities. The financial services sector is undergoing a significant transformation driven by technological advancements and changing customer expectations. Data-driven strategies are at the forefront of this transformation, enabling institutions to gain deeper insights into customer behavior, preferences, and needs. By leveraging big data analytics, artificial intelligence (AI), and machine learning (ML), financial institutions can personalize their offerings, enhance customer engagement, and drive loyalty. One of the key trends in data-driven CEM is the use of predictive analytics to anticipate customer needs and provide proactive solutions. By analyzing historical data and customer interactions, financial institutions can identify patterns and trends, enabling them to offer personalized products and services. This approach not only enhances the customer experience but also improves operational efficiency and reduces costs. However, implementing data-driven CEM strategies comes with its challenges. Data privacy and security concerns are paramount, requiring institutions to comply with strict regulations and ensure the protection of customer information. Additionally, integrating data from various sources and legacy systems can be complex and time-consuming, requiring significant investment in technology and infrastructure. Despite these challenges, the opportunities presented by data-driven CEM in financial services are vast. By harnessing the power of data, institutions can gain a competitive edge, increase customer satisfaction, and drive business growth. Furthermore, data-driven CEM can enable financial institutions to develop innovative products and services that meet the evolving needs of customers in an increasingly digital world. In conclusion, data-driven strategies are revolutionizing customer experience management in financial services, offering institutions the ability to personalize offerings, enhance engagement, and drive loyalty. While challenges exist, the opportunities presented by data-driven CEM are substantial, making it a crucial focus area for institutions looking to stay ahead in a rapidly evolving industry landscape.

Blockchain technology has emerged as a transformative tool in the fintech industry, offering unprecedented opportunities to enhance real-time auditing processes, financial transparency, and fraud detection. This study explores how the integration of blockchain with advanced analytics can revolutionize traditional auditing practices, addressing critical challenges such as data tampering, inefficiencies, and lack of transparency. By leveraging blockchain’s immutable ledger and decentralized architecture, real-time auditing becomes more accurate, secure, and efficient, enabling continuous monitoring and verification of transactions. Advanced analytics, when combined with blockchain, empowers auditors with predictive capabilities, anomaly detection, and actionable insights, ensuring a proactive approach to fraud prevention. The inherent traceability of blockchain allows all transactions to be recorded in a tamper-proof manner, significantly reducing opportunities for fraudulent activities and improving compliance with regulatory standards. Moreover, the integration supports automated reconciliation processes and smart contracts, further streamlining financial operations and minimizing human errors. This paper also highlights the role of blockchain in fostering stakeholder trust through enhanced transparency, as all parties involved in financial ecosystems gain access to real-time, verifiable financial records. Key applications discussed include Know Your Customer (KYC) processes, anti-money laundering (AML) initiatives, and secure cross-border transactions, showcasing how blockchain addresses industry pain points. Despite its transformative potential, challenges such as scalability, integration with legacy systems, and regulatory uncertainties remain. The study discusses strategies for overcoming these hurdles, including collaborative efforts between fintech firms, regulators, and technology providers to establish standards and best practices. In conclusion, integrating blockchain technology with advanced analytics heralds a new era for financial transparency and fraud detection in the fintech sector. By reshaping real-time auditing processes, blockchain not only enhances operational efficiency but also sets a foundation for a more secure and trustworthy financial ecosystem. Keywords: Blockchain Technology, Real-Time Auditing, Financial Transparency, Fraud Detection, Fintech, Advanced Analytics, Regulatory Compliance, Smart Contracts, Financial Ecosystems, KYC, AML.

Artificial Intelligence (AI) and data-driven insights are revolutionizing Customer Relationship Management (CRM) in the financial services sector by enhancing customer engagement, streamlining operations, and enabling personalized experiences. By integrating advanced AI technologies such as machine learning, natural language processing (NLP), and predictive analytics, CRM systems can analyze vast amounts of customer data to uncover actionable insights, predict behaviors, and deliver tailored solutions. This transformation helps financial institutions build stronger relationships with customers while improving efficiency and competitiveness in a rapidly evolving market. AI-driven CRM systems provide financial institutions with tools to anticipate customer needs, segment audiences, and automate routine processes. Predictive analytics allows organizations to identify potential opportunities and risks, optimize marketing campaigns, and enhance customer retention. Natural language processing powers chatbots and virtual assistants, enabling real-time, personalized customer support while reducing operational costs. Additionally, data visualization and advanced reporting features enhance decision-making by offering clear and actionable insights to stakeholders. The adoption of AI and data-driven CRM solutions presents significant benefits, including increased customer satisfaction, enhanced loyalty, and improved operational efficiency. However, challenges such as data security concerns, regulatory compliance, and the complexity of integrating AI with existing systems remain critical barriers. Financial institutions must also address ethical considerations, such as ensuring transparency in AI decision-making and avoiding biases in customer interactions. This paper explores the role of AI and data-driven insights in transforming CRM within financial services, highlighting their applications, benefits, and challenges. It also examines successful case studies to provide actionable strategies for effective implementation. By leveraging AI and data-driven insights, financial institutions can revolutionize customer relationship management, drive sustainable growth, and remain resilient in an increasingly digital economy. Keywords: Artificial Intelligence, Data-Driven Insights, Customer Relationship Management, Financial Services, Predictive Analytics, Machine Learning, Natural Language Processing, Customer Engagement, Personalized Experiences, CRM Transformation.