Knowledge management Books

Book SynopsisFrom cell phones to Web portals, advances in information and communications technology have thrust society into an information age that is far-reaching, fast-moving, increasingly complex, and yet essential to modern life. Now, renowned scholar and author David Luenberger has produced Information Science, a text that distills and explains the most important concepts and insights at the core of this ongoing revolution. The book represents the material used in a widely acclaimed course offered at Stanford University. Drawing concepts from each of the constituent subfields that collectively comprise information science, Luenberger builds his book around the five E''s of information: Entropy, Economics, Encryption, Extraction, and Emission. Each area directly impacts modern information products, services, and technology--everything from word processors to digital cash, database systems to decision making, marketing strategy to spread spectrum communication.Trade ReviewWinner of the 2006 Award for Best Professional/Scholarly Book in Computer & Information Science, Association of American Publishers "This is a fascinating and enjoyable book to read. It is clear throughout the book that David Luenberger is an experienced teacher who has put careful thought into his writing. He wrote and uses this book for a course in the Dept. of Engineering--Economic Systems and Operations Research at Stanford University. The students range from sophomores to graduate students, and the book is very readable for students at all of these levels."--Susan Kelly, The UMAP JournalTable of ContentsPreface xiii Chapter 1: INTRODUCTION 1 1.1 Themes of Analysis 2 1.2 Information Lessons 4 Part I: ENTROPY: The Foundation of Information Chapter 2: INFORMATION DEFINITION 9 2.1 A Measure of Information 10 2.2 The Definition of Entropy 12 2.3 Information Sources 14 2.4 Source Combinations 15 2.5 Bits as a Measure 16 2.6 About Claude E. Shannon 17 2.7 Exercises 18 2.8 Bibliography 19 Chapter 3: CODES 21 3.1 The Coding Problem 21 3.2 Average Code Length and Entropy 27 3.3 Shannon's First Theorem 30 3.4 Exercises 33 3.5 Bibliography 34 Chapter 4: COMPRESSION 35 4.1 Huffman Coding 35 4.2 Intersymbol Dependency 40 4.3 Lempel-Ziv Coding 44 4.4 Other Forms of Compression 48 4.5 Exercises 52 4.6 Bibliography 53 Chapter 5: CHANNELS 55 5.1 Discrete Channel 56 5.2 Conditional and Joint Entropies 57 5.3 Flipping a Channel 60 5.4 Mutual Information 62 5.5 Capacity* 65 5.6 Shannon's Second Theorem* 66 5.7 Exercises 68 5.8 Bibliography 69 Chapter 6: ERROR-CORRECTING CODES 70 6.1 Simple Code Concepts 71 6.2 Hamming Distance 73 6.3 Hamming Codes 75 6.4 Linear Codes 77 6.5 Low-Density Parity Check Codes 78 6.6 Interleaving 79 6.7 Convolutional Codes 80 6.8 Turbo Codes 82 6.9 Applications 83 6.10 Exercises 85 6.11 Bibliography 86 Summary of Part I 89 Part II: ECONOMICS: Strategies for Value Chapter 7: MARKETS 93 7.1 Demand 94 7.2 Producers 97 7.3 Social Surplus 99 7.4 Competition 100 7.5 Optimality of Marginal Cost Pricing 101 7.6 Linear Demand Curves 102 7.7 Copyright and Monopoly 103 7.8 Other Pricing Methods 107 7.9 Oligopoly 108 7.10 Exercises 111 7.11 Bibliography 113 Chapter 8: PRICING SCHEMES 114 8.1 Discrimination 114 8.2 Versions 116 8.3 Bundling 119 8.4 Sharing 124 8.5 Exercises 127 8.6 Bibliography 128 Chapter 9: VALUE 130 9.1 Conditional Information 131 9.2 Informativity and Generalized Entropy* 133 9.3 Decisions 135 9.4 The Structure of Value 135 9.5 Utility Functions* 139 9.6 Informativity and Decision Making* 140 9.7 Exercises 141 9.8 Bibliography 142 Chapter 10: INTERACTION 143 10.1 Common Knowledge 144 10.2 Agree to Disagree? 146 10.3 Information and Decisions 149 10.4 A Formal Analysis* 150 10.5 Metcalfe's Law 153 10.6 Network Economics* 155 10.7 Exercises 159 10.8 Bibliography 160 Summary of Part II 161 Part III: ENCRYPTION: Security through Mathematics Chapter 11: CIPHERS 165 11.1 Definitions 166 11.2 Example Ciphers 166 11.3 Frequency Analysis 169 11.4 Cryptograms 169 11.5 The Vigenere Cipher 171 11.6 The Playfair Cipher 174 11.7 Homophonic Codes 175 11.8 Jefferson's Wheel Cipher 176 11.9 The Enigma Machine 177 11.10 The One-Time Pad 181 11.11 Exercises 183 11.12 Bibliography 184 Chapter 12: CRYPTOGRAPHY THEORY 186 12.1 Perfect Security 186 12.2 Entropy Relations 188 12.3 Use of a One-Time Pad* 193 12.4 The DES and AES Systems 196 12.5 Exercises 197 12.6 Bibliography 198 Chapter 13: PUBLIC KEY CRYPTOGRAPHY 200 13.1 A Basic Dilemma 200 13.2 One-Way Functions 201 13.3 Discrete Logarithms 202 13.4 Diffie-Hellman Key Exchange 203 13.5 Modular Mathematics 205 13.6 Alternative Puzzle Solution 208 13.7 RSA 209 13.8 Square and Multiply* 211 13.9 Finding Primes* 213 13.10 Performance* 214 13.11 The Future 215 Appendix: The Extended Euclidean Algorithm 216 13.12 Exercises 217 13.13 Bibliography 218 Chapter 14: SECURITY PROTOCOLS 220 14.1 Digital Signatures 220 14.2 Blinded Signatures 223 14.3 Digital Cash 225 14.4 Identification 226 14.5 Zero-Knowledge Proofs 228 14.6 Smart Cards 231 14.7 Exercises 234 14.8 Bibliography 235 Summary of Part III 237 Part IV: EXTRACTION: Information from Data Chapter 15: DATA STRUCTURES 241 15.1 Lists 241 15.2 Trees 244 15.3 Traversal of Trees 247 15.4 Binary Search Trees (BST) 248 15.5 Partially Ordered Trees 252 15.6 Tries* 254 15.7 Basic Sorting Algorithms 255 15.8 Quicksort 257 15.9 Heapsort 260 15.10 Merges 261 15.11 Exercises 262 15.12 Bibliography 263 Chapter 16: DATABASE SYSTEMS 264 16.1 Relational Structure 264 16.2 Keys 267 16.3 Operations 267 16.4 Functional Dependencies 271 16.5 Normalization 271 16.6 Joins and Products* 277 16.7 Database Languages 279 16.8 Exercises 281 16.9 Bibliography 282 Chapter 17: INFORMATION RETRIEVAL 284 17.1 Inverted Files 285 17.2 Strategies for Indexing 287 17.3 Inverted File Compression* 291 17.4 Queries 293 17.5 Ranking Methods 294 17.6 Network Rankings 296 17.7 Exercises 299 17.8 Bibliography 299 Chapter 18: DATA MINING 301 18.1 Overview of Techniques 301 18.2 Market Basket Analysis 303 18.3 Least-Squares Approximation 306 18.4 Classification Trees 310 18.5 Bayesian Methods 314 18.6 Support Vector Machines 319 18.7 Other Methods 323 18.8 Exercises 325 18.9 Bibliography 327 Summary of Part IV 327 Part V: EMISSION: The Mastery of Frequency Chapter 19: FREQUENCY CONCEPTS 331 19.1 The Telegraph 334 19.2 When Dots Became Dashes 335 19.3 Fourier Series 338 19.4 The Fourier Transform 339 19.5 Thomas Edison and the Telegraph 342 19.6 Bell and the Telephone 342 19.7 Lessons in Frequency 345 19.8 Exercises 347 19.9 Bibliography 349 Chapter 20: RADIO WAVES 350 20.1 Why Frequencies? 350 20.2 Resonance 354 20.3 The Birth of Radio 354 20.4 Marconi's Radio 355 20.5 The Spark Bandwidth 357 20.6 The Problems 359 20.7 Continuous Wave Generation 360 20.8 The Triode Vacuum Tube 361 20.9 Modulation Mathematics 363 20.10 Heterodyne Principle 365 20.11 Frequency Modulation 367 20.12 Exercises 369 20.13 Bibliography 372 Chapter 21: SAMPLING AND CAPACITY 373 21.1 Entropy 373 21.2 Capacity of the Gaussian Channel 376 21.3 Sampling Theorem 378 21.4 Generalized Sampling Theorem* 380 21.5 Thermal Noise 383 21.6 Capacity of a Band-Limited Channel 384 21.7 Spread Spectrum 385 21.8 Spreading Technique 387 21.9 Multiple Access Systems 388 21.10 Exercises 391 21.11 Bibliography 392 Chapter 22: NETWORKS 393 22.1 Poisson Processes 394 22.2 Frames 395 22.3 The ALOHA System 396 22.4 Carrier Sensing 398 22.5 Routing Algorithms 399 22.6 The Bellman-Ford Algorithm 400 22.7 Distance Vector Routing 401 22.8 Dijkstra's Algorithm 402 22.9 Other Issues 404 22.10 Exercises 405 22.11 Bibliography 406 Summary of Part V 407 Index 409

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Book SynopsisIn The Patron's Payoff, Jonathan Nelson and Richard Zeckhauser apply the innovative methods of information economics to the study of art. Their findings, written in highly accessible prose, are surprising and important. Building on three economic concepts--signaling, signposting, and stretching--the book develops the first systematic methodology foTrade ReviewOne of Choice's Outstanding Academic Titles for 2009 "[E]nlightening."--Times Literary Supplement "But the basic point of this book--that a careful study of economic and related social needs can help us further understand the genesis of many works of visual culture--is undeniable, and the editors' and authors' cogent presentation of the possibilities inherent in their approach is masterful. Recognizing the motivations of elites expands our understanding of the roles that visual works could play during the period we now identify as the Italian Renaissance. As a reviewer I congratulate Nelson and Zeckhauser, while continuing to lament art history's inability--in the Renaissance at least--to gain access to a broader understanding of the diverse society and complex and subtle culture that supported the production of these works."--David G. Wilkins, CAA Reviews "In The Patron's Payoff, art historian Jonathan K. Nelson and economist Richard J. Zeckhauser have harnessed their separate disciplines into a new analytical key for understanding the linked motivations of patron and artist or architect in conspicuous commissions... No less than the American financier who donates a museum wing on condition it bears his name, or the merchandiser who endows a university institute named for him, the results of Renaissance patronage had to be, first of all, highly visible."--Judith Harris, California Literary Review "Nelson and Zeckhauser offer historians of art and culture a powerful method for appraising the driving force behind works of art commissioned in the Renaissance... The Patron's Payoff offers and innovative and potent tool for probing how works of art functioned in Renaissance social life."--Michelle O'Malley, Renaissance Quarterly "The book's interdisciplinary approach provides a blueprint for others who might test these concepts with patrons and periods necessarily omitted from this study. Common language and readable prose illuminate the theory and animate the relationships between works of art, patrons, artists, and audience. This book will be useful to art historians, cultural historians, economists, and others interested in the significance of the production and consumption of elite culture."--D.N. Dow, Choice "These are all well-written, interesting, well-researched essays, varying in chronological range and in geographical focus."--Bernadine Barnes, EH.net "[T]his volume is a model of how cross-disciplinary interaction can enrich the understanding of practitioners in two participating disciplines."--Neil De Marchi, Journal of Economic Literature "The Patron's Payoff is impressive not only for its innovative interdisciplinary approach and the compilation of an extensive source material ... the reading [is] very entertaining, and clearly shows that even high-profile science can be attractive and intelligible."--Mila Horky, Sehepunkte "[This] book [is] an innovative examination of art, economics, and communication that should be required reading for all who admire Italy's grand masterpieces as well as those who have made the study of Renaissance art and architecture a profession."--Fredrika Jacobs, European Legacy "One hopes that information economists will gain as much as art historians can from this book."--Sally Hickson, Renaissance and ReformationTable of ContentsIllustrations ix Foreword xiii Preface xvii Introduction 1 Part I: THE COMMISSIONING GAME 15 Chapter One: Main Players: Patrons, Artists, and Audiences 17 Chapter Two: Analytic Framework: Benefits, Costs, and Constraints 37 Chapter Three: Theories of Distinction: Magnificence and Signaling 67 Chapter Four: Selecting and Magnifying Information: Signposting and Stretching 85 Part II: THE PATRON'S PAYOFF 111 Chapter Five: Private Chapels in Florence: A Paradise for Signalers by Jonathan K. Nelson and Richard J. Zeckhauser 113 Chapter Six: Commissioning Familial Remembrance in Fourteenth-Century Florence: Signaling Alberti Patronage at the Church of Santa Croce by Thomas J. Loughman 133 Chapter Seven: Signs of Success: Leone Leoni's Signposting in Sixteenth-Century Milan by Kelley Helmstutler Di Dio 149 Chapter Eight: Mantegna's Madonna della Vittoria and the Rewriting of Gonzaga History by Molly Bourne 166 Chapter Nine: Image Is Everything: Visual Art as Self-Advertising (Europe and America) by Larry Silver 185 Contributors 225 Index 227

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Book SynopsisSpanning disciplines from evolutionary theory and cultural anthropology to the history of books, libraries, and computer science, Alex Wright weaves an intriguing narrative about pre-computer age information explosions.Trade ReviewGlut is a readable romp through the history of information processing. Wright argues that advances in information technology have always sparked conflict between written and oral traditions. * New Scientist *Glut defies classification. From Incan woven threads to Wikipedia, Alex Wright shows us that humans have been attempting to fix categories upon the world throughout history, and that organizing information is a fundamental part of what makes us human. Many books tell you how to organize things—this one tells you why we do it. * Harper's Magazine *Glut is a penetrating and highly entertaining meditation on our information age and its historical roots. Alex Wright argues that now is the time to take a hard look at how we have communicated with one another since coming down from the trees, because the way we organize knowledge determines much about how we live. * Los Angeles Times Book Review *Alex Wright delivers a fascinating tour of the many ways that humans have collected, organized, and shared information to show how the information age started long before microchips or movable type. * Publishers Weekly *This stimulating book offers much opportunity to reflect on the nature and long history of information management as a damper to the panic or the elation we may variously feel as we face ever greater scales of information overload. * Nature *

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Book SynopsisTrade Review"In this highly original volume, Jason Davis revisits the classic problem that individual and organizational goals typically diverge. He does so in the context of modern digital media that dramatically facilitate individuals' creation of network ties. When such networking is at cross-purposes to organizational well-being – as often happens when weak ties proliferate – managers badly need the ingenious advice that Davis offers."—Mark Granovetter, Joan Butler Ford Professor, Stanford University; author of Society and Economy: Framework and Principles"Rarely does a book come along that so completely rewrites what we thought we knew. Digital Relationships is just such a book. It's a 'must-read' for academics and executives who want to understand why social networks are so challenging. By probing the deepening chasm between individual and organizational interests, Digital Relationships will re-shape your conception of how social networks work. And more often, don't."—Kathleen Eisenhardt, S. W. Ascherman Professor, Stanford University; co-author of Simple Rules: How to Thrive in a Complex WorldTable of Contents1. Networks are the Problem: Confronting the Social Capital Consensus 2. Too Many Ties: Divergent Interests with the Falling Costs of Digital Networking 3. Ties Too Weak: Insufficient Firm-Specific Social Investments to Mobilize Diversity 4. Entrenched Brokers and Ossified Bridges: Monopolies of Information and Control 5. Scale Too Free: Negative Externalities of Inequality in Social Capital 6. Persistence: Managerial Intervention Transience and the Reemergence of Agency Problems 7. Agentic Function of the Executive: Strategic Social Capital and the Work-From-Home Experiment 8. Network Governance:ReinterpretingOrganizational Design and Boundaries 9. Network Agency over the Life Cycle: Entrepreneurship and Innovation Ecosystems 10. A Research Agenda: More Cases, More Models, More Experiments

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Book SynopsisThis pathbreaking book goes some way to fill this gap in the literature and as such, will prove invaluable to academics, R&D managers, regional policy makers and students with an interest in international business, business economics, regional studies and organization studies.Trade ReviewThis wonderful volume brings together contributions mainly from the innovation literature, whose findings are in a sense quite familiar, but which in this collection are juxtaposed in such a way as to highlight their common institutional underpinnings. This is very much due to the efforts of the editor, whose insightful introduction and editorial vision brings out several interesting and emerging themes from this collection of papers. I think this volume breaks new ground in highlighting the embeddedness of MNE subsidiaries in multiple contexts, and it will be of considerable interest to scholars engaged with institutional analysis. However, I also believe that researchers interested in regional embeddedness, the geography of innovation, and knowledge management will find new angles to their work in this collected volume. --- Sarianna M. Lundan, University of Bremen, GermanyTable of ContentsContents: 1. Introduction: The Debate on Corporate Embeddedness Martin Heidenreich PART I: CHALLENGES AND VARIETIES OF CORPORATE EMBEDDEDNESS 2. Regional Embeddedness of Multinational Companies and their Limits: A Typology Martin Heidenreich and Jannika Mattes 3. The Multi-home-based Corporation: Solving an Insider–Outsider Dilemma Örjan Sölvell 4. MNCs between the Local and the Global: Knowledge Bases, Proximity and Distributed Knowledge Networks Bjørn T. Asheim, Bernd Ebersberger and Sverre J. Herstad 5. MNCs, Clusters and Varieties of Innovative Impulse Philip Cooke PART II: KNOWLEDGE INFRASTRUCTURES AS EMBEDDING DEVICES 6. Simulating the Role of MNCs for Knowledge and Capital Dynamics in Networks of Innovation Petra Ahrweiler, Michel Schilperoord, Nigel Gilbert and Andreas Pyka 7. Technological Capabilities and the Regional Embeddedness of Multinational Enterprises. A Case Study of Germany and the UK Simona Iammarino, Jan-Philipp Kramer, Elisabetta Marinelli and Javier Revilla Diez 8. The Organizational Decomposition of Innovation and Territorial Knowledge Dynamics: Insights from the German Software Industry Simone Strambach and Benjamin Klement 9. The Impact of Regional Institutional Characteristics on the Location of MNCs – a European Perspective Knut Koschatzky and Elisabeth Baier PART III: THE SOCIAL AND POLITICAL CONSTRUCTION OF CORPORATE EMBEDDEDNESS 10. Modes of Regional Embeddedness: Companies in Seven European Regions Compared Dieter Rehfeld 11. The Pôles de Compétitivité: Regional Innovation Clusters with a French Touch Christoph Barmeyer and Katharina Krüth 12. Multinational Companies and the Production of Collective Goods in Central and Eastern Europe Bob Hancké 13. The Role of Multinational Corporations in the National Innovation Systems of the EU New Member States Rajneesh Narula and José Guimón 14. Conclusion: Corporate Embeddedness as a Strategic and Dynamic Process of Skilled Actors Jannika Mattes and Martin Heidenreich Index

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Book SynopsisWith in-depth analysis, this book will appeal to academics and students of STS (Science, Technology and Society), history of science and technology, business history, innovation studies, law, science and technology policy as well as business studies.Trade ReviewArapostathis and Dutfield's rich and well-edited collection offers a cogent challenge to patent-centred views of innovation. Their seventeen authors persuasively show that patenting is just one of a repertoire of tools successfully used to protect innovative work in a broad variety of context-dependent ways. It is essential reading for anyone wanting to learn about past contingencies in the transformation of techno-scientific creativity into wealth.- Graeme Gooday, University of Leeds, UKEssential reading, not just for IP historians and lawyers, but for anyone concerned at the insidious corporate take-over of modern life. It explodes the fallacy that patent systems exist to safeguard inventors' interests: the 20th Century saw inventors become salaried corporate employees, universities adopt a model of academic entrepreneurship, and IPRs create their own, increasingly global, logic. Yet, from Left and Right now arise fundamental ethical questions: isn't intellectual property just legal chicanery; shouldn't knowledge be a universal, public good? --- Christine MacLeod, University of Bristol, UKTable of ContentsContents: Introduction Stathis Arapostathis and Graham Dutfield PART I: INNOVATION CULTURES AND KNOWLEDGE MANAGEMENT 1. ‘Claim the Earth’: Protecting Edison’s Inventions at Home and Abroad Paul Israel 2. Managing Invention: Setting the Boundaries of Ownership Andrea R. Maestrejuan 3. The Photographic Paper that Made Leo Baekeland’s Reputation: Entrepreneurial Incentives for not Patenting Joris Mercelis 4. Software Piracy: Not necessarily Evil – or its Role in Software Development in Greece Theodore Lekkas PART II: INDIVIDUALS, INSTITUTIONS AND THE MANAGEMENT OF INTANGIBLE ASSETS 5. Collective Invention and Patent Law Individualism, 1877–2012 – or, the Curious Persistence of Inventor’s Moral Right Graham Dutfield 6. Something in the Air: The Post Office and Early Wireless, 1882–1899 Elizabeth Bruton 7. Contested Inventors: British Patent Disputes and the Culture of Invention in the Late Nineteenth Century Stathis Arapostathis 8. From Colour TV War to Non-Aggression Pact: Patents as Actants of Techno-political Diplomacy in a European Standardization Process Andreas Fickers PART III: KNOWLEDGE MANAGEMENT AND THE INDUSTRY-STATE-ACADEMIA NEXUS 9. Commerce and Academe: American Universities as Hosts of Entrepreneurial Science, 1880–1920 Susan W. Morris 10. Managing Knowledge in ‘Systematised Plant Breeding’: Mendelism and British Agricultural Science, 1900–1930 Berris Charnley 11. Patenting the Atom: The Controversial Management of State Secrecy and Intellectual Property Rights in Atomic Research Simone Turchetti PART IV: TECHNO-SCIENCES AND GLOBAL IP REGIMES: FROM HISTORY TO PRESENT CONCERNS 12. The International Patent System and the Ethics of Global Justice Henk van den Belt and Michiel Korthals 13. Intellectual Property Rights in the Plant Sciences and Development Goals in Agriculture: An Historical Perspective Niels Louwaars, Bram de Jonge and Peter Munyi 14. Business TRIPS: American Corporations and Patents Head to the Global South, 1950–2010 Eda Kranakis Index

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Book SynopsisArtificial intelligence is shaking up economies around the world as well as society at large and is predicted to be either the best or worst thing to happen to humanity. This book looks at what exactly artificial intelligence is, how it can be classified, how it differentiates from other concepts such as machine learning, big data, blockchain, or the Internet-of-Things, and how it has evolved and might evolve over time. Providing a clear and unbiased picture of artificial intelligence, the book provides critical analyses of the advantages and disadvantages, opportunities and threats of AI progress for business and civilisation. Solutions and possible directions of how humanity might deal with rapid development and evolutions will be given and discussed, and consider regulation, employment, ethics, education and international cooperation. Unlike existing literature, this book provides a comprehensive overview of AI based on detailed analysis and insight. Finally, several real-Table of Contents Commencements: AI, business, and civilization Clarifications: AI, big data, the internet-of-things, and robotics What is (and is not) artificial intelligence? Classifying, exemplifying, and envisioning AI’s past, present, and (future) perspective Concerns: AI’s double-edged sword in education, enterprises, and elections Did higher education dig its own grave by developing AI? Both blessing and curse? AI in the workplace Might AI become a threat to democracy…or is it already? Constructions: Preparing for the AI revolution Regulation and the role of the state AI literacy and ethical conduct International relations and cross-cultural cooperation Cases: From retailing to retelling to retaliation Walmart: The retAIl giant New York’s Metropolitan Museum of ARTificial intelligence China: The ultimate AI trAIning ground Conclusions: Our fate made in machines

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Book SynopsisSociety 5.0 points toward a human-centred approach by the use of modern, advanced technologies and artificial intelligence. This book explores and offers an overview of knowledge management embraced in the current scenario of Society 5.0, shedding light on its importance in a society that is increasingly digital and interconnected. The book enhances current managerial and economic research by offering the âœhumanâ side of knowledge management (KM) intertwined with the use of artificial intelligences (AIs). Each chapter explores KM from different perspectives, including entrepreneurship, innovation, marketing, and strategy, in a theoretical and practical way. They include insights from both practitioners and scholars, enriched by practical tools that can be used during laboratories, workshops and tutorials. The book presents evidence on how to manage KM and develop new knowledge in different subjects, with the aim of overcoming conventional KM strategy and show how business anTable of ContentsChapater 1 – Knowledge Management and AI for CreativityChapater 2 - Knowledge Management and and AI for InnovationChapater 3 - Knowledge Management and and AI for MarketingChapater 4 - Knowledge Management and and AI for StrategyConclusion

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Book SynopsisSustainable Self-Governance in Businesses and Society offers a sound introduction to Stafford Beer's Viable System Model (VSM) and clarifies its relevance to support organisational sustainability and self-governance. While the VSM has been known since the early 1980s, it hasn't been always easy to understand and to apply.  It explains the self-transformation methodology to analyse the way organisations manage (or not) their complexity and govern themselves. The work is supported by multiple examples of application in organisations of all scales from small to multi-national corporations and from organised social networks to communities and national organisations. It clarifies the relevance of Beer's theory to support systemic learning and change in organisations, and to coach them to self-organise and self-govern. Readers interested in further understanding insights from complex systems and cybernetics theories for designing and transforming organisationsTrade Review"Lots of people have been waiting for this book. Angela Espinosa is a noted authority on organisational cybernetics and worked closely with Stafford Beer. Her work deepens our understanding of the ‘viable system model’ and offers significant insight into how best to use it in practice."- Professor Michael C. Jackson, OBE"It is a pleasure to recommend this book that brings multiple applications of the Viable Systems Model (sometimes combined with other systems approaches) to practical problems in society. Too often, new methodologies remain within academia and their potential goes unrealized but this book gives numerous examples of work that combines an empathetic approach to people trying to make their institutions work while maintaining the rigor of the science behind them."- Dr. Allena Leonard, Cybernetician, Director Team Syntegrity International, President Metaphorum. Former President of the American Society for Cybernetics and the International Society for the Systems Sciences"The functioning of our society depends strongly on the functioning of its organizations. Angela Espinosa's book, based on great application experience, shows in a practical way how to apply the Viable System Model so that we can finally make organizations work, however complex and dynamic their environment. A must read for all leaders dealing with organizational issues."- Dr. Martin Pfiffner, Board of Trustees Fondation Oroborus'Lots of people have been waiting for this book. Angela Espinosa is a noted authority on organisational cybernetics and worked closely with Stafford Beer. Her work deepens our understanding of the "viable system model" and offers significant insight into how best to use it in practice.'- Professor Michael C. Jackson, OBE'It is a pleasure to recommend this book that brings multiple applications of the Viable System Model (sometimes combined with other systems approaches) to practical problems in society. Too often, new methodologies remain within academia and their potential goes unrealized but this book gives numerous examples of work that combines an empathetic approach to people trying to make their institutions work while maintaining the rigor of the science behind them.'- Dr. Allena Leonard, Cybernetician, Director Team Syntegrity International, President Metaphorum. Former President of the American Society for Cybernetics and the International Society for the Systems Sciences'The functioning of our society depends strongly on the functioning of its organizations. Angela Espinosa's book, based on great application experience, shows in a practical way how to apply the Viable System Model so that we can finally make organizations work, however complex and dynamic their environment. A must read for all leaders dealing with organizational issues.'- Dr. Martin Pfiffner, Board of Trustees Fondation OroborusTable of ContentsList of FiguresList of TablesPrefaceAcknowledgmentsList of AbbreviationsList of ContributorsIntroduction1. Towards Self-Governed Businesses and SocietiesSystems, Complexity, and Cybernetics SciencesOrganisational CyberneticsComplex Systems SciencesComplexification in the 21st CenturyUnderstanding Complexity (and Variety)What is Complexity and Variety?Organisational Elements (O, M, E)Ashby's Law of Requisite VarietyViability vs Complexity ManagementManaging Variety and LearningOrganisational Learning and DevelopmentManaging Complexity in OrganisationsWhy We Need to Evolve Towards Sustainable Self-Governed OrganisationsSummary2. Organisational Viability (and Sustainability)The Need for More Resilient Organisations and SocietiesWhat is a Viable and Sustainable (v&s) Organisation?The Physiological InspirationThe Viable System ModelSystem 1 (S1)System 2 (S2)System 3 (S3)S3: Generating SynergiesAligning Meta-Systemic and Operational ManagementComplementarity of S3 and S2 RolesThe Control DilemmaSystem 3* (S3*)System 4 (S4)System 5 (S5)The Recursive Viable System TheoremManaging Complexity: Organisational PrinciplesResponsible AutonomySelf-RegulationAdaptationSelf-GovernanceSustainable Self-GovernanceLeaving Behind Earlier Critics of the VSM3. A Methodology to Support Self-transformation Towards Viability and SustainabilityOn VSM MethodologiesDeveloping the Self-Transformation Methodology (STM)The Ontology of the ObserverThe STM: A Systemic Methodology to Facilitate Organisation LearningOn VSM EpistemologyThe Self-Transformation Methodology in a NutshellDesigning and Starting a v&s ProjectAgreements on Organisational IdentityRich Pictures - The Organisational LandscapeStatement of Organisational IdentityRecursive AnalysisVSM Preliminary DiagnosisMapping the Viable System of the System in FocusDoing a VSM Preliminary DiagnosisComplete VSM DiagnosisMapping v&s Roles and MechanismsIdentifying and Mapping Diagnostic IssuesAssessing System 1’s InteractionsThe Inside and Now (Systems 1, 2, and 3)The Outside and Then (Systems 3, 4, and 5)Aligning Strategy, Structure, and Information SystemsAligning Strategy and StructureStrategic Information ManagementSelf-Transformation Projects and PlanMonitoring and Assessing PerformanceTeam SyntegritySummary4. Towards More Resilient and Healthier Organisations and SocietiesOrganisational Health, Resilience, and Self-GovernanceEarly Alarms from the COVID-19 PandemicTowards More Resilient IndividualsRepresenting an Individual as a Viable (and Sustainable) SystemTowards More Viable and Sustainable IndividualsEarly Alarms to Businesses v&sTowards More Resilient BusinessesCase study – M*Clean (with A.C. Martinez)Towards More Resilient Health ProvidersPreliminary VSM Analysis of the NHS in EnglandVSM Diagnosis of a Surgery Unit in a Local English Health Trust (with J. Walker and K. Grover)Conclusions5. 'Fits like a Glove': The VSM for Supporting More Systemic Educational OrganisationsFrom Non-Systemic to Systemic Education and Educational InstitutionsWhat Is a Non-Systemic Approach to Education?Towards More Systemic Educational InstitutionsSelf-Transformation of ELF – The Whole Story (with Jon Walker and Vladimir Pop)Clarifying the School’s IdentityRecursive AnalysisVSM DiagnosisAligning Strategy, Structure, and Information SystemsSelf-Transformation Projects and PlanMonitoring and Assessing PerformanceReviewing Elf’s Self-TransformationDesigning TINTA, a Systemic Education Online Provider (with G. Ramirez)TINTA’s Approach to Systemic EducationAgreeing on TINTA’s IdentityRecursive Analysis - TINTA’s Primary ServicesDesigning TINTA’s Organisation with VSM CriteriaTINTA Coming to Life: First Self-AssessmentSystemic Education in Higher Education InstitutionsSystemic Management in Universities around the WorldMagdalena University (v&s) Case StudySelf-Transformation of Educational Institutions - Post-COVID-196. v&s in Organisational NetworksComplex Organisational Systems as Social NetworksThe Rise of Social Networks (21st Century)What is a v&s Network?Background Research on VSM and Social NetworksExample: A v&s Industrial NetworkDesigning a State-Owned Enterprise on a Complex Environment (with A. Al Hinai and J. Walker)Complexity Challenges to Design a National Broadband NetworkVSM Design of the OBCVSM DesignSocial Network AnalysisFive Years LaterRedesigning a Latin-American 2nd Generation Broadband Network (with M. Giraldo and J. Walker)The N2BN IdentityRecursive AnalysisVSM DiagnosisAligning Strategy and StructureDesigning Strategies for Supply Chain Integration at the UK Offshore Wind Industry (with Julija Danilova)Analysing SCI with the STMVSM FindingsSuggested Reorganisation StrategiesLearning from the ResearchDilemmas of Self-Governance in Organisational Networks7. Sustainable Self-GovernanceContemporary Approaches to GovernanceTraditional Approaches to GovernanceCollaborative Approaches to GovernanceSustainable Self-Governance (v&s)Towards Sustainable Self-GovernanceA Framework to Assess Sustainable Self-GovernanceExample: Sustainable Self-Governance in a Socio-Ecological SystemAssessing Self-Governance in an Indigenous Community in the Amazon (with C. Duque)Background on Their Governance StructuresVSM Analysis of Their Governance StructuresLearning from the v&s Project (by C. Duque)Sustainable Self-Governance in an Afro-Caribbean Community (with C. Duran)Past and Present of the Orika Community Council (OCC)Assessing Sustainable Self-Governance at OrikaLearning from the Case Study (by C. Duran)Conclusions8. Facilitating Systemic Change with the VSMSystemic Change: What It Is and How It Can Be DoneThe Cybersyn ProjectStrategic Information Management at the Colombian President’s Office (1990-1992)Re-Engineering the National Auditing Office (NAO) in ColombiaRedesigning the NAOThe Model of the StateImplementing the ‘Systemic Auditing Process’Learning from the ProjectMonitoring the Impact of National Programs to Reduce Poverty in ColombiaRedesigning the National School System (NSS) in ColombiaDeveloping the National Environmental System (NES) in ColombiaThe Relevance of Beers’ Legacy to Facilitate Systemic ChangeThe Need for Massive Systemic Changes to Improve Response to the COVID-19 Pandemic9. An Idea Whose Time Has ComeLessons on Using the VSM for Sustainable Self-GovernanceDeveloping VSM-Related Methodologies and ToolsVSM and MultimethodologyVSM and Team SyntegrityAddressing Traditional Criticisms to the VSMVSM Research LandscapeConclusionsIndex

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Book SynopsisWhile organisations become more and more global, they also become more and more dispersed and virtual. This challenges the sense of a shared organisational identity and the ability of employees to communicate personally held knowledge. To address these challenges this book offers an innovative multidisciplinary approach to knowledge communication in global organisations. The book develops a multidisciplinary analytical lens through which to understand employee identity formations and knowledge communication practises. Using detailed analyses of interviews from a real organisation, the book builds an understanding of how 21st century employees make sense of a virtual organisational reality characterised by multiple simultaneous projects and virtual, dispersed teams. These analyses are conducted using a new discourse analysis method for analysing research interviews, Discursive Sensemaking Analysis. Using these methods and findings, researchers, project managers and HR professionals wTable of ContentsChapter 1: Introduction Part I: Discursive Sensemaking – Foundation, theory & method Chapter 2: Discursive Sensemaking Analysis - a foundation Chapter 3: Discursive Sensemaking Analysis – a theory Chapter 4: Discursive Sensemaking Analysis – a method Part II: Multidisciplinary perspective on knowledge communication practices in virtual teams Chapter 5: Challenges and opportunities of virtual work in global organisations Chapter 6: A vocabulary for describing virtual knowledge communication Chapter 7: Knowing as learning in Communities of Practice (CoP) Chapter 8: Professional identity as (D)iscursive construction Chapter 9: Relationships supporting virtual knowledge communication Chapter 10: Conclusion and discussion of theory and findings

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Book SynopsisBusiness operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team''s performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.Trade Review'This is a timely book for team science, with a unique perspective that uses computational approaches to study the network effect on team performance. The book has a nice balance of theory, algorithms, and empirical studies. The authors possess years of experience in the field.' Charu Aggarwal, IBM Research AI'A comprehensive study that pushes forward our understanding of and ability to forecast and design team performance - a critical, yet complex human-subject phenomenon to which this book brings in-depth technical rigor.' Leman Akoglu, Carnegie Mellon University'This pioneering book is essential to technologists, data scientists, and researchers alike, offering a modern, computational approach to the science of teaming and how to manage the convergence of people, information, and technology in networked organizations.' Norbou Buchler, US Army Data and Analysis Center'Li and Tong have provided a thorough and insightful exploration of current research on teams in networks, linking computational techniques with results from the social sciences. A pleasure to read.' Sucheta Soundarajan, Syracuse University'This brief volume is a valuable resource for managers, but managers with a strong background in data science, and for other technologists involved in designing systems that support user interactions … The added value of this book is provided by the mathematical formalisms used, which encode characteristics of the computational challenges discussed … The topical focus results in a unique volume that might lead interested readers to discover new research avenues … Recommended' J. Brzezinski, ChoiceTable of Contents1. Introduction; 2. Team performance characterization; 3. Team performance prediction; 4. Team performance optimization; 5. Team performance explanation; 6. Human agent teaming; 7. Conclusion and future work.

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Book SynopsisThis textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.Trade Review'This exciting new text covers everything today's aspiring data scientist needs to know, managing to be comprehensive as well as accessible. Like a good confidence interval, the Gabors have got you almost completely covered!' Joshua Angrist, Massachusetts Institute of Technology, winner of the Nobel Memorial Prize in Economic Sciences'This is an excellent book for students learning the art of modern data analytics. It combines the latest techniques with practical applications, replicating the implementation side of classroom teaching that is typically missing in textbooks. For example, they used the World Management Survey data to generate exercises on firm performance for students to gain experience in handling real data, with all its quirks, problems, and issues. For students looking to learn data analysis from one textbook, this is a great way to proceed.' Nicholas Bloom, Stanford University'I know of few books about data analysis and visualization that are as comprehensive, deep, practical, and current as this one; and I know of almost none that are as fun to read. Gábor Békés and Gábor Kézdi have created a most unusual and most compelling beast: a textbook that teaches you the subject matter well and that, at the same time, you can enjoy reading cover to cover.' Alberto Cairo, University of Miami'A beautiful integration of econometrics and data science that provides a direct path from data collection and exploratory analysis to conventional regression modeling, then on to prediction and causal modeling. Exactly what is needed to equip the next generation of students with the tools and insights from the two fields.' David Card, University of California, Berkeley, winner of the Nobel Memorial Prize in Economic Sciences'This textbook is excellent at dissecting and explaining the underlying process of data analysis. Békés and Kézdi have masterfully woven into their instruction a comprehensive range of case studies. The result is a rigorous textbook grounded in real-world learning, at once accessible and engaging to novice scholars and advanced practitioners alike. I have every confidence it will be valued by future generations.' Kerwin K. Charles, Yale School of Management'This book takes you by the hand in a journey that will bring you to understand the core value of data in the fields of machine learning and economics. The large amount of accessible examples combined with the intuitive explanation of foundational concepts is an ideal mix for anyone who wants to do data analysis. It is highly recommended to anyone interested in the new way in which data will be analyzed in the social sciences in the next years.' Christian Fons-Rosen, Barcelona Graduate School of Economics'This sophisticatedly simple book is ideal for undergraduate- or Master's-level Data Analytics courses with a broad audience. The authors discuss the key aspects of examining data, regression analysis, prediction, Lasso, and random forests, and more, with using elegant prose instead of algebra. Using well-chosen case studies, they illustrate the techniques and discuss all of them patiently and thoroughly.' Carter Hill, Louisiana State University'This is not an econometrics textbook. It is a data analysis textbook. And a highly unusual one - written in plain English, based on simplified notation, and full of case studies. An excellent starting point for future data analysts or anyone interested in finding out what data can tell us.' Beata Javorcik, University of Oxford'A multifaceted book that considers many sides of data analysis, all of them important for the contemporary student and practitioner. It brings together classical statistics, regression, and causal inference, sending the message that awareness of all three aspects is important for success in this field. Many 'best practices' are discussed in accessible language, and illustrated using interesting datasets.' llya Ryzhov, University of Maryland'This is a fantastic book to have. Strong data skills are critical for modern business and economic research, and this text provides a thorough and practical guide to acquiring them. Highly recommended.' John van Reenen, MIT Sloan'Energy and climate change is one of the most important public policy challenges, and high- quality data and its empirical analysis is a foundation of solid policy. Data Analysis for Business, Economics, and Policy will make an important contribution to this with its innovative approach. In addition to the comprehensive treatment of modern econometric techniques, the book also covers the less glamorous but crucial aspects of procuring and cleaning data, and drawing useful inferences from less-than-perfect datasets. As the center of gravity of the energy system shifts to developing economies where data quality is still an issue, this will provide an important and practical combination for both academic and policy professionals.' Laszlo Varro, Chief Economist, International Energy AgencyTable of ContentsPart I. Data Exploration: 1. Origins of data; 2. Preparing data for analysis; 3. Exploratory data analysis; 4. Comparison and correlation; 5. Generalizing from data; 6. Testing hypotheses; Part II. Regression Analysis: 7. Simple regression; 8. Complicated patterns and messy data; 9. Generalizing results of a regression; 10. Multiple linear regression; 11. Modeling probabilities; 12. Regression with time series data; Part III. Prediction: 13. A framework for prediction; 14. Model building for prediction; 15. Regression trees; 16. Random forest and boosting; 17. Probability prediction and classification; 18. Forecasting from time series data; Part IV. Causal Analysis: 19. A framework for causal analysis; 20. Designing and analyzing experiments; 21. Regression and matching with observational data; 22. Difference-in-differences; 23. Methods for panel data; 24. Appropriate control groups for panel data; Bibliography; Index.

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Book SynopsisAs we interact online we are creating new kinds of knowledge and community. How are these communities formed? How do we know whether to trust them as sources of information? In other words, Should we believe Wikipedia? This book explores what community is, what knowledge is, how the internet facilitates new kinds of community, and how knowledge is shaped through online collaboration and conversation. Along the way the author tackles issues such as how we represent ourselves online and how this shapes how we interact, why there is so much bad behavior online and what we can do about it. And the most important question of all: What can we as internet users and designers do to help the internet to bring out the best in us all?Trade Review'This book reminds us of conversations we're not having about online life - with family, friends, with our political representatives - because we don't know where to start. The Internet seems so opaque, that we don't know to get traction on our concern that it no longer serves our human needs. This welcome volume suggests entry points for designers and users - to start those necessary conversations about how to make online life serve our emotional and social purposes. Both scholarly and down-to-earth, filled with compelling examples, it is a textbook for classrooms, dinnertables, and policy discussions.' Sherry Turkle, MIT, Author of The Empathy Diaries, Reclaiming Conversation, Alone Together, and Life on the Screen'Should you believe Wikipedia? is an extremely useful book about the positive aspects of life online: supportive online communities, social construction of knowledge, online collaboration, the ways online communities can create and harvest social capital, how to safely and productively use Wikipedia, and more. With so much news and speculation about what is wrong about life online, this book from an OG participant and academic expert is a welcome and well-documented argument for all the good things life online can do. I would give it to every high school graduate.' Howard Rheingold, Author of Net Smart and The Virtual Community'Simply brilliant! Bruckman brings together abstract, sociological and psychological perspectives to articulate a science of online community design. Should You Believe Wikipedia? combines practical skills and theoretical understanding to implement and evaluate the social platforms of the future. This work sets the bar for what students of human-computer interaction need to understand and do to address the societal challenges brought by the ubiquity of online interactions.' Keith N. Hampton, Department of Media and Information, Michigan State University'From its birth, Amy Bruckman has understood and explained the Net. This book, both practical and theoretical, offers the most mature account that we have of how community online gets made and corrupted. At a moment when the hope of the Internet has faded for so many, this clear and powerful work gives us at least a path back, and a reason to pursue it.' Lawrence Lessig, Harvard Law School'Ultimately, the effect of reading Should You Believe Wikipedia? is like spending time with the smartest person you know. Bruckman is an excellent guide, and weaves together concepts from diverse sources into a coherent whole … In this excellent volume, Bruckman provides the background, philosophical tools, and encouragement to allow us to work towards a better world, and that is a gift in itself.' Andrew D. Miller, Computer Supported Cooperative Work'Educators interested in developing inclusive, socially just online learning experiences that are also informed by Internet culture and social institutions would not go wrong with Should You Believe Wikipedia? Online Communities and The Construction of Knowledge … as a place to start.' Naomi Barnes, Postdigital Science and EducationTable of ContentsAcknowledgments; Introduction; 1. Are online “communities” really communities?; 2. What can online collaboration accomplish?; 3. Should you believe Wikipedia?; 4. How does the internet change how we think?; 5. How do people express identity online, and why is this important for online interaction?; 6. What is bad online behavior, and what can we do about it?; 7. How do business models shape online communities?; 8. How can we help the internet to bring out the best in us all?

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Book SynopsisCollecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.Trade Review"I first taught a Ph.D. level course in business applications of data mining 10 years ago. I regularly search the web, looking for business-oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics. I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text." (Journal of the American Statistical Association, 1 January 2014)Table of ContentsPreface ix Acknowledgments xi 1. Introduction 1 Reference 6 2. Processing the Information and Getting to Know Your Data 7 2.1 Example 1: 2006 Birth Data 7 2.2 Example 2: Alumni Donations 17 2.3 Example 3: Orange Juice 31 References 39 3. Standard Linear Regression 40 3.1 Estimation in R 43 3.2 Example 1: Fuel Efficiency of Automobiles 43 3.3 Example 2: Toyota Used-Car Prices 47 Appendix 3.A The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction 53 References 54 4. Local Polynomial Regression: a Nonparametric Regression Approach 55 4.1 Model Selection 56 4.2 Application to Density Estimation and the Smoothing of Histograms 58 4.3 Extension to the Multiple Regression Model 58 4.4 Examples and Software 58 References 65 5. Importance of Parsimony in Statistical Modeling 67 5.1 How Do We Guard Against False Discovery 67 References 70 6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) 71 6.1 Example 1: Prostate Cancer 74 6.2 Example 2: Orange Juice 78 References 82 7. Logistic Regression 83 7.1 Building a Linear Model for Binary Response Data 83 7.2 Interpretation of the Regression Coefficients in a Logistic Regression Model 85 7.3 Statistical Inference 85 7.4 Classification of New Cases 86 7.5 Estimation in R 87 7.6 Example 1: Death Penalty Data 87 7.7 Example 2: Delayed Airplanes 92 7.8 Example 3: Loan Acceptance 100 7.9 Example 4: German Credit Data 103 References 107 8. Binary Classification, Probabilities, and Evaluating Classification Performance 108 8.1 Binary Classification 108 8.2 Using Probabilities to Make Decisions 108 8.3 Sensitivity and Specificity 109 8.4 Example: German Credit Data 109 9. Classification Using a Nearest Neighbor Analysis 115 9.1 The k-Nearest Neighbor Algorithm 116 9.2 Example 1: Forensic Glass 117 9.3 Example 2: German Credit Data 122 Reference 125 10. The Na¨ýve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables 126 10.1 Example: Delayed Airplanes 127 Reference 131 11. Multinomial Logistic Regression 132 11.1 Computer Software 134 11.2 Example 1: Forensic Glass 134 11.3 Example 2: Forensic Glass Revisited 141 Appendix 11.A Specification of a Simple Triplet Matrix 147 References 149 12. More on Classification and a Discussion on Discriminant Analysis 150 12.1 Fisher’s Linear Discriminant Function 153 12.2 Example 1: German Credit Data 154 12.3 Example 2: Fisher Iris Data 156 12.4 Example 3: Forensic Glass Data 157 12.5 Example 4: MBA Admission Data 159 Reference 160 13. Decision Trees 161 13.1 Example 1: Prostate Cancer 167 13.2 Example 2: Motorcycle Acceleration 179 13.3 Example 3: Fisher Iris Data Revisited 182 14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods 185 14.1 R Packages for Tree Construction 185 14.2 Chi-Square Automatic Interaction Detection (CHAID) 186 14.3 Ensemble Methods: Bagging, Boosting, and Random Forests 188 14.4 Support Vector Machines (SVM) 192 14.5 Neural Networks 192 14.6 The R Package Rattle: A Useful Graphical User Interface for Data Mining 193 References 195 15. Clustering 196 15.1 k-Means Clustering 196 15.2 Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions 204 15.3 Hierarchical Clustering Procedures 212 References 219 16. Market Basket Analysis: Association Rules and Lift 220 16.1 Example 1: Online Radio 222 16.2 Example 2: Predicting Income 227 References 234 17. Dimension Reduction: Factor Models and Principal Components 235 17.1 Example 1: European Protein Consumption 238 17.2 Example 2: Monthly US Unemployment Rates 243 18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares 247 18.1 Three Examples 249 References 257 19. Text as Data: Text Mining and Sentiment Analysis 258 19.1 Inverse Multinomial Logistic Regression 259 19.2 Example 1: Restaurant Reviews 261 19.3 Example 2: Political Sentiment 266 Appendix 19.A Relationship Between the Gentzkow Shapiro Estimate of “Slant” and Partial Least Squares 268 References 271 20. Network Data 272 20.1 Example 1: Marriage and Power in Fifteenth Century Florence 274 20.2 Example 2: Connections in a Friendship Network 278 References 292 Appendix A: Exercises 293 Exercise 1 294 Exercise 2 294 Exercise 3 296 Exercise 4 298 Exercise 5 299 Exercise 6 300 Exercise 7 301 Appendix B: References 338 Index 341

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Book SynopsisLead your organization into the industrial revolution of analytics with The Analytics Revolution The topics of big data and analytics continue to be among the most discussed and pursued in the business world today.Trade Review"A good read" (Supply Management, May 2015)Table of ContentsForeword xi Preface xv Acknowledgments xxv PART I The Revolution Has Begun 1 Chapter 1 Understanding Operational Analytics 3 Defining Operational Analytics 4 Welcome to Analytics 3.0 10 How Analytics Are Changing Business 20 Putting Operational Analytics in Perspective 26 Wrap-Up 30 Notes 31 Chapter 2 More Data . . . More Data . . . Big Data! 33 Cutting through the Hype 33 Preparing for Big Data 39 Putting Big Data in Context 48 Wrap-Up 58 Notes 59 Chapter 3 Operational Analytics in Action 61 Improving Customer Experiences 62 Time Is of the Essence 68 Making Us Safer 71 Increasing Operational Efficiency 74 Improving Our Lives in the Future 77 Finding Unexpected Value in Data 79 Wrap-Up 83 Notes 84 PART II Laying the Foundation 87 Chapter 4 Want Budget? Build the Business Case! 89 Setting the Priorities 89 Choosing the Right Decision Criteria 93 Business Case Framework to Consider 101 Tips for Creating a Winning Business Case 108 Wrap-Up 115 Notes 116 Chapter 5 Creating an Analytic Platform 117 Planning 118 Building 123 Using 140 Wrap-Up 143 Notes 145 Chapter 6 Governance and Privacy 147 Setting the Stage for Governance 148 Deciding Where Analytics Happen 154 Governing Operational Analytics 158 Privacy 165 Wrap-Up 172 Notes 173 PART III Making Analytics Operational 175 Chapter 7 The Analytics 177 Creating Operational Analytics Processes 177 Expanding into New Analytics Disciplines 181 Focusing Analytics Efforts 187 Comparing Analytics Approaches 193 Lessons from the Past 198 Wrap-Up 204 Notes 205 Chapter 8 The Analytics Organization 207 A Major Shift Has Occurred 207 Staffing 209 Organizing 218 Succeeding 225 Wrap-Up 234 Notes 235 Chapter 9 The Analytics Culture 237 Instilling the Proper Mind-set 237 Implementing Effective Policies 245 Facilitating Success 250 Enabling and Handling the Right Failures 256 Wrap-Up 261 Notes 262 Conclusion Join the Revolution! 263 About the Author 267 Index 269

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Book SynopsisThe classic guide to instructional design, fully updated for the new ways we learn Rapid Instructional Design is the industry standard guide to creating effective instructional materials, providing no-nonsense practicality rather than theory-driven text. Beginning with a look at what instructional design really means, readers are guided step-by-step through the ADDIE model to explore techniques for analysis, design, development, intervention, and evaluation. This new third edition has been updated to cover new applications, technologies, and concepts, and includes many new templates, real-life examples, and additional instructor materials. Instruction delivery has expanded rapidly in the nine years since the second edition''s publication, and this update covers all the major advances in the field. The major instructional models are expanded to apply to e-learning, MOOCs, mobile learning, and social network-based learning. Informal learning and communities of practice Table of ContentsTool List vii Preface for the Third Edition xiii Introduction xv Purpose xv Audiences xvi Special Elements xvii Organization of the Book xix Chapter 1 What Is This Instructional Design Stuff Anyway? 1 Why Instructional Design? 2 What Is Instructional Design? 3 A Few Definitions 5 Advantages of Instructional Design 9 Disadvantages of Instructional Design 13 Chapter 2 Before You Do Anything: Pre-Instructional Design Activities 17 Organizational Needs 18 Performance Assessment 23 Assessing Training Needs 33 Choosing Needs to Address 39 The Needs Assessment Report 42 Quick and Dirty Cost/Benefit Analysis 47 Training Needs Analysis 54 Chapter 3 Do You Know What You Need to Do? Analysis 63 Data-Collection Methods 64 Why Analyze? 73 Types of Analysis 73 Computer-Aided Analysis 102 Chapter 4 How to Do It: Design 107 Make the Right Decision Now 107 Delivery Decision 108 Objectives 128 Design Documents 143 Course Descriptions 161 Gathering Content 162 Adding Structure: The Instructional Plan 168 Trainee Evaluation (Test Questions and Tests) 178 Hints for Designing in Various Formats 196 Chapter 5 Doing It Right: Development 203 End Products of Development 203 The Facilitator Guide as an End Product 205 Scripts and Storyboards 231 Participant Packages and Other Print Materials 235 Other Media 239 Hints for Developing Material 247 Chapter 6 Getting It Where It Does the Most Good: Implementation 263 Beta Tests and Pilots 263 Reviews Revisited 279 Common Implementation Issues 282 Other Instructor-Led Classroom Implementation Needs 287 Hints for Implementation 299 Field Trips 306 Chapter 7 Did It Do Any Good? Evaluation 311 Why Evaluation? 311 The Key to Good Evaluation 312 Types of Evaluation 315 Evaluation of Self-Instruction Programs 334 Revisions: What to Do with What You’ve Learned 338 Hints for Evaluating 344 Chapter 8 Doing It Faster: More Rapid Design Shortcuts 353 Software for Instructional Design 354 Analysis Software 355 Test Development Software 355 Miscellaneous Software 356 Rapid Prototyping 356 Learning Objects/Granular Training 357 Public Courses 358 Off-The-Shelf Programs 358 Technology Vendors 358 Performance Support–Based “Training” 359 Problem-Based Learning (PBL) 361 Training Management Systems/Learning Management Systems (LMS)/Learning Content Management Systems (LCMS) 361 Digital Cameras 362 What Does an ID Do? 362 Miscellaneous 364 Chapter 9 Asynchronous e-Learning Design 367 Definitions 367 Creating and Implementing an e-Learning System 369 Determining a Comprehensive e-Learning Strategy 371 Designing and Developing Good Programs 373 Learning Management Systems and Learning Content Management Systems 374 Preparing the Organization Globally for e-Learning 378 Self-Direction and e-Learning 380 Planning for a Smooth, Successful Implementation 384 Creating an Effective Monitoring and Evaluation Plan 385 Asynchronous e-Learning Design and Development 387 Analysis 387 Material Development 390 Learner Evaluation 397 Learner Interfaces 398 Beta Tests and Pilots 399 Software 400 Repurposing 401 Evaluating Asynchronous e-Learning Programs 403 Summary 403 Chapter 10 Synchronous e-Learning Design 409 Advantages 409 Disadvantages and Misconceptions 410 Design Considerations for Synchronous e-Learning 413 Mini-Interactions 414 Repurposing and Redesigning Synchronous e-Learning Programs 415 Other Synchronous Activities 416 More Detailed Facilitator Guides 419 Learner Guide 422 General Technology Considerations 423 Media 425 Designing Continuing Interactions 430 Audience Analysis 432 Implementation 434 Online Learning: A Special Type of e-Learning 446 What the Learners Say 451 Chapter 11 New Design Applications 453 Flipped Classrooms 453 Mobile Learning 457 Virtual Learning Environments 461 Social Network–Based Learning 463 Massive Open Online Courses (MOOCs) 466 Communities of Practice 468 Informal Learning 469 The Cloud 471 Glossary 475 Suggested Readings 499 Other Resources 511 About the Author 517 Index 519

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Book SynopsisLeverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. ThisTable of ContentsForeword xv Preface xxi Acknowledgments xxiii Chapter 1 Introduction to Data-Driven Concepts 1 Introduction 2 Current Approaches 2 Is There a Crisis in Geophysical and Petrophysical Analysis? 3 Applying an Analytical Approach 4 What Are Analytics and Data Science? 5 Meanwhile, Back in the Oil Industry 8 How Do I Do Analytics and Data Science? 10 What Are the Constituent Parts of an Upstream Data Science Team? 13 A Data-Driven Study Timeline 15 What Is Data Engineering? 18 A Workflow for Getting Started 19 Is It Induction or Deduction? 30 References 32 Chapter 2 Data-Driven Analytical Methods Used in E&P 34 Introduction 35 Spatial Datasets 36 Temporal Datasets 37 Soft Computing Techniques 39 Data Mining Nomenclature 40 Decision Trees 43 Rules-Based Methods 44 Regression 45 Classification Tasks 45 Ensemble Methodology 48 Partial Least Squares 50 Traditional Neural Networks: The Details 51 Simple Neural Networks 54 Random Forests 59 Gradient Boosting 60 Gradient Descent 60 Factorized Machine Learning 62 Evolutionary Computing and Genetic Algorithms 62 Artificial Intelligence: Machine and Deep Learning 64 References 65 Chapter 3 Advanced Geophysical and Petrophysical Methodologies 68 Introduction 69 Advanced Geophysical Methodologies 69 How Many Clusters? 70 Case Study: North Sea Mature Reservoir Synopsis 72 Case Study: Working with Passive Seismic Data 74 Advanced Petrophysical Methodologies 78 Well Logging and Petrophysical Data Types 78 Data Collection and Data Quality 82 What Does Well Logging Data Tell Us? 84 Stratigraphic Information 86 Integration with Stratigraphic Data 87 Extracting Useful Information from Well Reports 89 Integration with Other Well Information 90 Integration with Other Technical Domains at the Well Level 90 Fundamental Insights 92 Feature Engineering in Well Logs 95 Toward Machine Learning 98 Use Cases 98 Concluding Remarks 99 References 99 Chapter 4 Continuous Monitoring 102 Introduction 103 Continuous Monitoring in the Reservoir 104 Machine Learning Techniques for Temporal Data 105 Spatiotemporal Perspectives 106 Time Series Analysis 107 Advanced Time Series Prediction 108 Production Gap Analysis 112 Digital Signal Processing Theory 117 Hydraulic Fracture Monitoring and Mapping 117 Completions Evaluation 118 Reservoir Monitoring: Real-Time Data Quality 119 Distributed Acoustic Sensing 122 Distributed Temperature Sensing 123 Case Study: Time Series to Optimize Hydraulic Fracture Strategy 129 Reservoir Characterization and Tukey Diagrams 131 References 138 Chapter 5 Seismic Reservoir Characterization 140 Introduction 141 Seismic Reservoir Characterization: Key Parameters 141 Principal Component Analysis 146 Self-Organizing Maps 146 Modular Artificial Neural Networks 147 Wavelet Analysis 148 Wavelet Scalograms 157 Spectral Decomposition 159 First Arrivals 160 Noise Suppression 161 References 171 Chapter 6 Seismic Attribute Analysis 174 Introduction 175 Types of Seismic Attributes 176 Seismic Attribute Workflows 180 SEMMA Process 181 Seismic Facies Classification 183 Seismic Facies Dataset 188 Seismic Facies Study: Preprocessing 189 Hierarchical Clustering 190 k-means Clustering 193 Self-Organizing Maps (SOMs) 194 Normal Mixtures 195 Latent Class Analysis 196 Principal Component Analysis (PCA) 198 Statistical Assessment 200 References 204 Chapter 7 Geostatistics: Integrating Seismic and Petrophysical Data 206 Introduction 207 Data Description 208 Interpretation 210 Estimation 210 The Covariance and the Variogram 211 Case Study: Spatially Predicted Model of Anisotropic Permeability 214 What Is Anisotropy? 214 Analysis with Surface Trend Removal 215 Kriging and Co-kriging 224 Geostatistical Inversion 229 Geophysical Attribute: Acoustic Impedance 230 Petrophysical Properties: Density and Lithology 230 Knowledge Synthesis: Bayesian Maximum Entropy (BME) 231 References 237 Chapter 8 Artificial Intelligence: Machine and Deep Learning 240 Introduction 241 Data Management 243 Machine Learning Methodologies 243 Supervised Learning 244 Unsupervised Learning 245 Semi-Supervised Learning 245 Deep Learning Techniques 247 Semi-Supervised Learning 249 Supervised Learning 250 Unsupervised Learning 250 Deep Neural Network Architectures 251 Deep Forward Neural Network 251 Convolutional Deep Neural Network 253 Recurrent Deep Neural Network 260 Stacked Denoising Autoencoder 262 Seismic Feature Identification Workflow 268 Efficient Pattern Recognition Approach 268 Methods and Technologies: Decomposing Images into Patches 270 Representing Patches with a Dictionary 271 Stacked Autoencoder 272 References 274 Chapter 9 Case Studies: Deep Learning in E&P 276 Introduction 277 Reservoir Characterization 277 Case Study: Seismic Profile Analysis 280 Supervised and Unsupervised Experiments 280 Unsupervised Results 282 Case Study: Estimated Ultimate Recovery 288 Deep Learning for Time Series Modeling 289 Scaling Issues with Large Datasets 292 Conclusions 292 Case Study: Deep Learning Applied to Well Data 293 Introduction 293 Restricted Boltzmann Machines 294 Mathematics 297 Case Study: Geophysical Feature Extraction: Deep Neural Networks 298 CDNN Layer Development 299 Case Study: Well Log Data-Driven Evaluation for Petrophysical Insights 302 Case Study: Functional Data Analysis in Reservoir Management 306 References 312 Glossary 314 About the Authors 320 Index 323

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Book SynopsisA comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for railway track engineering, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering addresses common issues with the implementation of big data applications while exploring the limitations, advantages, and disadvantages of more conventional methods. In addition, the book provides a unifying approach to analyzing large volumes of data in railway track engineering using an array of proven methods and software technologies. Dr. Attoh-Okine considers some of today's most notable applications and implementations and highlights when a particular method or algorithm is most appropriate. Throughout, the book presents numerous real-world examples to illustrate the latest railway engineering big data applications of predictive analytics, such aTable of ContentsPreface xi Acknowledgments xiii 1 Introduction 1 1.1 General 1 1.2 Track Components 2 1.3 Characteristics of Railway Track Data 4 1.4 Railway Track Engineering Problems 6 1.5 Wheel–Rail Interface Data 11 1.6 Geometry Data 15 1.7 Track Geometry DegradationModels 20 1.8 Rail Defect Data 25 1.9 Inspection and Detection Systems 33 1.10 Rail Grinding 37 1.11 Traditional Data Analysis Techniques 40 1.12 Remarks 41 References 42 2 Data Analysis – Basic Overview 49 2.1 Introduction 49 2.2 Exploratory Data Analysis (EDA) 49 2.3 Symbolic Data Analysis 53 2.4 Imputation 54 2.5 Bayesian Methods and Big Data Analysis 56 2.6 Remarks 57 References 57 3 Machine Learning: A Basic Overview 59 3.1 Introduction 59 3.2 Supervised Learning 60 3.3 Unsupervised Learning 61 3.4 Semi-Supervised Learning 61 3.5 Reinforcement Learning 61 3.6 Data Integration 63 3.7 Data Science Ontology 63 3.8 Imbalanced Classification 69 3.9 Model Validation 70 3.10 Ensemble Methods 71 3.11 Big P and Small N (P â N) 74 3.12 Deep Learning 79 3.13 Data Stream Processing 95 3.14 Remarks 105 References 105 4 Basic Foundations of Big Data 113 4.1 Introduction 113 4.2 Query 116 4.3 Taxonomy of Big Data Analytics in Railway Track Engineering 123 4.4 Data Engineering 124 4.5 Remarks 130 References 130 5 Hilbert–Huang Transform, Profile, Signal, and Image Analysis 133 5.1 Hilbert–Huang Transform 133 5.2 Axle Box Acceleration 150 5.3 Analysis 151 5.4 Remarks 153 References 153 6 Tensors – Big Data in Multidimensional Settings 157 6.1 Introduction 157 6.2 Notations and Definitions 158 6.3 Tensor Decomposition Models 161 6.4 Application 164 6.5 Remarks 170 References 171 7 Copula Models 175 7.1 Introduction 175 7.2 Pair Copula: Vines 184 7.3 Computational Example 186 7.4 Remarks 192 References 193 8 Topological Data Analysis 197 8.1 Introduction 197 8.2 Basic Ideas 197 8.3 A Simple Railway Track Engineering Application 203 8.4 Remarks 204 References 204 9 Bayesian Analysis 207 9.1 Introduction 207 9.2 Markov Chain Monte Carlo (MCMC) 210 9.3 Approximate Bayesian Computation 210 9.4 Markov Chain Monte Carlo Application 216 9.5 ABC Application 219 9.6 Remarks 221 References 222 10 Basic Bayesian Nonparametrics 225 10.1 General 225 10.2 Dirichlet Family 226 10.3 Dirichlet Process 227 10.4 Finite Mixture Modeling 231 10.5 Bayesian Nonparametric Railway Track 232 10.6 Remarks 233 References 233 11 Basic Metaheuristics 235 11.1 Introduction 235 11.2 Remarks 237 References 239 12 Differential Privacy 241 12.1 General 241 12.2 Differential Privacy 242 12.3 Remarks 247 References 247 Index 249

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Book SynopsisLearn about the successesof Black entrepreneurs through a collection of unique case studies Successful Black Entrepreneursisan insightful collection ofHarvard Business Schoolcase studiesaboutBlack entrepreneurs succeeding in a variety of industries andthrough different routes, including start-ups, franchising, and acquisitions. The book also recognizes and celebrates Black entrepreneurial excellence, as it takes the reader through the stages of entrepreneurship, including ideation, raising capital, growing the company, and taking it public. In addition to identifying the positive aspects of Black entrepreneurship, the book also uses data, research, and anecdotes to highlight the challenges faced by Black entrepreneurs, including: An inability to access capital from traditional financial institutions like banks and private equity firmsThe requirement to practice racial concealment in the company ofWhite customers in order to achieve success Perfect for students, aspiring entrepreneurs, and established business leaders,Successful Black Entrepreneursprovidespracticalperspectivesfrom Black entrepreneurs about what it takes to succeed in business.Table of ContentsPreface vii Introduction xi Chapter 1 History of Black Entrepreneurship 1 Chapter 2 Importance of Black Entrepreneurs 37 Chapter 3 Black Start-Up Entrepreneurs 43 Chapter 4 Entrepreneurship Through Acquisitions 75 Chapter 5 Entrepreneurship Through Franchising 125 Chapter 6 Access to Capital for Black Entrepreneurs 157 Chapter 7 Black Turnaround Entrepreneurs 203 Chapter 8 Entrepreneurial Exits: Selling the Company 235 Chapter 9 Black Intrapreneurs 285 Epilogue 311 Appendix 313 Acknowledgments 317 About the Author 321 Index 323

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Book SynopsisHow to lead the digital transformation of governments Digital Government Excellence: Lessonsfrom Effective Digital Leadersdelivers a fascinating treatment of digital leadership as governments around the world start or restart the digital transformation of their work and service delivery. The author provides a playbook on how to achieve digital excellence via interviews with 20 remarkable digital government leaders from around the world. Each one offers insights on strategies for how to incorporate the best of digital into public services and practical tips on leading digital reforms and delivery teams. The book also: Explores how to begin the task of making all of government to go digital or go deeper and bolder in this direction, including the first steps and beyondHighlights leadership styles and practices for effective and lasting delivery of digital strategies and reformsProvides food for thought about whatit takes to be an impactful digital transformation leader in government and beyond The book is ideal for Chief Digital/Information/Technology Officers or digital agency leaders in public service. Digital Government Excellence is also an indispensable resource for any practitioner, policymaker or political leader in governments at any level, as well as any student or advisor of governments looking intohow to deliver digital transformation in the public sector.Table of ContentsPreface ix Acknowledgments xi List of Abbreviations xiii Introduction xv 1. Aisha Bin Bishr, Dubai, United Arab Emirates (UAE) 1 2. Alex Benay, Canada 15 3. Anna-Maija Karjalainen, Finland 31 4. Barry Lowry, Ireland 45 5. Bolor-Erdene Battsengel, Mongolia 59 6. Cheow Hoe Chan, Singapore 71 7. Daniel Abadie, Argentina 83 8. Diego Piacentini, Italy 97 9. Hillary Hartley, Ontario, Canada 109 10. Innocent Bagamba Muhizi, Rwanda 123 11. José Clastornik, Uruguay 135 12. Lars Frelle-Petersen, Denmark 147 13. Luis Felipe Monteiro, Brazil 159 14. Mike Bracken, United Kingdom 173 15. Pedro Silva Dias, Portugal 189 16. Randall Brugeaud, Australia 203 17. Shai-Lee Spiegelman, Israel 219 18. Taavi Kotka, Estonia 233 19. Tim Occleshaw, New Zealand 245 20. Yolanda Martínez, Mexico 261 Epilogue: How to Lead a Government to Digital Excellence 279 About the Author 287 Index 289

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Book SynopsisPresents an ultimate theory of knowledge-based management and organizational knowledge creation based on empirical research and an extensive literature review. It explores knowledge management as a global concept and is relevant to any company that wants to prosper and thrive in the global knowledge economy.Trade Review"Nonaka and his co-authors must be congratulated on developing an understanding of this ephemeral concept of knowledge, and on widening it out beyond business objectives to general wellbeing." - Gordon Harris, Professional ManagerTable of ContentsIntroduction Characteristics of Knowledge The Theoretical Framework Vision and Driving Objectives: Values for the Common Good Eisai Honda Implications Ba Mayekawa Manufacturing Co., Ltd Kumon Implications Dialogue and Practice: Leveraging Organizational Dialects Seven-Eleven Japan Muji Implications Dynamic Knowledge Assets in Process YKK JFE: Synthesizing Experience Implications Leadership: Fostering Distributed Excellence in the Organization Canon Toyota Prius Implications Conclusions

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Book SynopsisAs technology evolves, it can be difficult to maintain a competitive edge. The management of intangible resources like competence, relationships, brands, processes and systems becomes increasingly important in such a world. Intellectual Capital as a Management Tool reviews the evidence to demonstrate where the intellectual capital view of the firm has made major contributions. The book introduces an updated version of the Intellectual Capital Navigator as an operational tool to help managers maximise value generation from an organisations portfolio of diverse resources. This tool is the only tool that enables organisations to use the resource based view of the firm in an operational way. The book also discusses future developments of the Intellectual Capital Navigator, increasing its precision around the financial aspects of the organisation.The book has broad application across all types of organisations and in all operating environments and is vTrade Review'Filling a gap in the intellectual capital field, the book reviews critical developments in the field in the last two decades and discusses future versions of the Intellectual Capital Navigator and the Effector. Academics, researchers, students and managers and business leaders will benefit from the innovative views and insights as well as practical lessons.' - Patricia Ordóñez de Pablos, The University of Oviedo, SpainTable of ContentsChapter 1: The Evolution of the Intellectual Capital Perspective; Chapter 2: The Contribution of The Intellectual Capital View of The Firm to Key Business Issues; Chapter 3: The Intellectual Capital Navigator as a Strategic Tool; Chapter 4: Analyses Using the Navigator and the Effector

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Book SynopsisAre you overworked, unappreciated and under-resourced? This book understands you, and provides years and years of User Support experience packed into one volume. The ''How To'' book that every IT department needs, it will help turn your helpdesk into a company asset. How to be successful at probably the most stressful job in IT This book offers tools for measuring productivity and features ten key steps for successful support, while User Support successes and failures are revealed in true life case studies.This book gives you techniques for:Justifying staff and other expenditure Gaining senior management support Getting the users on your side Running a motivated and productive team Designing and managing services and service levels The second edition of this popular book brings updates to several of the author''s ideas, strategies and techniques with new material on: Customer Relationship Management - definition and the role of the helpdesk E-Support and the Internet Contrasting the CTrade Review"Noel's passion for the industry combined with years of personal experience in the field makes him uniquely qualified to help you deliver great helpdesk customer service no matter how hopeless you think your current work environment is."- Phil Verghis, Vice President, Service Delivery, Akamai Technologies USA and multiple award-winning author of the Internet Helpdesk FAQ"Helpdesk managers weary of industry jargon, nonsensical acronyms, and technological hype will be relieved to find a book that talks their language. Despite years of experience, Noel Bruton has managed to remain firmly rooted in reality, and is able to clearly describe the complexities involved in the helpdesk, while remembering that it is often the most basic of issues that require the most attention."- James West, Editor, Customer Service News Magazine"This book won't tell you how to get your printer working with your word processor - it's better than that. It tells you how to deploy your Support staff so as to have the best chance of preventing or fixing such problems. If you're in charge of a PC Support group, or would like to be one day, get this book and read it." - Dr. Alan SolomonTable of ContentsPart 1 What is ‘support’?; Chapter 1 Defining computer user support; Chapter 2 Support as an IT function; Chapter 3 Forms of support; Chapter 4 Typical support structures; Chapter 5 Support functions; Part 2 Client management; Chapter 6 What is a ‘customer’?; Chapter 7 What do they want?; Chapter 8 Prioritizing clients; Chapter 9 Keeping in contact; Chapter 10 Customer service in IT support; Chapter 11 A view from above; Chapter 12 Marketing the support de; Part ment; Part 3 Service management; Chapter 13 Analysing service needs; Chapter 14 Putting services in place; Chapter 15 Excellence in support service; Chapter 16 Service level agreements; Chapter 17 Support from outside; Chapter 18 The international dimension; Part 4 Workload management; Chapter 19 Reactivity and proactivity; Chapter 20 Managing the queue; Chapter 21 Delegation and escalation; Chapter 22 Measurement and reporting; Chapter 23 Controlling the workflow; Part 5 Resources management; Chapter 24 Justifying user support expenditure; Chapter 25 Knowledge; Chapter 26 Equipment; Part 6 Staff management; Chapter 27 The ideal support person; Chapter 28 Motivation and productivity; Chapter 29 Staffing and structure; Chapter 30 User support management considerations;

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Book SynopsisLike other critical organizational assets, information is a strategic asset that requires high level of oversight in order to be able to effectively use it for organizational decision-making, performance improvement, cost management, and risk mitigation.Adopting an information governance program shows a healthcare organizationâs commitment to managing its information as a valued strategic asset. Information governance serves the dual purpose of optimizing the ability to extract clinical and business value from healthcare information while meeting compliance needs and mitigating risk. Healthcare organizations that have information governance programs will have a competitive edge over others and contributes to safety and quality of care, population health, operational efficiency and effectiveness, and cost reduction initiatives. This is a much-needed book in the healthcare market space. It will explain, in clear terms, how to develop, launch, and oversee an Information GTrade Review"Proper attention to information governance most industries may mean the difference between profit and loss, but in healthcare it can mean the difference between life and death. As a healthcare professional, no longer can you afford to manage your information as anything less than the true asset it is. Thankfully, Robert Smallwood has given you this playbook to do just that." Douglas B. Laney, VP and Distinguished Analyst, Gartner, and author of Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage Table of ContentsPreface. INTRODUCTION TO INFORMATION GOVERNANCE. The Healthcare Information Governance Imperative. IG PLANNING & POLUCY DEVELOPMENT. Information Risk Planning & Management. Strategic Planning and Best Practices for IG. IG Policy Development. STRATEGIES AND ADVICE FROM THE FIELD. IG Education is Key to IG Success. IG in Healthcare. IG and InfoSec in Healthcare. IG and Privacy Issues. Identifying, Classifying and Managing Your Information Assets. Creating an Information Asset Register to Assis in Privacy Compliance. IG Insight: The Soft Stuff is the Hard Stuff. Anticipating Conflicts in Your IG Program. Information Governance and Brand Management: A Critical Link. Information Governance by Design: "Baking" IG into Everyday Processes. Long Term Digital Preservation in IG Programs. Veteran Advice on Getting Your IG Program Launched. Glossary. About the Author. About the Contributors.

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Book SynopsisWhy does information technology disappoint or enslave us? Why do so many information systems projects collapse? How can we do better? There are many technical, social, economic and other aspects to consider. How do we ensure we take all these into account as we research ITC or employ them? ICT affects our lives and world more profoundly than ever before. How may we understand it? This book employs philosophy to lay foundations for understanding the complexity of ICT, in five areas: The nature of information and computers, and artificial intelligence; The use of ICT at work and home, for serious and less-serious use; The ICT features that annoy or delight us; Societal issues, such as surveillance, e-government, ICT in developing countries, climate change, what technological progress is and what is the role of ICT as a whole and of the information systems field; ICT development - includinTable of ContentsPART 1 1. Introduction 2. Philosophies 3. Dooyeweerd's Philosophy 4. Foundations, Research and Practice PART 2 5. The Nature of Information and Communication Technology 6. Understanding ICT Use 7. Understanding ICT Features 8. Understanding ICT and Society 9. Understanding ICT Development PART 3 10. Overview and Reflection 11. Contributions and Limitations 12. Opportunities and Recommendations

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Book SynopsisDavid Kroenke has many years of teaching experience at Colorado State University, Seattle University, and the University of Washington. He has led dozens of seminars for college professors on the teaching of information systems and technology; in 1991, the International Association of Information Systems named him Computer Educator of the Year. In 2009, David was named Educator of the Year by the Association of Information Technology Professionals-Education Special Interest Group (AITP-EDSIG). David worked for the U.S. Air Force and Boeing Computer Services. He was a principal in the startup of three companies, serving as the vice president of product marketing and development for the Microrim Corporation and as chief of database technologies for Wall Data, Inc. He is the father of the semantic object data model. David's consulting clients have included IBM, Microsoft, and Computer Sciences Corporations, as well as numerous smaller companies. Recently, David Table of Contents I. Why MIS? 1. The Importance of MIS 2. Collaboration Information Systems 3. Strategy and Information Systems II. Information Technology 4. Hardware, Software, and Mobile Systems 5. Database Processing 6. The Cloud III. Using IS for Competitive Advantage 7. Processes, Organizations, and Information Systems 8. Social Media Information Systems 9. Business Intelligence Systems IV. Information Systems Management 10. Information Systems Security 11. Information Systems Management 12. Information Systems Development The International Dimension Application Exercises Glossary

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Book SynopsisThe Venture Capital Investment Framework Venture capital is the economic engine that drives entrepreneurship and innovation through capital investments, board membership, advice, introductions to relevant employees, and customers. Despite the outsized importance of venture capital, the inner workings remain hidden. Venture is still a mentor-led industry and it is an industry where you have to do a lot of self-educationyou have to learn by doing, and you have to get up to speed quickly. Until now. Author William Lin spent over a decade in venture capital, starting in an entry-level position, helping to start a leading VC firm from scratch, and eventually becoming Managing Partner. In The VC Field Guide: Fundamentals of Venture Capital, Lin shares his unique framework, the Venture Capital Investment Framework, to help any venture capitalist, entrepreneur, or investor make better investment decisions, quicker. He delivers an incisive and practical handbooTable of ContentsForeword xi Preface xiii Acknowledgments xv Part I Introduction 1 Introduction 3 My Background 3 Who Is This Book For? 9 The Framework’s Origin 10 Endnote 14 Part II The Venture Capital Investment Framework 15 Chapter 1 The Venture Capital Investment Framework 17 Startup Investing 17 Chapter 2 Who = Team 23 Different Paths to Success 26 Curiosity Is a Must- Have for Founding Teams 27 Individual or Company Growth? 30 The CEO and Team Challenges 31 Curiosity and ? 33 Failure Is a Valued Asset 37 In- Person Data 38 Team Dynamics that Work 40 Chapter 3 What = Problem 47 Deep Questions, First Principles 49 Common Sense and Bias 52 You Don’t Have to Solve Your Own Problem 53 Finding a Problem 55 Chapter 4 When = Timing 59 Timing = Environment 61 Specialization Leads to Success, Leading to Optionality 62 Generational Timing 64 Timing and Disruption 66 The Rise and Fall of Companies 68 Chapter 5 Where = Market 73 How VCs Look at TAM 74 Strategy for VCs Based on the Size of the Firm 79 Calculating TAM and What Does It Imply? 81 Chapter 6 Why = Solution 83 Solving One Problem Really Well 85 Building the Right Solution 87 Building a Sustainable Business 90 Chapter 7 How = Scale 91 The Sales Organization 93 Sales Organization and Customer Alignment 96 The Technology Organization 98 Chapter 8 How to Use the VCIF 101 The VCIF in Action: An Investment We Made 102 An Investment We Didn’t Make 107 Stages 112 Part III Notes to Stakeholders 115 Chapter 9 The Venture Capital Role 117 The Basics of a Venture Capital Firm 118 Winners Lose a Lot 123 Metrics 126 My First Trial by Fire 128 Chapter 10 Notes for Students 135 Three Entry- Level Career Paths in Venture 135 Chapter 11 Notes for Current VCs 141 VC Career Paths 141 VC Career Myths 147 Chapter 12 Notes for Entrepreneurs: Some Thoughts 151 Chapter 13 Notes for Startup Customers 155 Chapter 14 Notes for LPs: Characteristics of Some of the Best VCs 159 Sector Knowledge 161 Chapter 15 Long- Term Planning: Venture Capital Cycles, Optionality, Starting a VC Firm 165 Venture Capital Cycles 167 Optionality 168 Could You Start Your Own VC Firm? 172 Conclusion 175 Afterword 179 Glossary 181 Index 185

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Book SynopsisDramatically improve your decisions with data and AI In Decision Intelligence: Transform Your Team and Organization with AI-Driven Decision-Making, a team of pioneering decision and AI strategists delivers a digestible and hands-on resource for professionals at every part of the decision-making journey. The book discusses the latest technology and approaches that bridge the gap between behavioral science, data science, and technological innovation. Discover how leaders from various industries and environments are using data and AI to make better future decisions, taking both human as well as business factors into account. This book covers: A demystifying behind-the-scenes peek inside how AI models, forecasts, and optimization for business challenges really work, and why they open up entirely new possibilities. A business-ready introduction to decision intelligence, exploring why traditional decision-making strategies are outdated andTable of ContentsFigures List xi Foreword xiii Acknowledgments xvii Introduction xxi Chapter 1 Decoding Decision-Making: Good and Bad Decisions 1 How to Measure the Quality of a Decision 6 The History of Decision-Making 10 The Impact of Technology on Business Decision-Making in the 21st Century 14 Regaining the Human Aspects 17 Chapter 2 Why Traditional Decision-Making Is Broken 21 The New (Corporate) Normal: An Increasingly Dynamic and Complex Reality with Uncertainties 23 Why Data Analytics and Business Intelligence Can’t Keep up with the New Reality 31 The Illusion of Human Control: Will We Ever Be Able to Make the Best Possible Choice? 35 Chapter 3 Decision Intelligence: Making Relevant Information Visible and Actionable 39 How to Shift Your Decision-Making Perspective 43 The Ultimate Partnership Between Humans and Machines 47 Chapter 4 The Business Value of Decision Intelligence 55 From Using DI as a Strategy to DecisionOS 57 Step up the Operational Game: Recurring Use-Cases for Companies 61 Chapter 5 Decision Intelligence in Practice: Industry Examples of Applied DI 67 DI in Logistics 69 DI in Retail 78 DI in Pricing and Marketing 83 Chapter 6 The Technology Stack: Applying AI Systems for Decision-Making 91 Data, the Backbone to Leverage Business Value 95 Artificial Intelligence: Understanding the Patterns Behind 102 Optimization Is Key for Better Business Outcomes 116 Chapter 7 Decision Intelligence Organization: The Key to Unlocking the True Potential of Data-Driven Decision-Making 125 Culture Eats Intelligent Decision-Making for Breakfast 129 Ways of Working in DI Organizations 132 The Four Rs of the DI Organization 137 Cultivating a DI Organization: A Symphony of Skills 142 Recognizing Biases in Your Decision-Making Process 147 Chapter 8 Leading a Decision Intelligence Organization 155 Trust and Courage 158 Transparency and Experimentation 162 Psychological Safety: The Secret Ingredient for a Decision Intelligence Organization 167 Embracing Failure and Forging Forward 175 Epilogue 179 References 183 Index 195

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Book SynopsisTransform your company's AI and data frameworks to unlock the true power of disruptive new tech In From Data to Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines, accomplished entrepreneur and AI strategist Vineet Vashishta delivers an engaging and insightful new take on making the most of data, artificial intelligence, and technology at your company. You'll learn to change the culture, strategy, structure, and operational framework of your company to take full advantage of disruptive advances in tech. The author explores fascinating work being undertaken by firms in the real world, as well as high-value use cases and innovative projects and products made possible by realigning organizational frameworks using the capabilities of new technologies. He explains how to get everyone in your company on the same page, following a single framework, in a way that ensures individual departments get what they want and need. You'll learn to outline aTable of ContentsIntroduction vii Chapter 1 Overview of the Frameworks 1 Chapter 2 There Is No Finish Line 15 Chapter 3 Why Is Transformation So Hard? 37 Chapter 4 Final vs. Evolutionary Decision Culture 59 Chapter 5 The Disruptor’s Mindset 71 Chapter 6 A Data- Driven Definition of Strategy 89 Chapter 7 The Monolith—Technical Strategy 103 Chapter 8 Who Survives Disruption? 125 Chapter 9 Data—The Business’s Hidden Giant 139 Chapter 10 The AI Maturity Model 155 Chapter 11 The Human-Machine Maturity Model 171 Chapter 12 A Vision for AI Opportunities 185 Chapter 13 Discovering AI Treasure 201 Chapter 14 Large Model Monetization Strategies—Quick Wins 215 Chapter 15 Large Model Monetization Strategies—The Bigger Picture 229 Chapter 16 Assessing the Business’s AI Maturity 251 Chapter 17 Building the Data and AI Strategy 273 Chapter 18 Building the Center of Excellence 287 Chapter 19 Data and AI Product Strategy 301 Index 325

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Book SynopsisInformation technology auditing and Sarbanes-Oxley compliance have several overlapping characteristics. They both require ethical accounting practices, focused auditing activities, a functioning system of internal control, and a close watch by the board's audit committee and CEO. Written as a contribution to the accounting and auditing professions as well as to IT practitioners, IT Auditing and Sarbanes-Oxley Compliance: Key Strategies for Business Improvement links these two key business strategies and explains how to perform IT auditing in a comprehensive and strategic manner.Based on 46 years of experience as a consultant to the boards of major corporations in manufacturing and banking, the author addresses objectives, practices, and business opportunities expected from auditing information systems. Topics discussed include the concept of internal control, auditing functions, internal and external auditors, and the responsibilitiTable of ContentsMANAGEMENT CONTROL. Internal Control and Information Technology. Case Studies on Internal Control’s Contribution. Auditing Functions. Internal and External Audit. The Board’s Accountability for Audit. CASE STUDIES ON AUDITING A COMPANY’S INFORMATION TECHNOLOGY. Auditing the Information Technology Functions. Strategic IT Auditing: A Case Study. A Constructive View – Suggestions for IT Restructuring. A Broader Perspective of IT Auditing. TECHNICAL EXAMPLES IN AUDITING IT FUNCTIONS. Auditing IT Response Time and Reliability. Auditing the Security System. CAN IT HELP IN COMPLIANCE? THE CASE OF SOX. Sarbanes-Oxley Compliance and IT’s Contribution. What If: Backtesting Sarbanes-Oxley. INDEX.

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Book SynopsisThe book will appeal to anyone trying to make sense of contemporary higher education.Trade ReviewAs a collection, Academic Capitalism in the Age of Globalization presents ways that universities have become more entrepreneurial, more interdisciplinary, more competitive, and more marketable as postsecondary institutions and labor markets have become progressively globalized. Canadian Journal of Higher Education oTable of ContentsForewordAcknowledgmentsPart I: Academic Capitalism Revisited Chapter 1. Academic Capitalism in Theory and Research Chapter 2. Retheorizing Academic Capitalism: Actors, Mechanisms, Fields, and Networks Chapter 3. University Revolutions and Academic Capitalism: A Historical Perspective Chapter 4. Exploring the Academic Capitalist Time Regime Chapter 5. Learning to Litigate: University Patents in the Knowledge Economy Chapter 6. Academic Capitalism and Practical Activity: Extending the Research ProgramChapter 7. Extending Academic Capitalism by Foregrounding Academic LaborPart II: Academic Capitalism and Globalization Chapter 8. The Global Enterprise of Higher Education Chapter 9. Transnationalization of Academic Capitalism through Global Production Networks Chapter 10. How to Explain Academic Capitalism: A Mechanism-Based Approach Chapter 11. Peripheral Knowledge-Driven Economies: What Does Academic Capitalism Have to Say? Chapter 12. Academic Capitalism in Greater China: Theme and Variations Chapter 13. Risky Business: Academic Capitalism, Globalization, and the Risk UniversityChapter 14. Developing a Conceptual Model to Study the International Student Market List of Contributors Index

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Book SynopsisThe book will appeal to anyone trying to make sense of contemporary higher education.Trade ReviewAs a collection, Academic Capitalism in the Age of Globalization presents ways that universities have become more entrepreneurial, more interdisciplinary, more competitive, and more marketable as postsecondary institutions and labor markets have become progressively globalized. Canadian Journal of Higher Education oTable of ContentsForewordAcknowledgmentsPart I: Academic Capitalism Revisited Chapter 1. Academic Capitalism in Theory and Research Chapter 2. Retheorizing Academic Capitalism: Actors, Mechanisms, Fields, and Networks Chapter 3. University Revolutions and Academic Capitalism: A Historical Perspective Chapter 4. Exploring the Academic Capitalist Time Regime Chapter 5. Learning to Litigate: University Patents in the Knowledge Economy Chapter 6. Academic Capitalism and Practical Activity: Extending the Research ProgramChapter 7. Extending Academic Capitalism by Foregrounding Academic LaborPart II: Academic Capitalism and Globalization Chapter 8. The Global Enterprise of Higher Education Chapter 9. Transnationalization of Academic Capitalism through Global Production Networks Chapter 10. How to Explain Academic Capitalism: A Mechanism-Based Approach Chapter 11. Peripheral Knowledge-Driven Economies: What Does Academic Capitalism Have to Say? Chapter 12. Academic Capitalism in Greater China: Theme and Variations Chapter 13. Risky Business: Academic Capitalism, Globalization, and the Risk UniversityChapter 14. Developing a Conceptual Model to Study the International Student Market List of Contributors Index

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Book SynopsisGreat CIOs consistently exceed key stakeholders'' expectations and maximize the business value delivered through their company''s technology. What''s their secret?Sure, IT professionals need technological smarts, plus an understanding of their company''s goals and the competitive landscape. But the best of them possess a far more potent ability: they forge good working relationships with everyone involved in an IT-enabled project, whether it''s introducing new hardware or implementing a major business transformation.In The CIO Edge, the authors draw on Korn/Ferry International''s extensive empirical data on leadership competencies as well as Gartner''s research on IT trends and the CIO role. They prove that, for IT leaders, mastering seven essential skills yields big results.This new book lays out the people-to-people leadership competencies that the highest-performing CIOs have in common—including the ability to inspire others, connect with a dTrade Review"So if you're wondering what your legacy will be or how to shape it in the present, then The CIO Edge will assist you on your quest with practical case studies and easy, applicable steps to deliver results." Public CIO "...this fast-paced, enjoyable book dives headfirst into the key strengths that highly effective CIOs possess."- CIO Magazine and Computerworld "Throughout the book there are good references and case study interviews with CIOs, and while it is US CIOs who dominate, there is some good experience to glean. Overall The CIO Edge is worth a read for new CIOs, or it's one to pass onto those members of your team you are coaching as part of a succession plan." -- CIO MagazineTable of ContentsTOC Part 1: Characteristics of High-Performing CIOs Chapter 1: Introduction Chapter 2: Leadership First. Everything Else Second Chapter 3: The Make Up of the High Performing CIO Part 2: Cultivating Your People Skills Chapter 4: Lead Differently Chapter 5: Embrace Your Softer Side Chapter 6: Inspire Others Chapter 7: Build People, Not Systems Part 3: The Payoff, Becoming a Great CIO Chapter 8: The Right Relationships, the Right Results Chapter 9: Mastering the Art of Communication Chapter 10: The Professional (and Personal) Payoff

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Book SynopsisAvailable Open Access under CC-BY-NC licence. This book examines the increasing importance of engagement with non-academic groups and actors in the co-production of knowledge and real-world influence in academic research.Trade Review“Focusing on the personhood of researchers and scholars, this collection addresses important practical and epistemological questions about knowledge construction and impact both within and outside of the academy. A valuable bookshelf addition for anyone interested in the research process and product and the relationship between them.” Gayle Letherby, Plymouth University and The University of Greenwich, UKTable of ContentsIntroduction ~ Dr Sarah Marie Hall and Dr Ralitsa Hiteva; Dwarfism Expectations ~ Dr. Erin Pritchard; Disability Policy for Nomadic Positionings ~ Prof. Pamela Moss and Dr Michael J Prince; "You're not from 'round here, are you?": Class and accent as opportunity and obstacle in research encounters ~ Dr Sarah Marie Hall; A feminist retrofit: Spaces of encounter in collaborative domestic energy research ~ Prof. Gordon Waitt; Enacting intersectionality: the academic as critic, advocate and policymaker ~Dr. JP Catungal; Encountering "Experts" in the gender-social science-engineering nexus ~ Dr. Ralitsa Hiteva; Participants as experts in the own lives ~ Dr. Michael Richardson; Conclusions, key lessons and tactics: addressing social difference in spaces of engagement.

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Book SynopsisIntelligence is used daily by law enforcement personnel across the world in operations to combat terrorism and drugs and to assist in investigating serious and organized crime. Managing Intelligence: A Guide for Law Enforcement Professionals is designed to assist practitioners and agencies build an efficient system to gather and manage intelligence effectively and lawfully in line with the principles of intelligence-led policing.Research for this book draws from discussions with hundreds of officers in different agencies, roles, and ranks from the UK, United States, Australia, New Zealand, and Canada. Highlighting common misunderstandings in law enforcement about intelligence, the book discusses the origins of these misunderstandings and puts intelligence in context with other policing models. It looks at human rights and ethical considerations as well as some of the psychological factors that inhibit effective intelligence management. With practicTable of ContentsThe Concept of Managing Intelligence. Intelligence in Context. Understanding Intelligence. Human Rights, Legislation, and Ethics. Psychology and Intelligence Management. The Intelligence Cycle (Revisited). Building an Intelligence Management System. Analysis. Intelligence Originating from Traditional. Law Enforcement Activities. Intelligence from Covert Operations. Using Intelligence. Challenges and the Way Ahead. Index.

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