This book focuses on the issues and challenges posed by COVID-19, proposing ways to deal with the supposed 'new normal' which the pandemic has introduced in the functioning of business, society, and environment. Among the issues discussed are employee well-being and mental health, impact of changes in education sector, marketing, selling and distribution of goods, change in business model for SME, impact on travel and personal grooming sector, consumer preferences, performance impact of intellectual capital, performance of banks-pre merger, and so on. Focus is on presenting strong research results backed by statistical analysis using different tools. There are managerial solutions to the problems being faced by businesses and firms. The presentations would throw great insights on how businesses have coped during pandemic times in a developing economy like India.

Models derived from the Real Business Cycle perspective have recently taken a major place in business cycle research. The papers in this present volume bring three contributions to this research programme: A critical evaluation of the canonical RBC models, new elements of empirical relevance, based on comparative calibration and testing, and new specifications, at the frontier of business cycle research, coping with non walrasian features, contracts and nominal rigidities, unemployment and growth.

This handbook distils the wealth of expertise and knowledge from a large community of researchers and industrial practitioners in Software Product Lines (SPLs) gained through extensive and rigorous theoretical, empirical, and applied research. It is a timely compilation of well-established and cutting-edge approaches that can be leveraged by those facing the prevailing and daunting challenge of re-engineering their systems into SPLs. The selection of chapters provides readers with a wide and diverse perspective that reflects the complementary and varied expertise of the chapter authors. This perspective covers the re-engineering processes, from planning to execution. SPLs are families of systems that share common assets, allowing a disciplined software reuse. The adoption of SPL practices has shown to enable significant technical and economic benefits for the companies that employ them. However, successful SPLs rarely start from scratch, but instead, they usually start from a set of existing systems that must undergo well-defined re-engineering processes to unleash new levels of productivity and competitiveness. Practitioners will benefit from the lessons learned by the community, captured in the array of methodological and technological alternatives presented in the chapters of the handbook, and will gain the confidence for undertaking their own re-engineering challenges. Researchers and educators will find a valuable single-entry point to quickly become familiar with the state-of-the-art on the topic and the open research opportunities; including undergraduate, graduate students, and R&D engineers who want to have a comprehensive understanding of techniques in reverse engineering and re-engineering of variability-rich software systems.

This book includes selected papers submitted to the ICADABAI-2017 conference, offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in the area of analytics. It discusses predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. The papers in the predictive analytics applications section discuss web analytics, email marketing, customer churn prediction, retail analytics and sports analytics. The section on machine learning applications then examines healthcare analytics, insurance analytics and machine analytics using different innovative machine learning techniques. Human resource analytics addresses important issues relating to talent acquisition and employability using analytics, while a paper in the section on operations analytics describe an innovative application in oil and gas industry. The papers in the analytics in finance part discuss the use of analytical tools in banking and commodity markets, and lastly the econometric applications part presents interesting banking and insurance applications.

This authoritative text/reference describes the state of the art in requirements engineering for software systems for distributed computing. A particular focus is placed on integrated solutions, which take into account the requirements of scalability, flexibility, sustainability and operability for distributed environments. Topics and features: discusses the latest developments, tools, technologies and trends in software requirements engineering; reviews the relevant theoretical frameworks, practical approaches and methodologies for service requirements; examines the three key components of the requirements engineering process, namely requirements elicitation, requirements specification, and requirements validation and evaluation; presents detailed contributions from an international selection of highly reputed experts in the field; offers guidance on best practices, and suggests directions for further research in the area.

Managing for IT skills is never easy at the firm level, due to the fact that technologies change constantly and rapidly. The supply and demand of IT skills fluctuate, and firms do not have commonly recognized frameworks to manage IT skills of their workforce. A consistent taxonomy of IT skills is underdeveloped and used infrequently in industry. This book provides the basic vocabulary and managerial framework for managing strategically the IT workforce at the firm level. It also informs managers what tools and services are available to assess the skill levels of their IT workforce and job candidates. Finally, it gives different perspectives on managing IT skills - how individuals, HR managers, educators, and governments approach IT skills management.

The information systems field has contributed greatly to the rise of the information economy and the information society. Yet, after more than a quarter-century since its formation, it still is plagued by doubts about its identity and legitimacy. "Information Systems: The State of the Field" contains the reflections of leading IS scholars on the nature of the discipline, its core identity and the challenges of creating a strong and legitimate academic enterprise centred on information systems. It includes debates, reflections and commentaries from a group of leading information system scholars, and offers an overview of the state of the field at this time. This book is intended for all who are interested in the nature and direction of the information system field as it enters the 21st century.

"The sociologist Zygmund Bauman has defined a discipline which is constantly debating its credentials as a "flawed" discipline. This critique can certainly be applied to the IS discipline. The editors of this book must be congratulated on collecting together the principal writings reflecting the nature of the debate to provide a learned and fascinating account of where the field now stands and perhaps where it is going. It is essential reading for any student of IS."
--Frank Land, Emeritus Professor, Department of Information Systems, London School of Economics

"The struggle for identity, according to Alford North Whitehead entails a dialectic of "becoming." It evolves from coping with continuous change, a conflict of perspectives and always asking: "Who am I?," "Who are we?," "Who are we not?," "What do we inherit from our past?." In this imaginatively edited volume, King and Lyytinenrecount information systems' restless pursuit for identity. Anyone who is affected by the struggles, but more importantly everyone who wants to join it must read this book."
--Richard O. Mason, Carr P. Collins Distinguished Professor, Management Information Sciences, Edwin L. Cox School of Business, Southern Methodist University

Among the many elementary expositions of psychoanalysis, "The Talking Cure" is unique in focusing on the actual analytic experience. Lichtenberg's approach is humanistic, demonstrating empathic understanding of the fears and hopes of the person seeking help. He provides a "feel" for what happens during the analytic voyage of self-discovery.

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. The first half of Volume III contains a detailed study of several classes of fixed income securities, ranging from simple vanilla options to highly exotic cancelable and path-dependent derivatives. The analysis is done in product-specific fashion covering, among other subjects, risk characterization, calibration strategies, and valuation methods. In its second half, Volume III studies the general topic of derivative portfolio risk management, with a particular emphasis on the challenging problem of computing smooth price sensitivities to market input perturbations.

Digitization, the global networking of individuals and organizations, and the transition from an industrial to an information society are key reasons for the importance of digital government. In particular, the enormous influence of the Internet as a global networking and communication system affects the performance of public services. This textbook introduces the concept of digital government as well as digital management and provides helpful insights and strategic advice for the successful implementation and maintenance of digital government systems.

This book continues the discussion of the effects of artificial intelligence in terms of economics and finance. In particular, the book focuses on the effects of the change in the structure of financial markets, institutions and central banks, along with digitalization analyzed based on fintech ecosystems. In addition to finance sectors, other sectors, such as health, logistics, and industry 4.0, all of which are undergoing an artificial intelligence induced rapid transformation, are addressed in this book. Readers will receive an understanding of an integrated approach towards the use of artificial intelligence across various industries and disciplines with a vision to address the strategic issues and priorities in the dynamic business environment in order to facilitate decision-making processes. Economists, board members of central banks, bankers, financial analysts, regulatory authorities, accounting and finance professionals, chief executive officers, chief audit officers and chief financial officers, chief financial officers, as well as business and management academic researchers, will benefit from reading this book.

This book examines the field of parallel database management systems and illustrates the great variety of solutions based on a shared-storage or a shared-nothing architecture. Constantly dropping memory prices and the desire to operate with low-latency responses on large sets of data paved the way for main memory-based parallel database management systems. However, this area is currently dominated by the shared-nothing approach in order to preserve the in-memory performance advantage by processing data locally on each server. The main argument this book makes is that such an unilateral development will cease due to the combination of the following three trends: a) Today's network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory on a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic and provide high-availability. c) A modern storage system such as Stanford's RAM Cloud even keeps all data resident in the main memory. Exploiting these characteristics in the context of a main memory-based parallel database management system is desirable. The book demonstrates that the advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible.

Soft computing techniques are innovative tools that use nature-inspired algorithms to run predictive analysis of industries from business to software measurement. These tools have gained momentum in recent years for their practicality and flexibility. The Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making collects both empirical and applied research in the field of fuzzy set theory, and bridges the gap between the application of soft computational approaches and the organizational decision making process. This publication is a pivotal reference for business professionals, IT specialists, software engineers, and advanced students of business and information technology.

How might one determine if a financial institution is taking risk in a balanced and productive manner? A powerful tool to address this question is economic capital, which is a model-based measure of the amount of equity that an entity must hold to satisfactorily offset its risk-generating activities. This book, with a particular focus on the credit-risk dimension, pragmatically explores real-world economic-capital methodologies and applications. It begins with the thorny practical issues surrounding the construction of an (industrial-strength) credit-risk economic-capital model, defensibly determining its parameters, and ensuring its efficient implementation. It then broadens its gaze to examine various critical applications and extensions of economic capital; these include loan pricing, the computation of loan impairments, and stress testing. Along the way, typically working from first principles, various possible modelling choices and related concepts are examined. The end result is a useful reference for students and practitioners wishing to learn more about a centrally important financial-management device.

In this volume, noted experts in a variety of information, business, and management fields offer a comprehensive overview of the role information plays in global business and its impact on competition and competitiveness. At the core of the collection is a common belief in the essential value of information to the modern business and a recognition that the corporate intelligence function must today cope with changing realities produced by both new technology and the globalization of markets. Taking these as their points of departure, the contributors discuss a broad spectrum of corporate intelligence issues ranging from the uses of artificial intelligence and the structure of the corporate intelligence system to the nature of security threats, financial warfare, and corporate risk assessment.

The chapters are divided into five sections and begin with two essays on the emerging interrelated global world order. George Roukis discusses the corporate intelligence process as it embodies the global view, while Hugh Conway shows how modern technology has changed the corporate intelligence function. Three chapters explore the information applications of new technologies, including the use of the computer to further all aspects of corporate intelligence gathering and the emergence of an information industry to serve the needs of intelligence gatherers. The following section contains chapters that address, in turn, the use of intelligence in strategic decisionmaking, coping with bad news, the process of intelligence gathering, and field-marketing intelligence. Turning to a discussion of outside threats to corporate intelligence data security, the contributors examine computer security in general, defense related computer security, and the terrorist threat to corporations. In the final section, the contributors look at a number of strategic challenges. A particularly interesting chapter examines corporate intelligence in Japan; others look at geography and corporate risk assessment, the Soviet foreign intelligence service, and corporate responses to financial warfare. Competitive intelligence and marketing executives, as well as students in international business programs, will find this volume enlightening and provocative reading.

A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be formulated on graphs and networks. This edition provides many new applications and algorithms while maintaining the classic foundations on which contemporary algorithm

Modern businesses generate huge volumes of accounting data on a daily basis. The recent advancements in information technology have given organizations the ability to capture and store data in an efficient and effective manner. However, there is a widening gap between this data storage and usage of the data. Business intelligence techniques can help an organization obtain and process relevant accounting data quickly and cost efficiently. Such techniques include: query and reporting tools, online analytical processing (OLAP), statistical analysis, text mining, data mining, and visualization. Business Intelligence Techniques is a compilation of chapters written by experts in the various areas. While these chapters stand on their own, taken together they provide a comprehensive overview of how to exploit accounting data in the business environment.

Big Data technologies have the potential to revolutionize the agriculture sector, in particular food safety and quality practices. This book is designed to provide a foundational understanding of various applications of Big Data in Food Safety. Big Data requires the use of sophisticated approaches for cleaning, processing and extracting useful information to improve decision-making. The contributed volume reviews some of these approaches and algorithms in the context of real-world food safety studies. Food safety and quality related data are being generated in large volumes and from a variety of sources such as farms, processors, retailers, government organizations, and other industries. The editors have included examples of how big data can be used in the fields of bacteriology, virology and mycology to improve food safety. Additional chapters detail how the big data sources are aggregated and used in food safety and quality areas such as food spoilage and quality deterioration along the supply chain, food supply chain traceability, as well as policy and regulations. The volume also contains solutions to address standardization, data interoperability, and other data governance and data related technical challenges. Furthermore, this volume discusses how the application of machine-learning has successfully improved the speed and/or accuracy of many processes in the food supply chain, and also discusses some of the inherent challenges. Included in this volume as well is a practical example of the digital transformation that happened in Dubai, with a particular emphasis on how data is enabling better decision-making in food safety. To complete this volume, researchers discuss how although big data is and will continue to be a major disruptor in the area of food safety, it also raises some important questions with regards to issues such as security/privacy, data control and data governance, all of which must be carefully considered by governments and law makers.

This book draws on a neo-institutional theory to characterize service-oriented manufacturing firms in relation to more familiar organizational forms, such as lean and agile. It sheds light on whether being lean is a prerequisite for agile organizations and whether agile organizations are precursors of service-oriented organizations. The book empirically examines the prevalence of such organizations using representative samples of manufacturing firms in an industrialized country. This approach makes it possible to "zoom in" and determine whether the extent of adoption of digital manufacturing innovations, digital services, and service-oriented business models varies with organizations' size, industry, product complexity, lot size, type of design process, and type of manufacturing process. In turn, it shows which digital manufacturing innovations, lean practices, and services contribute to leanness-related performance capabilities like quality and costs; agility-related capabilities like fast delivery, flexibility and innovation; and service-oriented capabilities like high service performance and digitalization. In addition, it explores the question of whether lean, agile, and service-oriented performance capabilities contribute to financial performance separately or jointly.

This book covers sustainable development in smart society's 5.0 using data analytics. The data analytics is the approach of integrating diversified heterogeneous data for predictive analysis to accredit innovation, decision making, business analysis, and strategic decision making. The data science brings together the research in the field of data analytics, online information analytics, and big data analytics to synthesize issues, challenges, and opportunities across smart society 5.0. Accordingly, the book offers an interesting and insightful read for researchers in the areas of decision analytics, cognitive analytics, big data analytics, visual analytics, text analytics, spatial analytics, risk analytics, graph analytics, predictive analytics, and analytics-enabled applications.

Emerging technologies such as the Internet and biotechnology have the potential to create new industries and transform existing ones. Incumbent firms, despite their superior resources, often lose out to smaller rivals in developing emerging technologies. Why do these incumbents have so much difficulty with disruptive technologies? How can they anticipate and overcome their handicaps?

Wharton on Managing Emerging Technologies presents insights, tools, and frameworks from leading busi-ness thinkers based on the research of Wharton's Emerging Technologies Management Research Program. This pioneering industry-academic partnership, established in 1994, is one of the longest and broadest initiatives on the management of emerging technologies. For the first time, this book distills the insights from the program into a single volume for managers, covering a wide range of issues related to the successful management of emerging technologies.

The editors contend that managing emerging technologies represents a "different game," requiring a different set of management skills, frameworks, and strategies than those used by established firms to manage existing technologies. In this book, experts from diverse fields examine key issues such as:
* Common pitfalls and potential solutions for incumbent firms in managing emerging technologies
* Strategies for assessing the potential of new markets and designing technologies to take advantage of market "lumpiness"
* The need for scenario planning and "disciplined imagination" to develop strategies under uncertainty
* The limits of patents in protecting gains from technology, and the use of lead time and other strategies
* The power of innovative financial strategies and the use of real options in making investments
* Using alliances and new organizational forms
* Developing a "customized workplace"

Wharton on Managing Emerging Technologies represents a powerful survival kit for managers "dropped behind the lines" of these new technologies. The authors provide a comprehensive set of tools and insights that will help you understand the new challenges and develop effective strategies to succeed at this different game.

Praise for WHARTON on MANAGING EMERGING TECHNOLOGIES

"New technologies are transforming markets, businesses, and society at an ever-increasing rate. We have a critical need for better road maps for managing our way through this new terrain. This book offers critical insights and useful new models for thinking through these challenges."-Professor Thomas Gerrity, Director of the Wharton e-Commerce Forum

"Wharton on Managing Emerging Technologies covers the emerging technology landscape-from strategy to finance to human resources-in a way that only a group of top scholars from many disciplines could do. Insightful, accessible, and smart ideas that make for 'must reading' for thoughtful executives in today's turbulent economy. The authors prove, once again, the power of research to yield deep insight into tough business problems."-Kathleen M. Eisenhardt, Professor of Strategy and Organization, Stanford University and coauthor, Competing on the Edge: Strategy As Structured Chaos

"Wharton on Managing Emerging Technologies provides those of us who live in the chaotic environment of 'discovery by the minute' many ways of thinking about how to make a new idea successful. The collective knowledge and personal wisdom represented in this book is like having a person with hundreds of years of learning and experience sitting on the business team. Wharton on Managing Emerging Technologies is vital to anyone trying to develop new businesses in today's world."-Terry J. Fadem, Director, Corporate New Business Development, DuPont

Advances in information technology (IT) have influenced how organizations do business. With IT playing such a pivotal role in the operations and success of an organization, it is imperative that it be used strategically. As a repository of cases, Cases on E-Readiness and Information Systems Management in Organizations: Tools for Maximizing Strategic Alignment contains research that readers can use to assess the e-readiness of their own organizations. This book presents principles, tools, and techniques about e-readiness, while also offering in-depth perspectives on applying the e-readiness model for the purpose of aligning IT with organizational strategies.

This book describes a conceptual management system derived from the Business and Technology Relationship Model (BTRM). The BTRM describes the relationship between business and technology and provides simple definitions for service quality, alignment, agility, and governance. It explains our problems with traditional methods, democratizes the management and governance of enterprise technologies, and is suitable for introducing process automation. This book describes in detail how the BTRM, combined with a focus on value creation and value delivery, will enable continuous change, in the context of current, emerging and future technologies. It illustrates the potential for real-time insight and control not previously considered and provides a wide range of information to plan an implementation, understand where AI can be applied, and its importance in the world of self-managing systems. The topic of this book is particularly relevant for business managers, business technology managers and technology service providers.

This book addresses the concept of organizing which is centered around collective learning and on the organization paradigm. It presents a theory of organizational learning based on a model of memory, explaining processes and dynamics through which memory is built and updated.

Rapidly developing Artificial Intelligence (AI) systems hold tremendous potential to change various domains and exert considerable influence on societies and organizations alike. More than merely a technical discipline, AI requires interaction between various professions. Based on the results of fundamental literature and empirical research, this book addresses the management's awareness of the ethical and moral aspects of AI. It seeks to fill a literature gap and offer the management guidance on tackling Trustworthy AI Implementation (TAII) while also considering ethical dependencies within the company. The TAII Framework introduced here pursues a holistic approach to identifying systemic ethical relationships within the company ecosystem and considers corporate values, business models, and common goods aspects like the Sustainable Development Goals and the Universal Declaration of Human Rights. Further, it provides guidance on the implementation of AI ethics in organisations without requiring a deeper background in philosophy and considers the social impacts outside of the software and data engineering setting. Depending on the respective legal context or area of application, the TAII Framework can be adapted and used with a range of regulations and ethical principles. This book can serve as a case study or self-review for c-level managers and students who are interested in this field. It also offers valuable guidelines and perspectives for policymakers looking to pursue an ethical approach to AI.