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Evidence-based decision-making : how to leverage available data & avoid cognitive biases / / Andrew D. Banasiewicz
| Evidence-based decision-making : how to leverage available data & avoid cognitive biases / / Andrew D. Banasiewicz |
| Autore | Banasiewicz Andrew D. |
| Pubbl/distr/stampa | New York, NY : , : Routledge, , 2019 |
| Descrizione fisica | 1 online resource (270 pages) : illustrations (black and white) |
| Disciplina | 153.83 |
| Soggetto topico |
Decision making
Cognition Selectivity (Psychology) |
| ISBN |
9781351050067
9781351050074 1351050052 1351050079 1351050060 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; List of Figures; Preface; PART I: Decision-Making Challenges; 1 Subjective Evaluations; Thinking and Games; Mind vs. Machine; Learning and Remembering; The Decision-Making Brain; 2 Non-Generalizable Objectivity; Familiar Clues; Anecdotal Evidence; Nonrepresentative Samples; 3 Mass Analytics; Digitization of Life; Data as the New Normal; Data in Organizations; The Analytics Industry; PART II: Evidence-Based Practice; 4 Evidence-Based Movement; The Practice and Science of Management; Evidence-Based Practice; The Road Ahead
5 The Essence of EvidenceWhat is Evidence?; Empirical Evidence; Experiential Evidence; Internalizing Evidence; PART III: The Empirical & Experiential Evidence Framework; 6 Probabilistic Thinking; Decision Uncertainty; Evidence Pooling; Cross-Type Amalgamation; 7 The 3E Framework; Organizational Decision-Making; The Empirical & Experiential Evidence Framework; Insight Extraction; Believability of Evidence; 8 Sourcing & Assessing: Operational Data; Data, Research, and Decision-Making; Probabilistic Analyses of Organizational Data; Operational Data and Databases Getting Started with Operational DataExploring Operational Data; Exploratory Data Analysis; Confirmatory Data Analysis; 9 Sourcing & Assessing: Research, Norms, and Judgment; Thematic Analyses of Empirical Research; Summarizing Norms & Standards; Pooling Expert Judgment; PART IV: Evidence-Based Decision-Making in Organizations; 10 Internal Design & Dynamics; Organizations as Human Collectives; Business Organizations; Organizations and Decision-Making; The 3E Framework & Organizational Dynamics; 11 External Forces & Influences; External Forces; Non-Systematic Influences Appendix A: Cognitive Biases Appendix B: Inter-Expert Reliability; Author Biography; Index |
| Record Nr. | UNINA-9910793470403321 |
Banasiewicz Andrew D.
|
||
| New York, NY : , : Routledge, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Evidence-based decision-making : how to leverage available data & avoid cognitive biases / / Andrew D. Banasiewicz
| Evidence-based decision-making : how to leverage available data & avoid cognitive biases / / Andrew D. Banasiewicz |
| Autore | Banasiewicz Andrew D. |
| Pubbl/distr/stampa | New York, NY : , : Routledge, , 2019 |
| Descrizione fisica | 1 online resource (270 pages) : illustrations (black and white) |
| Disciplina | 153.83 |
| Soggetto topico |
Decision making
Cognition Selectivity (Psychology) |
| ISBN |
9781351050067
9781351050074 1351050052 1351050079 1351050060 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; List of Figures; Preface; PART I: Decision-Making Challenges; 1 Subjective Evaluations; Thinking and Games; Mind vs. Machine; Learning and Remembering; The Decision-Making Brain; 2 Non-Generalizable Objectivity; Familiar Clues; Anecdotal Evidence; Nonrepresentative Samples; 3 Mass Analytics; Digitization of Life; Data as the New Normal; Data in Organizations; The Analytics Industry; PART II: Evidence-Based Practice; 4 Evidence-Based Movement; The Practice and Science of Management; Evidence-Based Practice; The Road Ahead
5 The Essence of EvidenceWhat is Evidence?; Empirical Evidence; Experiential Evidence; Internalizing Evidence; PART III: The Empirical & Experiential Evidence Framework; 6 Probabilistic Thinking; Decision Uncertainty; Evidence Pooling; Cross-Type Amalgamation; 7 The 3E Framework; Organizational Decision-Making; The Empirical & Experiential Evidence Framework; Insight Extraction; Believability of Evidence; 8 Sourcing & Assessing: Operational Data; Data, Research, and Decision-Making; Probabilistic Analyses of Organizational Data; Operational Data and Databases Getting Started with Operational DataExploring Operational Data; Exploratory Data Analysis; Confirmatory Data Analysis; 9 Sourcing & Assessing: Research, Norms, and Judgment; Thematic Analyses of Empirical Research; Summarizing Norms & Standards; Pooling Expert Judgment; PART IV: Evidence-Based Decision-Making in Organizations; 10 Internal Design & Dynamics; Organizations as Human Collectives; Business Organizations; Organizations and Decision-Making; The 3E Framework & Organizational Dynamics; 11 External Forces & Influences; External Forces; Non-Systematic Influences Appendix A: Cognitive Biases Appendix B: Inter-Expert Reliability; Author Biography; Index |
| Record Nr. | UNINA-9910828640303321 |
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Banasiewicz Andrew D.
|
||
| New York, NY : , : Routledge, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Marketing database analytics : transforming data for competitive advantage / / Andrew D. Banasiewicz
| Marketing database analytics : transforming data for competitive advantage / / Andrew D. Banasiewicz |
| Autore | Banasiewicz Andrew D. |
| Pubbl/distr/stampa | New York : , : Routledge, , 2013 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 658.800285/574 |
| Soggetto topico |
Marketing
Marketing - Data processing Database management |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-135-12568-6
0-203-07646-X 1-135-12569-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Title Information ; Title Page; Copyright Page; Dedication; Table of Contents; List of Illustrations; About the Author; Preface; 1 The Informational Advantage; The Age of Data; The Believability Factor; What is Knowledge?; Knowledge as a Strategic Asset; The Emerging Field of Database Analytics; About This Book; 2 The Marketing Database Analytics Process; Approach and Philosophy; The Marketing Database Analytics Process; Part I: Need Identification; 3 Organizational Objectives and Informational Needs; Organizational Goals; Informational Objectives; 4 Skills and Tools; Skills and Tools
Process RequirementsPart II: Knowledge Creation; 5 Analytic Planning; Analytic Planning; Planning Framework; Mini-Case 5.1: Analytic Planning and Customer Loyalty Programs; 6 Data Basics; Data and Databases; Databases in a Nutshell; Data: Types; Data: Contents; It Is Not About the Digits; Textual Information; Single- vs. Multi-Source Analytics; Mini-Case 6.1: Multi-Source Analytics; 7 Analytic File Creation; Data Gathering; Analytic Dataset Creation; Metadata; Mini-Case 7.1: My Know-How and Your Data; 8 Exploratory Data Analyses; Initiating Data Analyses Database Analytics and Data ExplorationThe Exploratory Data Analysis Process; Data Reduction; Mini-Case 8.1: New Insights or Spurious Associations?; 9 Segmentation; Simple Yet Complex; The Evaluation Process; Loyalty Segmentation; Mini-Case 9.1: Segmenting the Leisure Boating Market; 10 Behavioral Predictions; Behavioral Predictions and Decision Support; Threat Anticipation; Opportunity Identification; Mini-Case 10.1: Consumer Coupons and Look-Alike Modeling; 11 Action-Attributable Incrementality; Incrementality Measurement; Impact Assessment; Action-Result Effect Estimation; Profit Impact Mini-Case 11.1: Estimating the Efficacy of Wireless Carriers' Advertising12 Database Scoring; Model Calibration; Database Scoring; Mini-Case 12.1: Consumer Coupons and Look-Alike Modeling Scoring; 13 From Findings To Knowledge; Knowledge Implementation; Deployment; Updating; Mini-Case 13.1: Deploying Database Marketing as a New Customer Acquisition Tool; Part III: Dissemination; 14 Organizational Knowledge Sharing; Analytic Insights and Decisioning; Dashboards and Scorecards; Report Deployment: A Normative Framework; Mini-Case 14.1: Communicating with Non-Technical Audiences; 15 In Closing Sputnik and InternetThe Semantic Web; Notes; Bibliography; Index |
| Record Nr. | UNINA-9910462960603321 |
|
Banasiewicz Andrew D.
|
||
| New York : , : Routledge, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Marketing database analytics : transforming data for competitive advantage / / Andrew D. Banasiewicz
| Marketing database analytics : transforming data for competitive advantage / / Andrew D. Banasiewicz |
| Autore | Banasiewicz Andrew D. |
| Pubbl/distr/stampa | New York : , : Routledge, , 2013 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 658.800285/574 |
| Soggetto topico |
Marketing
Marketing - Data processing Database management |
| ISBN |
1-135-12568-6
0-203-07646-X 1-135-12569-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Title Information ; Title Page; Copyright Page; Dedication; Table of Contents; List of Illustrations; About the Author; Preface; 1 The Informational Advantage; The Age of Data; The Believability Factor; What is Knowledge?; Knowledge as a Strategic Asset; The Emerging Field of Database Analytics; About This Book; 2 The Marketing Database Analytics Process; Approach and Philosophy; The Marketing Database Analytics Process; Part I: Need Identification; 3 Organizational Objectives and Informational Needs; Organizational Goals; Informational Objectives; 4 Skills and Tools; Skills and Tools
Process RequirementsPart II: Knowledge Creation; 5 Analytic Planning; Analytic Planning; Planning Framework; Mini-Case 5.1: Analytic Planning and Customer Loyalty Programs; 6 Data Basics; Data and Databases; Databases in a Nutshell; Data: Types; Data: Contents; It Is Not About the Digits; Textual Information; Single- vs. Multi-Source Analytics; Mini-Case 6.1: Multi-Source Analytics; 7 Analytic File Creation; Data Gathering; Analytic Dataset Creation; Metadata; Mini-Case 7.1: My Know-How and Your Data; 8 Exploratory Data Analyses; Initiating Data Analyses Database Analytics and Data ExplorationThe Exploratory Data Analysis Process; Data Reduction; Mini-Case 8.1: New Insights or Spurious Associations?; 9 Segmentation; Simple Yet Complex; The Evaluation Process; Loyalty Segmentation; Mini-Case 9.1: Segmenting the Leisure Boating Market; 10 Behavioral Predictions; Behavioral Predictions and Decision Support; Threat Anticipation; Opportunity Identification; Mini-Case 10.1: Consumer Coupons and Look-Alike Modeling; 11 Action-Attributable Incrementality; Incrementality Measurement; Impact Assessment; Action-Result Effect Estimation; Profit Impact Mini-Case 11.1: Estimating the Efficacy of Wireless Carriers' Advertising12 Database Scoring; Model Calibration; Database Scoring; Mini-Case 12.1: Consumer Coupons and Look-Alike Modeling Scoring; 13 From Findings To Knowledge; Knowledge Implementation; Deployment; Updating; Mini-Case 13.1: Deploying Database Marketing as a New Customer Acquisition Tool; Part III: Dissemination; 14 Organizational Knowledge Sharing; Analytic Insights and Decisioning; Dashboards and Scorecards; Report Deployment: A Normative Framework; Mini-Case 14.1: Communicating with Non-Technical Audiences; 15 In Closing Sputnik and InternetThe Semantic Web; Notes; Bibliography; Index |
| Record Nr. | UNINA-9910787318603321 |
|
Banasiewicz Andrew D.
|
||
| New York : , : Routledge, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Marketing database analytics : transforming data for competitive advantage / / Andrew D. Banasiewicz
| Marketing database analytics : transforming data for competitive advantage / / Andrew D. Banasiewicz |
| Autore | Banasiewicz Andrew D. |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | New York : , : Routledge, , 2013 |
| Descrizione fisica | 1 online resource (397 p.) |
| Disciplina | 658.800285/574 |
| Soggetto topico |
Marketing
Marketing - Data processing Database management |
| ISBN |
1-135-12568-6
0-203-07646-X 1-135-12569-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover ; Title Information ; Title Page; Copyright Page; Dedication; Table of Contents; List of Illustrations; About the Author; Preface; 1 The Informational Advantage; The Age of Data; The Believability Factor; What is Knowledge?; Knowledge as a Strategic Asset; The Emerging Field of Database Analytics; About This Book; 2 The Marketing Database Analytics Process; Approach and Philosophy; The Marketing Database Analytics Process; Part I: Need Identification; 3 Organizational Objectives and Informational Needs; Organizational Goals; Informational Objectives; 4 Skills and Tools; Skills and Tools
Process RequirementsPart II: Knowledge Creation; 5 Analytic Planning; Analytic Planning; Planning Framework; Mini-Case 5.1: Analytic Planning and Customer Loyalty Programs; 6 Data Basics; Data and Databases; Databases in a Nutshell; Data: Types; Data: Contents; It Is Not About the Digits; Textual Information; Single- vs. Multi-Source Analytics; Mini-Case 6.1: Multi-Source Analytics; 7 Analytic File Creation; Data Gathering; Analytic Dataset Creation; Metadata; Mini-Case 7.1: My Know-How and Your Data; 8 Exploratory Data Analyses; Initiating Data Analyses Database Analytics and Data ExplorationThe Exploratory Data Analysis Process; Data Reduction; Mini-Case 8.1: New Insights or Spurious Associations?; 9 Segmentation; Simple Yet Complex; The Evaluation Process; Loyalty Segmentation; Mini-Case 9.1: Segmenting the Leisure Boating Market; 10 Behavioral Predictions; Behavioral Predictions and Decision Support; Threat Anticipation; Opportunity Identification; Mini-Case 10.1: Consumer Coupons and Look-Alike Modeling; 11 Action-Attributable Incrementality; Incrementality Measurement; Impact Assessment; Action-Result Effect Estimation; Profit Impact Mini-Case 11.1: Estimating the Efficacy of Wireless Carriers' Advertising12 Database Scoring; Model Calibration; Database Scoring; Mini-Case 12.1: Consumer Coupons and Look-Alike Modeling Scoring; 13 From Findings To Knowledge; Knowledge Implementation; Deployment; Updating; Mini-Case 13.1: Deploying Database Marketing as a New Customer Acquisition Tool; Part III: Dissemination; 14 Organizational Knowledge Sharing; Analytic Insights and Decisioning; Dashboards and Scorecards; Report Deployment: A Normative Framework; Mini-Case 14.1: Communicating with Non-Technical Audiences; 15 In Closing Sputnik and InternetThe Semantic Web; Notes; Bibliography; Index |
| Record Nr. | UNINA-9910809209903321 |
|
Banasiewicz Andrew D.
|
||
| New York : , : Routledge, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Organizational learning in the age of data / / Andrew Banasiewicz
| Organizational learning in the age of data / / Andrew Banasiewicz |
| Autore | Banasiewicz Andrew D. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (316 pages) |
| Disciplina | 621.3 |
| Collana | EAI/Springer Innovations in Communication and Computing |
| Soggetto topico | Electrical engineering |
| ISBN | 3-030-74866-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Author Biography -- Chapter 1: Out with the Old - In with the New -- 1.1 Organizational Decision-Making -- 1.1.1 Hypercompetition and Intuition -- 1.1.2 To Intuit or to Infer? -- 1.1.3 Learning Organizations Versus Organizational Learning -- 1.1.3.1 Organizational Learning -- 1.1.4 Time for a New Perspective -- 1.1.4.1 The Scientific Method in the Age of Data -- 1.1.4.2 Axial Periods -- 1.2 Knowledge and Knowing -- 1.3 The Learning with Data Framework -- 1.3.1 Learning Inputs and Outcomes -- 1.3.2 Learning Modalities -- 1.3.2.1 Reason-Based Learning -- 1.3.2.2 Technology-Based Learning -- 1.3.3 Data Analytic Literacy -- 1.3.4 A Microperspective: Analytics as Search for Answers -- 1.3.4.1 Data-Driven Knowledge Creation Skilling -- 1.3.4.2 Learning Outcome Assessment -- 1.4 Organizational Learning Transformation -- References -- Chapter 2: Democratization of Organizations -- 2.1 Organizations as Human Collectives -- 2.1.1 A Brief History -- 2.1.1.1 Business Organizations -- 2.1.2 Commercial Organizations -- 2.1.3 Non-Commercial Organizations -- 2.1.4 Organizational Dynamics -- 2.2 Organizations as Commercial and Social Units -- 2.2.1 Organizational Governance -- 2.2.2 Operating Structure -- 2.2.2.1 Centralized Structures -- 2.2.2.2 Decentralized Structures -- 2.2.3 Organizational Culture -- 2.2.4 The Interaction Effect -- 2.3 Changing Organizational Dynamics -- 2.3.1 The Duty of Care and Business Judgement Rule -- 2.3.2 Stakeholder Activism -- 2.4 Changing Organizational Environment -- 2.4.1 Now That Everyone Has Voice, Is Anyone Listening? -- 2.4.2 De-Departmentalization of Innovation -- 2.4.2.1 Centralized - Decentralized - Networked -- 2.4.3 The Rise of the Gig Economy -- 2.5 Inspiration or Perspiration? -- Chapter 3: Organizational Dynamics and Individual Cognition -- 3.1 Informational Overabundance.
3.2 Institutional Influences -- 3.2.1 Organizational Form -- 3.2.1.1 Policies and Procedures -- 3.2.2 Group Dynamics -- 3.2.2.1 Organizational DNA -- 3.3 Individual-Level Factors -- 3.3.1 The Mechanics of Learning and Remembering -- 3.3.1.1 Mental Models -- 3.3.2 Cognitive, Behavioural and Emotive Factors -- 3.3.3 Learning Modes -- 3.4 Organizational Sensemaking -- 3.4.1 Searching for What Matters -- 3.4.2 New Knowledge -- Chapter 4: A Brief History of Learning with Data -- 4.1 Axial Ages -- 4.2 The Changing Nature of Work -- 4.2.1 Learning Foci -- 4.3 From the Age of Reason to the Age of Data -- 4.3.1 Statistical Inference -- 4.3.2 Machine Learning -- Chapter 5: Learning with Data in the Twenty-First Century -- 5.1 Digital Literacy -- 5.2 From Digital Nativity to Digital Productivity -- 5.2.1 Getting to Know Data -- 5.2.2 Making Sense of Data Structures -- 5.2.2.1 The Data-Information-Knowledge Continuum -- 5.2.3 Analytic Know-How -- 5.3 Building Data Analytic Proficiency -- 5.3.1 Data Preparation -- 5.3.1.1 Analytic Planning -- 5.3.1.2 Data Due Diligence -- 5.3.1.3 Data Feature Engineering -- 5.3.2 Derived Effects -- 5.3.2.1 Informational Domain Specification -- 5.3.3 Data Utilization -- 5.4 Statistical Data Utilization -- 5.4.1 Exploratory Analyses -- 5.4.1.1 Relating Categorical Variables -- 5.4.1.2 Relating Continuous Variables -- 5.4.1.3 Relating Categorical and Continuous Variables -- 5.4.2 Confirmatory Analyses -- 5.4.3 Predictive Analyses -- 5.5 Algorithmic Data Utilization -- 5.5.1 Data Mining Versus Machine Learning -- 5.5.2 Numeric Data Mining -- 5.5.3 Text Data Mining -- 5.5.3.1 Frequency Count and Tabulation -- 5.5.3.2 Natural Language Processing -- 5.6 Visual Data Learning -- 5.6.1 Design and Storytelling Considerations -- 5.6.1.1 Purpose, Audience and Content -- 5.6.1.2 Visual Grammar -- 5.6.1.3 Data Inputs -- 5.6.1.4 Storytelling. 5.6.2 Informational Visualization -- 5.6.3 Scientific Visualization -- 5.6.4 Graph Data Structures -- Chapter 6: Managing by the Numbers -- 6.1 The Evolution of the Art and Science of Managing -- 6.1.1 By the Numbers -- 6.1.2 To the Numbers -- 6.1.3 To Versus by the Numbers and Top-Down Versus Bottom-Up -- 6.2 The Latency Tiers -- 6.2.1 Latency Tiers Case Study: Customer Loyalty -- 6.3 Analytically Enabled Management -- 6.3.1 Learning Modalities and Performance of Tasks -- 6.3.2 Data Analytic Outcome Utilization -- 6.3.3 Assessment of Data Analytic Adeptness -- 6.3.3.1 Framing the Degree of Data Analytic Adeptness -- 6.4 Rights and Responsibilities -- 6.4.1 Unauthorized Data Access -- 6.4.2 Improper Data Access -- 6.4.3 Publication of De-anonymizable Data -- 6.5 Learning to Learn -- References -- Chapter 7: Decision Automation -- 7.1 From Decision Support to Decision Automation -- 7.1.1 Decision Support Systems Case: Buyer Loyalty Classification -- 7.1.2 Decision Automation Case: Social Credit Scoring -- 7.1.3 Autonomous Decision Systems Case: Self-Driving Cars -- 7.1.4 ADM Adoption Hurdles -- 7.1.4.1 The Next Step -- 7.2 The Heart of ADM: Machine Learning -- 7.2.1 The Key Modalities -- 7.3 The Soul of ADM: Human-Computer Interaction -- 7.3.1 The Question of Accountability -- 7.3.2 Human Versus Machine Mental Models -- 7.3.2.1 Data Feature Engineering -- 7.3.3 Right Tool for the Task -- 7.4 Data-Enabled Creativity -- 7.4.1 Reason-Algorithm Collaboration -- 7.4.2 The New Frontier of Imagination -- References -- Chapter 8: In Machine We Trust -- 8.1 Algocracy Anyone? -- 8.1.1 Algorithmic Bias -- 8.1.2 Decision Logic Transparency -- 8.1.3 Accountability -- 8.1.4 Concentration of Power -- 8.2 The Internet of Things -- 8.2.1 Applications and Implications -- 8.2.1.1 Privacy and the IoT -- 8.3 Informational Automation and Organizational Learning. 8.3.1 Variable Associations -- 8.3.1.1 Informative Versus Non-informative Associations -- 8.3.1.2 Causation: A Special Case of Association -- 8.3.2 Data Reimagined -- 8.3.2.1 Bespoke Aggregates -- 8.3.2.2 Graph Metavariables -- 8.3.2.3 Metadata -- 8.4 Algorithmic Decision-Making and Organizational Value -- 8.4.1 Analytics: From Edge to Augmented -- Reference -- Chapter 9: Letting Go: Knowing When and How -- 9.1 Brain as a Computer -- 9.1.1 The Decision-Making Brain -- 9.1.1.1 Food for Thought -- 9.1.2 Objective Evidence Versus Subjective Beliefs -- 9.2 Informational Literacy -- 9.2.1 The Digital Dimension -- 9.2.2 The Data Dimension -- 9.2.3 The Analytic Dimension -- 9.2.4 The Communication Dimension -- 9.3 In Data We Trust -- 9.4 Probabilistic Thinking -- 9.4.1 Understanding Probability -- 9.4.2 Probabilistic Evidence -- 9.5 Organizational Change -- 9.5.1 Understanding Organizational Change -- 9.5.2 Managing Organizational Change -- References -- Chapter 10: The Future? -- 10.1 Human-Machine Interweaving -- 10.1.1 Emulation, Simulation and Virtualization -- 10.1.1.1 Singularity? -- 10.1.2 From Doing to Thinking to Being -- 10.2 Transcendent Creativity -- 10.3 Liberal and Digital Arts -- 10.3.1 Managing Organizational Know-How -- 10.3.2 The Evolution of Learning -- 10.4 Up to the Challenge? -- Index. |
| Record Nr. | UNINA-9910491028303321 |
|
Banasiewicz Andrew D.
|
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||