Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization’s strategic business objectives.

By now, it is commonly accepted that investments in information and communication technologies (ICTs) can facilitate macroeconomic growth in developed countries. Research standards in ICT for development (ICT4D) are high, and it is a basic expectation that a theoretically sound conceptual investigation should yield actionable results. An additional expectation is that an on-the-ground study conducted in each setting should add to the common body of knowledge based on theory. In other words, one is expected to make a connection between the world of concepts and the world of reality. Middle-range theories and frameworks could help connect the case studies with grand theories, by helping to create a theoretically sound and practically applicable research architecture of ICT4D. This book demonstrates how creative use of various data analysis methods (e.g., data mining [DM], data envelopment analysis [DEA], and structural equation modeling [SEM]) and conceptual frameworks (e.g., neoclassical growth accounting, chaos and complexity theories) may be utilized for inductive and deductive purposes to develop and to test, in step-by-step fashion, theoretically sound frameworks for a large subset of ICT4D research questions. Specifically, this book showcases the utilization of DM, DEA, and SEM for the following purposes: Identification of the relevant context-specific constructs (inductive application) Identification of the relationships between the constructs (inductive application) Development of a framework incorporating the constructs and relationships discovered (inductive application) Testing of the constructed framework (deductive application) The book takes a multi-theoretical perspective to economic development research. It starts with an overview of ICT4D. Next it covers such frameworks and theories as neoclassical growth accounting and the theory of complementarity, complex systems and chaos theories, and the product life cycle (PLC) theory. There are also nontechnical overviews of the DM and data analytic methods that can be used in this research. Also presented is evidence that human capital and investment capital are complementary and are reliable sources of economic growth. The book concludes with methodological frameworks to guide investment decisions and the formulation of strategic policy.

The Global South is recognized as one of the fastest growing regions in terms of Internet population as well as the region that accounts for the majority of Internet users. However, It cannot be overlooked that with increasing connectivity to and dependence on Internet-based platforms and services, so too is the potential increased for information and cybersecurity threats and attacks. Further, it has long been established that micro, small, and medium enterprises (MSMEs) play a key role in national economies, serving as important drivers of economic growth in Global South economies. Yet, little is known about information security, cybersecurity and cybercrime issues and strategies contextualized to these developing economies and MSMEs. Cybercrime and Cybersecurity in the Global South: Concepts, Strategies and Frameworks for Greater Resilience examines the prevalence, nature, trends and impacts of cyber-related incidents on Global South economies. It further explores cybersecurity challenges, potential threats, and risks likely faced by MSMEs and governments of the Global South. A major thrust of this book is to offer tools, techniques, and legislative frameworks that can improve the information, data, and cybersecurity posture of Global South governments and MSMEs. It also provides evidence-based best practices and strategies relevant to the business community and general Information Communication Technology (ICT) users in combating and preventing cyber-related incidents. Also examined in this book are case studies and experiences of the Global South economies that can be used to enhance students' learning experience. Another important feature of this book is that it outlines a research agenda to advance the scholarship of information and cybersecurity in the Global South. Features: Cybercrime in the Caribbean Privacy and security management Cybersecurity compliance behaviour Developing solutions for managing cybersecurity risks Designing an effective cybersecurity programme in the organization for improved resilience The cybersecurity capability maturity model for sustainable security advantage Cyber hygiene practices for MSMEs A cybercrime classification ontology

A resource on quantitative methods and methodologies for researchers, practitioners, and doctoral students Demonstrates, in a step-by-step fashion, the development of the theoretically sound general framework allowing for instantiating a variety of research models. Illustrates a process of constructing of a variety of methodological modules- components comprised of complementary quantitative data analytic methods Explores the negative societal effects of implications of ICT and discusses future research directions to mitigate such effects

A number of developing countries, including small island states have common problems that have affected their development and growth. Knowledge Management (KM) initiatives can be used to address some of these issues, but these developing countries need to understand what is needed to implement them, in order to improve economic conditions. While many of these countries have access to technologies that can be used to assist in knowledge management, relevant and low cost KM initiatives need to be considered in improving their existing KM processes. Sectors critical to the growth of these developing countries include health care, crime management, disaster recovery management, small and medium size enterprise development. Knowledge Management for Development: Domains, Strategies and Technologies for Developing Countries highlights the opportunities in these sectors and provides advice as to how these countries should go about understanding, building and adopting the relevant KM strategies and technologies. This book identifies appropriate technologies which should be considered to increase productivity within the identified sectors in the developing countries and also sectors in where knowledge management initiatives can yield maximum value. It also considers the constraints of these territories, recommending appropriate technologies and strategies for KM initiatives. It provides advice on how these technologies should be adopted in these sectors of developing countries. Investing in these strategies should benefit these countries development and growth.

Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science.

A number of developing countries, including small island states have common problems that have affected their development and growth. Knowledge Management (KM) initiatives can be used to address some of these issues, but these developing countries need to understand what is needed to implement them, in order to improve economic conditions. While many of these countries have access to technologies that can be used to assist in knowledge management, relevant and low cost KM initiatives need to be considered in improving their existing KM processes. Sectors critical to the growth of these developing countries include health care, crime management, disaster recovery management, small and medium size enterprise development. Knowledge Management for Development: Domains, Strategies and Technologies for Developing Countries highlights the opportunities in these sectors and provides advice as to how these countries should go about understanding, building and adopting the relevant KM strategies and technologies. This book identifies appropriate technologies which should be considered to increase productivity within the identified sectors in the developing countries and also sectors in where knowledge management initiatives can yield maximum value. It also considers the constraints of these territories, recommending appropriate technologies and strategies for KM initiatives. It provides advice on how these technologies should be adopted in these sectors of developing countries. Investing in these strategies should benefit these countries development and growth.

Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems.

"Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking" focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science.

By now, it is commonly accepted that investments in information and communication technologies (ICTs) can facilitate macroeconomic growth in developed countries. Research standards in ICT for development (ICT4D) are high, and it is a basic expectation that a theoretically sound conceptual investigation should yield actionable results. An additional expectation is that an on-the-ground study conducted in each setting should add to the common body of knowledge based on theory. In other words, one is expected to make a connection between the world of concepts and the world of reality. Middle-range theories and frameworks could help connect the case studies with grand theories, by helping to create a theoretically sound and practically applicable research architecture of ICT4D. This book demonstrates how creative use of various data analysis methods (e.g., data mining [DM], data envelopment analysis [DEA], and structural equation modeling [SEM]) and conceptual frameworks (e.g., neoclassical growth accounting, chaos and complexity theories) may be utilized for inductive and deductive purposes to develop and to test, in step-by-step fashion, theoretically sound frameworks for a large subset of ICT4D research questions. Specifically, this book showcases the utilization of DM, DEA, and SEM for the following purposes: Identification of the relevant context-specific constructs (inductive application) Identification of the relationships between the constructs (inductive application) Development of a framework incorporating the constructs and relationships discovered (inductive application) Testing of the constructed framework (deductive application) The book takes a multi-theoretical perspective to economic development research. It starts with an overview of ICT4D. Next it covers such frameworks and theories as neoclassical growth accounting and the theory of complementarity, complex systems and chaos theories, and the product life cycle (PLC) theory. There are also nontechnical overviews of the DM and data analytic methods that can be used in this research. Also presented is evidence that human capital and investment capital are complementary and are reliable sources of economic growth. The book concludes with methodological frameworks to guide investment decisions and the formulation of strategic policy.

Based on expert practitioners' contributions from across the globe including Brazil, Jamaica, Malaysia, Pakistan, Thailand, the United Kingdom, and the United States, Strategic Project Management: Contemporary Issues and Strategies for Developing Economies offers modern experiences, best practices, and tools for individuals and teams working in projects spanning diverse environments in developing economies. The book answers the questions: what are the issues and challenges experienced in "developing" countries and how can effective project management practices address them? It then presents strategies and sustainable solutions. The book covers the foundations of project management, highlighting particular strategies that may resonate with organizations across the globe, particularly developing economies. It includes dialogue on project success criteria and performance evaluation techniques, stakeholder management, program and portfolio management, managing knowledge in projects and case studies across industries such as ICT, education and law. In addition, the book showcases: Diverse perspectives and experiences in the effective management of projects from the developing economies The importance of project maturity through the adoption of sound strategic project management principles Application of project management standards and practices in specific domains Emerging tools and techniques that can enhance the management of different types of projects Opportunities for future research and collaborations The contributors share ideas, insights, and experiences for all forms of business projects with a core ICT artifact or supported by ICT to deliver the specific artifact, product, service, or result. The chapters discuss the range of issues found in managing different types of projects across many domains and countries and underline the similarities and nuances in managing projects with strategies that resonate in developing economies. The book, in a nutshell, gives you tried-and-true advice from experts that you can put to immediate use.

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization's strategic business objectives.