De som köpt den här boken har ofta också köpt Slow Productivity av Cal Newport (häftad).
Köp båda 2 för 1998 krALAN THOMAS ROPER (retired) was a professor at Rose-Hulman Institute of Technology in Terre Haute, Indiana. He is the past editor of the journal Impact Assessment and Project Appraisal and the past director of the Center for Technology and Policy Studies at Rose-Hulman. SCOTT W. CUNNINGHAM obtained a MSc in public policy from the Georgia Institute of Technology and a DPhil in science, technology, and innovation policy from the University of Sussex. He is currently Assistant Professor of Policy Analysis in the Department of Technology, Policy, and Management at Delft University of Technology. ALAN L. PORTER has led development of "technology opportunity analysis" and mining electronic, bibliographic data sources to generate intelligence on emerging technologies. He holds an MA in psychology and a PhD in engineering psychology, both from UCLA. He is currently Director of Research and Development for Search Technology, Inc., in Norcross, Georgia. THOMAS W. MASON was the founding head of the Engineering Management Department (www.rose-hulman.edu/msem). While on a three-year leave from Rose-Hulman, he served as CFO and CEO of a 140-person network management systems business. FREDERICK A. ROSSINI (retired) is a former provost at George Mason University in Fairfax, Virginia. JERRY BANKS is Professor Emeritus, Department of Industrial and Systems Engineering, Georgia Institute of Technology in Atlanta, Georgia.
Acknowledgments xv 1 Introduction 1 1.1 About This Book 1 1.2 Technology and Society 2 1.2.1 Social Change 3 1.2.2 Technological Change 4 1.3 Management and the Future 6 1.3.1 Management and Innovation Processes 7 1.3.2 The Role of Technology Forecasting 9 1.3.3 The Importance of Technology Forecasting 10 1.3.4 The Role of Social Forecasting 12 1.4 Conclusions 13 References 13 2 Technology Forecasting 15 2.1 What Is Technology Forecasting? 15 2.1.1 Models of Technology Growth and Diffusion 17 2.1.2 Technology Forecasting in Context 18 2.1.3 What Makes a Forecast Good? 20 2.1.4 Common Errors in Forecasting Technology 21 2.2 Methodological Foundations 23 2.2.1 The Technology Delivery System 24 2.2.2 Inquiring Systems 28 2.3 Technology Forecasting Methods 31 2.3.1 Overview of the Most Frequently Used Forecasting Methods 33 2.3.2 Method Selection 37 2.4 Conclusion 37 References 38 3 Managing the Forecasting Project 40 3.1 Information Needs of the Forecasting Project 40 3.1.1 The Technology Managers Needs 42 3.1.2 The Forecast Managers Needs 43 3.1.3 Information about Team Members 44 3.2 Planning the Technology Forecast 46 3.3 Team Organization, Management, and Communications 47 3.3.1 Organizing and Managing the Technology Forecast 50 3.3.2 Communications 54 3.3.3 Summary Conclusions about Project Management and Organization 55 3.4 Success: The Right Information at the Right Time 56 3.5 Project Scheduling 57 3.5.1 Program Evaluation and Review Technique (PERT) 58 3.5.2 Gantt Chart 60 3.5.3 Project Accountability Chart (PAC) 60 3.5.4 Project Scheduling Software 61 3.6 Conclusions 62 References 62 4 Exploring 65 4.1 Establishing the Contextthe TDS 65 4.1.1 Societal and Institutional Contexts 66 4.1.2 Technology Context 67 4.1.3 Stakeholders 68 4.1.4 Understanding the TDS 69 4.1.5 An Example TDS Model 70 4.2 Monitoring 72 4.2.1 Why Monitor? 74 4.2.2 Who Should Monitor? 75 4.2.3 Monitoring Strategy 76 4.2.4 Monitoring Focused on Management of Technology Issues 79 4.2.5 Monitoring Focused on the Stage of the Technology Development 81 4.3 The Stimulation of Creativity 81 4.3.1 Five Elements of Creativity 81 4.3.2 Group Creativity 92 4.4 Conclusion 95 References 95 5 Gathering and Using Information 98 5.1 Expert Opinion 99 5.1.1 Selecting Experts 99 5.1.2 Selecting Expert Opinion Techniques 100 5.2 Gathering Information on the Internet 105 5.2.1 Science and Technology on the Internet 106 5.2.2 Society and Culture on the Internet 109 5.3 Structuring the Search 113 5.4 Preparing Search Results 116 5.5 Using Search Results 117 5.6 Developing Science, Technology, and Social Indicators 119 5.6.1 Science and Technology Indicators 119 5.6.2 Social Indicators 122 5.7 Communicating Search Results 122 5.8 Conclusions 123 References 124 6 Analyzing Phase 129 6.1 Perspective on Data and Methods 129 6.1.1 Overview and Caveats 130 6.1.2 Internet Time Series Data and Trends 132 6.1.3 Analytical Modeling 133 6.2 Linear Regression and Extensions 134 6.3 Growth Models 138 6.3.1 The Models 138 6.3.2 Dealing with the Data 143 6.3.3 Regression and Growth Modeling: What Can Go Wrong? 144 6.4 Simulation 145 6.4.1 Quantitative Cross-Impact Analysis 146 6.4.2 Qualitative Cross-Impact Analysis 152 6.5 Monte Carlo Simulation 153 6.5.1 Generating and Displaying Random Values 153 6.5.2 Sampling Multiple Random Variables 154 6.5.3 RFID Application in a Hospital Decision 156 6.6 System Dynamics 158 6.6.1 The System Dynamics Modeling Cycle 159 6.6.2 A Technology Forecasting Example: The Cable-to-the-Curb Model 162 6.7 Gaming 164 6.7.1 Decision Trees 165 6.7.2 Bayesian Estimation 166 6.7.3 Value of Information 167 6.7.4 Real Options Analysis 169 6.8 Software Suggestions 170 6.8.1 Software for Regression 170 6.8.2 Simulation Analysis Software 170 6.8.3