Oluwakemi Famoti’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.

The primary objective of this paper is to examine how government policies can promote digital inclusion and equity across the United States of America. It emphasizes the need for strategic interventions to bridge the digital divide, ensuring that all segments of society can benefit from technological advancements. The paper underscores the role of government in providing infrastructure, education, and regulatory frameworks that support digital access and literacy. Central to the paper is the exploration of various policy measures and their impact on digital inclusion. It analyzes successful initiatives such as national broadband strategies, digital literacy programs, and subsidies for low-income households. The paper discusses how these policies can be tailored to address the specific needs of marginalized communities, including rural populations, the elderly, and people with disabilities. The concept paper delves into theoretical models of digital inclusion, such as the Digital Divide Theory and the Capability Approach. It reviews these models to understand the multifaceted nature of digital exclusion and the socio-economic factors contributing to it. The paper also highlights the importance of a holistic approach that combines infrastructure development with digital skills training and public awareness campaigns. Addressing the practical challenges of promoting digital inclusion, the paper identifies issues such as budget constraints, rapid technological changes, and resistance to digital adoption. It proposes solutions including public-private partnerships, continuous policy evaluation, and targeted interventions for high-need areas. The paper emphasizes the need for collaboration between government, industry, and civil society to create a sustainable and inclusive digital environment. The anticipated outcomes of effective government policies include increased digital literacy, broader access to online services, and enhanced socio-economic opportunities for all citizens. These outcomes are expected to lead to greater social cohesion, economic growth, and improved quality of life. The paper argues that promoting digital inclusion is not only a matter of social justice but also a strategic imperative for national development. The paper provides a comprehensive framework for understanding and addressing digital exclusion through government intervention. By implementing inclusive policies and fostering collaboration across sectors, The United States of America government can ensure that all its citizens are empowered to participate in the digital economy. The paper calls for ongoing research and practical efforts to refine and enhance digital inclusion strategies, ensuring they remain responsive to the evolving technological landscape and societal needs. Keywords: Digital Access, Equity, Inclusion, USA.

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.