This open access book presents a number of case studies on digital transformation in Norway, one of the fore-runners in the digital progress index established by the European Commission in 2020. They explore the process of adoption, diffusion and value generation from digital technologies, and how the use of different digital solutions has enabled Norwegian enterprises to digitally transform their operations and business models. The book starts with an introductory chapter summarizing a vast body of literature in order to synthesize what is already known about digital transformation before exploring the Norwegian context in more detail. Then a series of case studies from the private and public sector in Norway is presented. They document a process perspective which describes the sequence of events during and after adoption of digital solutions, as well as the types of business value that were realized. Through these single studies, the process of digital transformation is illustrated, a number of key findings highlighted, and eventually theoretical and practical recommendations based on these cases emphasized. The book closes with a brief overview of some emerging technologies, and comments on how they are likely to change different sectors. Digital transformation has been one of the priority areas for the Norwegian government over the past years and puts Norwegian enterprises upfront in adopting novel technologies and utilizing them for achieving organizational goals. This experience accumulated over the years makes the Norwegian context a particularly interesting one in understanding how private and public organizations make use of new digital solutions, what lessons can be learnt during the process, and what are some of the key success and failure factors. This way the book is written for practitioners who are currently involved in digital transformation projects in their organizations, researchers of information systems and management, as well as master students in degrees of informatics and technology management.

Dynamic complexity results from hidden, un known factors-or more precisely, interactions between factors-that can unexpectedly im pact the perfor mance of systems. When the influences of dynamic complexity are not meas ured and understood, new never-seen-before behaviors can come as unwelcomed surprises, which disrupt the performance of systems. Left alone, processes that were once prized for their effi ciency unexpectedly begin to degrade-costs increase, while volumes and quality decline. Evidence of problems may come too late for effective resolution as technology advance ments induce rapid change and compress the time available to react to that change. The results of dynamic complexity are always negative and unmanaged dynamic complexity can bring business or global systems to the point of sudden chaos. The 2009 H1N1 pandemic, 2008 Credit Crunch and 2011 Fukushima Daiichi nuclear disaster are global examples of the dangers of undiagnosed dynamic complexity. With increasing frequency executive leaders today are discovering that their business and IT system performance levels are not meeting expectations. In most cases these performance deficiencies are caused by dynamic complexity, which lies hidden like a cancer until the symptoms reveal themselves-often when it is too late to avoid negative impacts on business outcomes. This book examines the growing business problem of dynamic complexity and presents a path to a practical solution. To achieve better predictability, organizations must be able to expose new, dangerous patterns of behavior in time to take corrective actions and know which actions will yield the optimal results. The book authors promote new methods of risk management that use data collection, analytics, machine learning and automation processes to help organizations more accurately predict the future and take strategic actions to improve performance outcomes. The presented means of achieving this goal are based upon the authors' practical experiences, backed by scientific principles, and results achieved through consulting engagements with over 350 global organizations.

This book adopts the managerial perspective to the study of smart cities. As such, this book is a necessary addition to the existing body of literature on smart cities. The chapters included in this book prove the case that transformation of cities to smart cities is a function of effective and efficient management practices implemented at diverse levels of smart cities. While advances in information and communication technology (ICT) are crucial, it is the ability to apply ICT consciously and efficiently that drives the transformation of cities to smart cities in a manner conducive to cities' sustainability and resilience. The book covers three sets of interconnected topics: Management and decision-making for urban design and infrastructure development Management and decision-making in context of smart cities development Ways of promoting and ensuring participation, representation and co-creation in smart cities These three groups of topics offer a great opportunity to acquire a clear, direct, and practice-driven knowledge and understanding of how effective management allows ICT-enhanced tools and applications to change smart cities, possibly making them smarter.

This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.

This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.

This book covers important issues related to managing supply chain risks from various perspectives. Supply chains today are vulnerable to disruptions with a significant impact on firms' business and performance. The aim of supply chain risk management is to identify the potential sources of risks and implement appropriate actions in order to mitigate supply chain disruptions. This book presents a set of models, frameworks, strategies, and analyses that are essential for managing supply chain risks. As a comprehensive collection of the latest research and most recent cutting-edge developments on supply chain risk and its management, the book is structured into three main parts: 1) Supply Chain Risk Management; 2) Supply Chain Vulnerability and Disruptions Management; and 3) Toward a Resilient Supply Chain. Leading academic researchers as well as practitioners have contributed chapters, combining theoretical findings and research results with a practical and contemporary view on how companies can manage the supply chain risks and disruptions, as well as how to create a resilient supply chain. This book can serve as an essential source for students and scholars who are interested in pursuing research or teaching courses in the rapidly growing area of supply chain risk management. It can also provide an interesting and informative read for managers and practitioners who need to deepen their knowledge of effective supply chain risk management.

Benchmarking is considered a must for modern management. This book presents an approach to benchmarking that has a solid mathematical basis and is easy to understand and apply. The book focuses on three main topics. It shows how to formalize the representation of benchmarking objects. Furthermore, it presents different methods from decision making and voting and their application to benchmarking. Finally, it discusses suitable features for different benchmarking objects. The objects considered are taken from IT management, but can be easily transferred to other business areas, which makes the book interesting for all practitioners in the management field.

This textbook provides guidance to both students and practitioners of enterprise architecture (EA) on how to develop and maintain enterprise models. Rather than providing yet another list of EA notations and frameworks from A to Z, it focuses on methods to perform such tasks. The problem of EA maintenance, named Enterprise Cartography, is an important aspect addressed in this book because EA is a never ending challenge that increases as the organization transformations pace also increases. The long time perspective also entails the evolution of architectural frameworks and notations, something that does not occur when developing new models. Thus, a catalogue of patterns, principles and methods is presented to develop and maintain EA models and views. After a general introduction to the book in chapter 1, chapter 2 presents basic concepts for EA modeling. Chapter 3 further details the set of EA concepts needed to present the patterns, and principles, which are subsequently introduced in chapter 4. Next, chapter 5 describes enterprise cartography concepts and principles. The remaining book then turns to techniques and methodologies. In chapter 6 an EA development method is summarized. In chapter 7 an enterprise strategy design approach is proposed, while in chapter 8 a business process design methodology is described. Chapters 9 and 10 focus on information architecture and information systems architecture design approaches, including information systems architecture planning and application portfolio management. Eventually, chapter 11 describes a method for enterprise cartography (EC) design. Last not least, several case studies on EA and EC are proposed in the last chapter.

This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States. This package includes MyLab. For courses in Business Statistics. A classic text for accuracy and statistical precision Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions. In this regard, it emphasizes an understanding of the assumptions that are necessary for professional analysis. In particular, it has greatly expanded the number of applications that utilize data from applied policy and research settings. The Ninth Edition of this book has been revised and updated to provide students with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics. This revision recognizes the globalization of statistical study and in particular the global market for this book. Reach every student by pairing this text with MyLab Statistics MyLab (TM) is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student. MyLab Statistics should only be purchased when required by an instructor. Please be sure you have the correct ISBN and Course ID. Instructors, contact your Pearson representative for more information.

Statistics for Business and Economics introduces statistics in the context of contemporary business. Emphasising statistical literacy in thinking, the text applies its concepts with real data and uses technology to develop a deeper conceptual understanding. Examples, activities, and case studies foster active learning in the classroom while emphasising intuitive concepts of probability and teaching students to make informed business decisions. The 14th Edition continues to highlight the importance of ethical behaviour in collecting, interpreting, and reporting on data, while also providing a wealth of new and updated exercises and case studies.

This book presents a framework for developing as well as a comprehensive collection of state-of-the-art process querying methods. Process querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organize and extract process-related information for subsequent systematic use. The book comprises sixteen contributed chapters distributed over four parts and two auxiliary chapters. The auxiliary chapters by the editor provide an introduction to the area of process querying and a summary of the presented methods, techniques, and applications for process querying. The introductory chapter also examines a process querying framework. The contributed chapters present various process querying methods, including discussions on how they instantiate the framework components, thus supporting the comparison of the methods. The four parts are due to the distinctive features of the methods they include. The first three are devoted to querying event logs generated by IT-systems that support business processes at organizations, querying process designs captured in process models, and methods that address querying both event logs and process models. The methods in these three parts usually define a language for specifying process queries. The fourth part discusses methods that operate over inputs other than event logs and process models, e.g., streams of process events, or do not develop dedicated languages for specifying queries, e.g., methods for assessing process model similarity. This book is mainly intended for researchers. All the chapters in this book are contributed by active researchers in the research disciplines of business process management, process mining, and process querying. They describe state-of-the-art methods for process querying, discuss use cases of process querying, and suggest directions for future work for advancing the field. Yet, also other groups like business or data scientists and other professionals, lecturers, graduate students, and tool vendors will find relevant information for their distinctive needs. Chapter "Celonis PQL: A Query Language for Process Mining" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.

This book provides an overview of the topics of data, sovereignty, and governance with respect to data and online activities through a legal lens and from a cybersecurity perspective. This first chapter explores the concepts of data, ownerships, and privacy with respect to digital media and content, before defining the intersection of sovereignty in law with application to data and digital media content. The authors delve into the issue of digital governance, as well as theories and systems of governance on a state level, national level, and corporate/organizational level. Chapter three jumps into the complex area of jurisdictional conflict of laws and the related issues regarding digital activities in international law, both public and private. Additionally, the book discusses the many technical complexities which underlay the evolution and creation of new law and governance strategies and structures. This includes socio-political, legal, and industrial technical complexities which can apply in these areas. The fifth chapter is a comparative examination of the legal strategies currently being explored by a variety of nations. The book concludes with a discussion about emerging topics which either influence, or are influenced by, data sovereignty and digital governance, such as indigenous data sovereignty, digital human rights and self-determination, artificial intelligence, and global digital social responsibility. Cumulatively, this book provides the full spectrum of information, from foundational principles underlining the described topics, through to the larger, more complex, evolving issues which we can foresee ahead of us.

Foundations of Social Entrepreneurship presents definitions of social entrepreneurship, explains its benefits and challenges, describes the components of an ecosystem of support, and presents practical tools to approach social entrepreneurial projects. It is designed to be easily approachable by anyone without prior in-depth knowledge of the subject. The book is divided into two parts; the first provides readers with theoretical foundations to understand the phenomenon of social entrepreneurship, its different interpretations, the context in which it developed, and its socio-economic function. The second part of the book covers what it takes to create and manage a social entrepreneurial initiative. Pedagogical features are incorporated throughout to aid learning. They include summary tables, international case studies of social entrepreneurs from both developed and emerging economies, as well as suggested exercises and examples of how the tools presented are used in practice. Truly global in its scope, with a strong emphasis on combining theory with practice, this text should be core reading for advanced undergraduate and postgraduate students studying Social Entrepreneurship, Enterprise, and Responsible Business. Online resources include links to resources, chapter-by-chapter PowerPoint slides and instructor's manual.

This write-in workbook is an invaluable resource to help students improve their Maths and English skills and help prepare for Level 1 and Level 2 Functional Skills exams. The real-life questions are all written with a business administration context to help students find essential Maths and English theory understandable, engaging and achievable. Written by Carole Vella, lecturer with a wealth of experience in the Retail and Business Administration industry, this workbook is an effective resource to support Maths and English learning in the classroom, at work and for personal study at home.

People have a hard time communicating, and also have a hard time finding business knowledge in the environment. With the sophistication of search technologies like Google, business people expect to be able to get their questions answered about the business just like you can do an internet search. The truth is, knowledge management is primitive today, and it is due to the fact that we have poor business metadata management.
This book is about all the groundwork necessary for IT to really support the business properly. By providing not just data, but the context behind the data. For the IT professional, it will be tactically practical--very "how to" and a detailed approach to implementing best practices supporting knowledge management. And for the the IT or other manager who needs a guide for creating and justifying projects, it will help provide a strategic map.

* First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management.
* Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projects.
* Very practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the way.
* Includes sample unstructured metadata for use in self-testing and developing skills.

While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following: * Data governance perceptions and challenges * Key considerations when implementing data governance to achieve and sustain success* Strategy and data governance* Different data governance maturity frameworks* Data governance - people and process elements* Data governance metrics This book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

This book offers state-of-the-art descriptions of intelligent service innovations in industry, supported by novel scientific approaches. It gathers findings presented at the 3rd Intelligent Services Summit, which took place in Zurich in September 2020, and chiefly focused on the design and application of Digital Twin as an enabler for business development in the field of smart services. Divided into three parts, the book addresses the challenges involved in the successful development and implementation of smart services for industry and science, ranging from data management to product design and lifecycle management. The four main aspects covered are industrial challenges, value system design (how to integrate resources into service ecosystems to create value), value creation through value proposition (how to create value for ecosystem actors), and value capture (how to create value for ecosystem businesses). Given its scope, the book offers an essential guide for practitioners and advanced students alike.

This book addresses many of the gaps in how industry and academia are currently tackling problems associated with big data. It introduces novel concepts, describes the end-to-end process, and connects the various pieces of the puzzle to offer a holistic view. In addition, it explains important concepts for a wide audience, using accessible language, diagrams, examples and analogies to do so. The book is intended for readers working in industry who want to expand their knowledge or pursue a related degree, and employs an industry-centered perspective.

The three volumes of Interest Rate Modeling present a comprehensive and up-to-date treatment of techniques and models used in the pricing and risk management of fixed income securities. Written by two leading practitioners and seasoned industry veterans, this unique series combines finance theory, numerical methods, and approximation techniques to provide the reader with an integrated approach to the process of designing and implementing industrial-strength models for fixed income security valuation and hedging. Aiming to bridge the gap between advanced theoretical models and real-life trading applications, the pragmatic, yet rigorous, approach taken in this book will appeal to students, academics, and professionals working in quantitative finance. Volume I provides the theoretical and computational foundations for the series, emphasizing the construction of efficient grid- and simulation-based methods for contingent claims pricing. The second part of Volume I is dedicated to local-stochastic volatility modeling and to the construction of vanilla models for individual swap and Libor rates. Although the focus is eventually turned toward fixed income securities, much of the material in this volume applies to generic financial markets and will be of interest to anybody working in the general area of asset pricing.

Software systems that used to be relatively autonomous entities such as e.g. accounting systems, order-entry systems etc. are now interlinked in large networks comprising extensive information infrastructures. What earlier used to be stand-alone proprietary systems are now for the most part replaced by more or less standardized interdependent systems that form large networks of production and use. Organizations have to make decisions about what office suite to purchase? The easiest option is to continuously upgrade the existing office suite to the latest version, but the battle between WordPerfect and Microsoft Word demonstrated that the choice is not obvious. What instant messenger network to join for global communication? Preferably the one most colleagues and friends use; AOL Instant Messenger, Microsoft Messenger, and ICQ represent three satisfactory, but disjunctive alternatives. Similarly organizations abandon their portfolio of homegrown IT systems and replace them with a single Enterprise Resource Planning (ERP) system. Several ERP alternatives exist on the market, but which is the right one for you? The argumentation and rationale behind these considerations are obviously related to the technological and social networks we are embedded in, but it is not always easy to specify how.
Networked Information Technologies: Diffusion and Adoption offers contributions from academics and practitioners who study networked information systems from a diffusion and adoption point of view. Themes related to the conceptualisation of diffusion and adoption of networked information systems are discussed along with studies of the diffusion of networked information systems in public sector institutions and private businesses.
This volume contains the edited proceedings of the IFIP Conference on The Diffusion and Adoption of Networked Information Technologies, which was sponsored by the International Federation for Information Processing (IFIP) Working Group 8.6 and held in Copenhagen, Denmark in October 2003.

O'Brien's Introduction to Information Systems 16e reflects the contemporary use of enterprise-wide business systems. New real-world case studies continue to correspond with this industry reality. The text's focus is on teaching the future manager the potential effect on business of the most current IT technologies such as the Internet, Intranets, and Extranets for enterprise collaboration, and how IT contributes to competitive advantage, reengineering business processes, problem solving, and decision-making. The benchmark text for the syllabus organized by technology (a week on databases, a week on networks, a week on systems development, etc.) taught from a managerial perspective. O'Brien defines technology and then explains how companies use the technology to improve performance. Real world cases finalize the explanation.

Professional data management is the foundation for the successful digital transformation of traditional companies. Unfortunately, many companies fail to implement data governance because they do not fully understand the complexity of the challenge (organizational structure, employee empowerment, change management, etc.) and therefore do not include all aspects in the planning and implementation of their data governance. This book explains the driving role that a responsive data organization can play in a company's digital transformation. Using proven process models, the book takes readers from the basics, through planning and implementation, to regular operations and measuring the success of data governance. All the important decision points are highlighted, and the advantages and disadvantages are discussed in order to identify digitization potential, implement it in the company, and develop customized data governance. The book will serve as a useful guide for interested newcomers as well as for experienced managers.

Sustainability requires companies to develop in an economically, environmentally and socially sustainable manner. Corporate sustainable development in turn requires movement towards cleaner production. In order to recognize the potential from cleaner production reduced costs and fewer environmental impacts through the reduced use of materials environmental management accounting (EMA) is a necessary information management tool.

Environmental Management Accounting for Cleaner Production reveals a set of tools for companies to collect, evaluate and interpret the information they need to estimate their potential to use cleaner production to realize cost savings and to make the best decisions about the available cleaner production options. EMA is therefore the key for driving environmental progress, cost savings, increased competitiveness and corporate sustainability through the means of cleaner production."