This edited three volume edition brings together significant papers previously published in the Journal of information Technology (JIT) over its 30 year publication history. The three volumes of Enacting Research Methods in Information Systems celebrate the methodological pluralism used to advance our understanding of information technology's role in the world today. In addition to quantitative methods from the positivist tradition, JIT also values methodological articles from critical research perspectives, interpretive traditions, historical perspectives, grounded theory, and action research and design science approaches. Volume 1 covers Critical Research, Grounded Theory, and Historical Approaches. Volume 2 deals with Interpretive Approaches and also explores Action Research. Volume 3 focuses on Design Science Approaches and discusses Alternative Approaches including Semiotics Research, Complexity Theory and Gender in IS Research. The Journal of Information Technology (JIT) was started in 1986 by Professors Frank Land and Igor Aleksander with the aim of bringing technology and management together and bridging the 'great divide' between the two disciplines. The Journal was created with the vision of making the impact of complex interactions and developments in technology more accessible to a wider audience. Retaining this initial focus, the JIT has gone on to extend into new and innovative areas of research such as the launch of JITTC in 2010. A high impact journal, JIT shall continue to publish leading trends based on significant research in the field.

This edited three volume edition brings together significant papers previously published in the Journal of information Technology (JIT) over its 30 year publication history. The three volumes of Enacting Research Methods in Information Systems celebrate the methodological pluralism used to advance our understanding of information technology's role in the world today. In addition to quantitative methods from the positivist tradition, JIT also values methodological articles from critical research perspectives, interpretive traditions, historical perspectives, grounded theory, and action research and design science approaches. Volume 1 covers Critical Research, Grounded Theory, and Historical Approaches. Volume 2 deals with Interpretive Approaches and also explores Action Research. Volume 3 focuses on Design Science Approaches and discusses Alternative Approaches including Semiotics Research, Complexity Theory and Gender in IS Research. The Journal of Information Technology (JIT) was started in 1986 by Professors Frank Land and Igor Aleksander with the aim of bringing technology and management together and bridging the 'great divide' between the two disciplines. The Journal was created with the vision of making the impact of complex interactions and developments in technology more accessible to a wider audience. Retaining this initial focus, the JIT has gone on to extend into new and innovative areas of research such as the launch of JITTC in 2010. A high impact journal, JIT shall continue to publish leading trends based on significant research in the field.

The goal of this guide and manual is to provide a practical and brief overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST) and to illustrate the methodologies and applications using R open source language and several data examples. Implementation relies on the R packages catR and mstR that have been already or are being developed by the first author (with the team) and that include some of the newest research algorithms on the topic. The book covers many topics along with the R-code: the basics of R, theoretical overview of CAT and MST, CAT designs, CAT assembly methodologies, CAT simulations, catR package, CAT applications, MST designs, IRT-based MST methodologies, tree-based MST methodologies, mstR package, and MST applications. CAT has been used in many large-scale assessments over recent decades, and MST has become very popular in recent years. R open source language also has become one of the most useful tools for applications in almost all fields, including business and education. Though very useful and popular, R is a difficult language to learn, with a steep learning curve. Given the obvious need for but with the complex implementation of CAT and MST, it is very difficult for users to simulate or implement CAT and MST. Until this manual, there has been no book for users to design and use CAT and MST easily and without expense; i.e., by using the free R software. All examples and illustrations are generated using predefined scripts in R language, available for free download from the book's website.

This book is published under a CC BY-NC 4.0 license. The editors present essential methods and tools to support a holistic approach to the challenge of system upgrades and innovation in the context of high-value products and services. The approach presented here is based on three main pillars: an adaptation mechanism based on a broad understanding of system dependencies; efficient use of system knowledge through involvement of actors throughout the process; and technological solutions to enable efficient actor communication and information handling. The book provides readers with a better understanding of the factors that influence decisions, and put forward solutions to facilitate the rapid adaptation to changes in the business environment and customer needs through intelligent upgrade interventions. Further, it examines a number of sample cases from various contexts including car manufacturing, utilities, shipping and the furniture industry. The book offers a valuable resource for both academics and practitioners interested in the upgrading of capital-intensive products and services. "The work performed in the project "Use-It-Wisely (UiW)" significantly contributes towards a collaborative way of working. Moreover, it offers comprehensive system modelling to identify business opportunities and develop technical solutions within industrial value networks. The developed UiW-framework fills a void and offers a great opportunity. The naval construction sector of small passenger vessels, for instance, is one industry that can benefit." Nikitas Nikitakos, Professor at University of the Aegean, Department of Shipping, Trade, and Transport, Greece. "Long-life assets are crucial for both the future competiveness and sustainability of society. Make wrong choices now and you are locked into a wrong system for a long time. Make the right choices now and society can prosper. This book gives important information about how manufacturers can make right choices." Arnold Tukker, Scientific director, Institute of Environmental Sciences (CML), Leiden University, and senior scientist, TNO.

This book examines trends and challenges in research on IT governance in public organizations, reporting innovative research and new insights in the theories, models and practices within the area. As we noticed, IT governance plays an important role in generating value from organization's IT investments. However there are different challenges for researchers in studying IT governance in public organizations due to the differences between political, administrative, and practices in these organizations. The first section of the book looks at Management issues, including an introduction to IT governance in public organizations; a systematic review of IT alignment research in public organizations; the role of middle managers in aligning strategy and IT in public service organizations; and an analysis of alignment and governance with regard to IT-related policy decisions. The second section examines Modelling, including a consideration of the challenges faced by public administration; a discussion of a framework for IT governance implementation suitable to improve alignment and communication between stakeholders of IT services; the design and implementation of IT architecture; and the adoption of enterprise architecture in public organizations. Finally, section three presents Case Studies, including IT governance in the context of e-government strategy implementation in the Caribbean; the relationship of IT organizational structure and IT governance performance in the IT department of a public research and education organization in a developing country; the relationship between organizational ambidexterity and IT governance through a study of the Swedish Tax Authorities; and the role of institutional logics in IT project activities and interactions in a large Swedish hospital.

This book analyses quantitative open source software (OSS) reliability assessment and its applications, focusing on three major topic areas: the Fundamentals of OSS Quality/Reliability Measurement and Assessment; the Practical Applications of OSS Reliability Modelling; and Recent Developments in OSS Reliability Modelling. Offering an ideal reference guide for graduate students and researchers in reliability for open source software (OSS) and modelling, the book introduces several methods of reliability assessment for OSS including component-oriented reliability analysis based on analytic hierarchy process (AHP), analytic network process (ANP), and non-homogeneous Poisson process (NHPP) models, the stochastic differential equation models and hazard rate models. These measurement and management technologies are essential to producing and maintaining quality/reliable systems using OSS.

This book reports on the latest advances and applications of chaotic systems. It consists of 25 contributed chapters by experts who are specialized in the various topics addressed in this book. The chapters cover a broad range of topics of chaotic systems such as chaos, hyperchaos, jerk systems, hyperjerk systems, conservative and dissipative systems, circulant chaotic systems, multi-scroll chaotic systems, finance chaotic system, highly chaotic systems, chaos control, chaos synchronization, circuit realization and applications of chaos theory in secure communications, mobile robot, memristors, cellular neural networks, etc. Special importance was given to chapters offering practical solutions, modeling and novel control methods for the recent research problems in chaos theory. This book will serve as a reference book for graduate students and researchers with a basic knowledge of chaos theory and control systems. The resulting design procedures on the chaotic systems are emphasized using MATLAB software.

This book identifies and discusses the main challenges facing digital business innovation and the emerging trends and practices that will define its future. The book is divided into three sections covering trends in digital systems, digital management, and digital innovation. The opening chapters consider the issues associated with machine intelligence, wearable technology, digital currencies, and distributed ledgers as their relevance for business grows. Furthermore, the strategic role of data visualization and trends in digital security are extensively discussed. The subsequent section on digital management focuses on the impact of neuroscience on the management of information systems, the role of IT ambidexterity in managing digital transformation, and the way in which IT alignment is being reconfigured by digital business. Finally, examples of digital innovation in practice at the global level are presented and reviewed. The book will appeal to both practitioners and academics. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox that enables easy understanding and assists in exploiting business opportunities involving digital business innovation.

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language.The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.

This book investigates organizational learning from a variety of information processing perspectives. Continuous change and complexity in regulatory, social and economic environments are increasingly forcing organizations and their employees to acquire the necessary job-specific knowledge at the right time and in the right format. Though many regulatory documents are now available in digital form, their complexity and diversity make identifying the relevant elements for a particular context a challenging task. In such scenarios, business processes tend to be important sources of knowledge, containing rich but in many cases embedded, hidden knowledge. This book discusses the possible connection between business process models and corporate knowledge assets; knowledge extraction approaches based on organizational processes; developing and maintaining corporate knowledge bases; and semantic business process management and its relation to organizational learning approaches. The individual chapters reveal the different elements of a knowledge management solution designed to extract, organize and preserve the knowledge embedded in business processes so as to: enrich organizational knowledge bases in a systematic and controlled way, support employees in acquiring job role-specific knowledge, promote organizational learning, and steer human capital investment. All of these topics are analyzed on the basis of real-world cases from the domains of insurance, food safety, innovation, and funding.

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra. The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the da ta before release. This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences.

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models. This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.

This book explores models and concepts of trust in a digitized world. Trust is a core concept that comes into play in multiple social and economic relations of our modern life. The book provides insights into the current state of research while presenting the viewpoints of a variety of disciplines such as communication studies, information systems, educational and organizational psychology, sports psychology and economics. Focusing on an investigation of how the Internet is changing the relationship between trust and communication, and the impact this change has on trust research, this volume facilitates a greater understanding of these topics, thus enabling their employment in social relations.

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.

This book presents recent research in the recognition of vulnerabilities of national systems and assets which gained special attention for the Critical Infrastructures in the last two decades. The book concentrates on R&D activities in the relation of Critical Infrastructures focusing on enhancing the performance of services as well as the level of security. The objectives of the book are based on a project entitled "Critical Infrastructure Protection Researches" (TAMOP-4.2.1.B-11/2/KMR-2011-0001) which concentrated on innovative UAV solutions, robotics, cybersecurity, surface engineering, and mechatornics and technologies providing safe operations of essential assets. This report is summarizing the methodologies and efforts taken to fulfill the goals defined. The project has been performed by the consortium of the Obuda University and the National University of Public Service.

This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.

This book presents new concepts as well as practical applications and experiences in the field of information technology for environmental engineering. The book has three main focus areas: firstly, it shows how information technologies can be employed to support natural resource management and conservation, environmental engineering, scientific simulation and integrated assessment studies. Secondly, it demonstrates the application of computing in the everyday practices of environmental engineers, natural scientists, economists and social scientists. And thirdly, it demonstrates how the complexity of natural phenomena can be approached using interdisciplinary methods, where computer science offers the infrastructure needed for environmental data collection and management, scientific simulations, decision support documentation and reporting.The book collects selected papers presented at the 7th International Symposium on Environmental Engineering, held in Port Elizabeth, South Africa in July 2015. It discusses recent success stories in eco-informatics, promising ideas and new challenges from the interdisciplinary viewpoints of computer scientists, environmental engineers, economists and social scientists, demonstrating new paradigms for problem-solving and decision-making.

This book presents the latest findings and ongoing research in the field of green information systems and green information and communication technology (ICT). It provides insights into a whole range of cross-cutting topics in ICT and environmental sciences as well as showcases how information and communication technologies allow environmental and energy efficiency issues to be handled effectively. The papers presented in this book are a selection of extended and improved contributions to the 28th International Conference on Informatics for Environmental Protection dedicated to ICT for energy efficiency. This book is essential and particularly worth reading for those who already gained basic knowledge and want to deepen and extend their expertise in the subjects mentioned above.

This book is a valuable read for a diverse group of researchers and practitioners who analyze assessment data and construct test instruments. It focuses on the use of classical test theory (CTT) and item response theory (IRT), which are often required in the fields of psychology (e.g. for measuring psychological traits), health (e.g. for measuring the severity of disorders), and education (e.g. for measuring student performance), and makes these analytical tools accessible to a broader audience. Having taught assessment subjects to students from diverse backgrounds for a number of years, the three authors have a wealth of experience in presenting educational measurement topics, in-depth concepts and applications in an accessible format. As such, the book addresses the needs of readers who use CTT and IRT in their work but do not necessarily have an extensive mathematical background. The book also sheds light on common misconceptions in applying measurement models, and presents an integrated approach to different measurement methods, such as contrasting CTT with IRT and multidimensional IRT models with unidimensional IRT models. Wherever possible, comparisons between models are explicitly made. In addition, the book discusses concepts for test equating and differential item functioning, as well as Bayesian IRT models and plausible values using simple examples. This book can serve as a textbook for introductory courses on educational measurement, as supplementary reading for advanced courses, or as a valuable reference guide for researchers interested in analyzing student assessment data.

This new Edition of Electronic Commerce is a complete update of the leading graduate level/advanced undergraduate level textbook on the subject. Electronic commerce (EC) describes the manner in which transactions take place over electronic networks, mostly the Internet. It is the process of electronically buying and selling goods, services, and information. Certain EC applications, such as buying and selling stocks and airline tickets online, are reaching maturity, some even exceeding non-Internet trades. However, EC is not just about buying and selling; it also is about electronically communicating, collaborating, and discovering information. It is about e-learning, e-government, social networks, and much more. EC is having an impact on a significant portion of the world, affecting businesses, professions, trade, and of course, people. The most important developments in EC since 2014 are the continuous phenomenal growth of social networks, especially Facebook , LinkedIn and Instagram, and the trend toward conducting EC with mobile devices. Other major developments are the expansion of EC globally, especially in China where you can find the world's largest EC company. Much attention is lately being given to smart commerce and the use of AI-based analytics and big data to enhance the field. Finally, some emerging EC business models are changing industries (e.g., the shared economy models of Uber and Airbnb). The 2018 (9th) edition, brings forth the latest trends in e-commerce, including smart commerce, social commerce, social collaboration, shared economy, innovations, and mobility.

This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.