Forecasting and Management of Technology

ALAN 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 xv1 Introduction 11.1 About This Book 11.2 Technology and Society 21.2.1 Social Change 31.2.2 Technological Change 41.3 Management and the Future 61.3.1 Management and Innovation Processes 71.3.2 The Role of Technology Forecasting 91.3.3 The Importance of Technology Forecasting 101.3.4 The Role of Social Forecasting 121.4 Conclusions 13References 132 Technology Forecasting 152.1 What Is Technology Forecasting? 152.1.1 Models of Technology Growth and Diffusion 172.1.2 Technology Forecasting in Context 182.1.3 What Makes a Forecast Good? 202.1.4 Common Errors in Forecasting Technology 212.2 Methodological Foundations 232.2.1 The Technology Delivery System 242.2.2 Inquiring Systems 282.3 Technology Forecasting Methods 312.3.1 Overview of the Most Frequently Used Forecasting Methods 332.3.2 Method Selection 372.4 Conclusion 37References 383 Managing the Forecasting Project 403.1 Information Needs of the Forecasting Project 403.1.1 The Technology Manager’s Needs 423.1.2 The Forecast Manager’s Needs 433.1.3 Information about Team Members 443.2 Planning the Technology Forecast 463.3 Team Organization, Management, and Communications 473.3.1 Organizing and Managing the Technology Forecast 503.3.2 Communications 543.3.3 Summary Conclusions about Project Management and Organization 553.4 Success: The Right Information at the Right Time 563.5 Project Scheduling 573.5.1 Program Evaluation and Review Technique (PERT) 583.5.2 Gantt Chart 603.5.3 Project Accountability Chart (PAC) 603.5.4 Project Scheduling Software 613.6 Conclusions 62References 624 Exploring 654.1 Establishing the Context—the TDS 654.1.1 Societal and Institutional Contexts 664.1.2 Technology Context 674.1.3 Stakeholders 684.1.4 Understanding the TDS 694.1.5 An Example TDS Model 704.2 Monitoring 724.2.1 Why Monitor? 744.2.2 Who Should Monitor? 754.2.3 Monitoring Strategy 764.2.4 Monitoring Focused on Management of Technology Issues 794.2.5 Monitoring Focused on the Stage of the Technology Development 814.3 The Stimulation of Creativity 814.3.1 Five Elements of Creativity 814.3.2 Group Creativity 924.4 Conclusion 95References 955 Gathering and Using Information 985.1 Expert Opinion 995.1.1 Selecting Experts 995.1.2 Selecting Expert Opinion Techniques 1005.2 Gathering Information on the Internet 1055.2.1 Science and Technology on the Internet 1065.2.2 Society and Culture on the Internet 1095.3 Structuring the Search 1135.4 Preparing Search Results 1165.5 Using Search Results 1175.6 Developing Science, Technology, and Social Indicators 1195.6.1 Science and Technology Indicators 1195.6.2 Social Indicators 1225.7 Communicating Search Results 1225.8 Conclusions 123References 1246 Analyzing Phase 1296.1 Perspective on Data and Methods 1296.1.1 Overview and Caveats 1306.1.2 Internet Time Series Data and Trends 1326.1.3 Analytical Modeling 1336.2 Linear Regression and Extensions 1346.3 Growth Models 1386.3.1 The Models 1386.3.2 Dealing with the Data 1436.3.3 Regression and Growth Modeling: What Can Go Wrong? 1446.4 Simulation 1456.4.1 Quantitative Cross-Impact Analysis 1466.4.2 Qualitative Cross-Impact Analysis 1526.5 Monte Carlo Simulation 1536.5.1 Generating and Displaying Random Values 1536.5.2 Sampling Multiple Random Variables 1546.5.3 RFID Application in a Hospital Decision 1566.6 System Dynamics 1586.6.1 The System Dynamics Modeling Cycle 1596.6.2 A Technology Forecasting Example: The Cable-to-the-Curb Model 1626.7 Gaming 1646.7.1 Decision Trees 1656.7.2 Bayesian Estimation 1666.7.3 Value of Information 1676.7.4 Real Options Analysis 1696.8 Software Suggestions 1706.8.1 Software for Regression 1706.8.2 Simulation Analysis Software 1706.8.3 Software for Analysis of Decisions 1706.8.4 Real Options Super Lattice Software 1706.8.5 Software Sites 171References 1717 Focusing Phase: Using Scenario Analysis 1747.1 Uncertainty 1757.1.1 Uncertainty Frameworks 1757.1.2 Source and Nature of Uncertainty 1767.1.3 Uncertainty and the Adaptive Paradigm 1777.1.4 Techniques for Addressing Uncertainty 1777.2 Scenarios 1787.2.1 Steps in Creating Scenarios 1787.2.2 Types of Scenarios 1827.3 Examples and Applications 1847.3.1 Scenarios for Renewable Energy Planning 1847.3.2 Pervasive Computing Scenarios 1857.3.3 Scenarios for Social Change 1867.4 Scenarios: Extensions and Advanced Techniques 1877.4.1 Scenarios in Multimethodology Forecasts 1877.4.2 Extensions of Scenario Analysis 1897.5 Conclusions 191References 1928 Economic and Market Analysis 1948.1 The Context 1948.1.1 Markets and Innovation 1978.1.2 Technology and Institutions 1998.2 Forecasting the Market 2038.2.1 The Consumer/Customer Marketplace 2048.2.2 Qualitative Techniques for Appraising Market Potential 2068.2.3 A Quantitative Approach—Adoption and Substitution: S-Curve Models 2078.3 Forecasting the Economic Context 2088.3.1 Macroeconomic Forecasting 2098.3.2 Input-Output Analysis 2108.3.3 General Equilibrium Models 2148.3.4 Hedonic Technometrics 2158.4 Forecasting in an Institutional Context 2168.4.1 Institutional Arrangements and the Market 2168.4.2 Game Theory 2188.4.3 Agent-Based Models 2198.5 Conclusion 219References 2209 Impact Assessment 2239.1 Impact Assessment in Technology Forecasting 2239.2 Impacts on Technology and Impacts of Technology 2249.3 A Comprehensive Approach to Impact Assessment 2259.4 Impact Identification 2269.4.1 Scanning Techniques 2269.4.2 Tracing Techniques 2279.4.3 Narrowing the Impact Set and Estimating Effects 2299.4.4 A Final Word 2299.5 Impact Analysis 2309.5.1 Analyzing Impacts on and Impacts of the Technology 2309.5.2 Analyzing Technological Impacts 2329.5.3 Analyzing Economic Impacts 2349.5.4 Analyzing Environmental Impacts 2349.5.5 Analyzing Social Impacts 2389.5.6 Analyzing Institutional Impacts 2399.5.7 Analyzing Political Impacts 2409.5.8 Analyzing Legal and Regulatory Impacts 2419.5.9 Analyzing Behavioral, Cultural, and Values Impacts 2429.5.10 Analyzing Health-Related Impacts 2439.6 Impact Evaluation 2449.7 Conclusion 245References 24510 Cost-Benefit and Risk Analysis 24810.1 Opportunity Costs and Choices 24810.2 Cost-Benefit Analysis 24910.2.1 Cost-Benefit Analysis within the Organization 24910.2.2 Societal Stake and the Organizational Response 25310.2.3 Cost-Benefit Analysis Methods 26010.2.4 Economic Value Added 26310.2.5 Earned Value Management 26410.2.6 The Balanced Scorecard 26510.3 Accounting for Risk and Uncertainty 26510.3.1 Accounting for Risk within Organizations 26510.3.2 Accounting for Risk—the Social Dimension 26910.4 Concluding the Focusing Phase 273References 27411 Implementing the Technology 27711.1 Forecasting Continues 27711.2 Implementation Issues 27811.3 Strategic Planning for Technology Implementation 27811.4 Selecting from among Alternative Implementations of the Technology 27911.4.1 Measurement 28211.4.2 Interpretive Structural Modeling 28411.4.3 Analytic Hierarchy Process 28511.4.4 Wrap-Up 28611.5 Technology Roadmapping 28611.6 Summary and Concluding Observations 287References 28712 Managing the Present from the Future 28912.1 The Overall Approach 28912.2 Selecting Methods and Techniques 29012.2.1 Using the TDS and the Major Families of Techniques 29012.2.2 The 80–20 Rule 29112.3 Alternative Perspectives 29112.4 Learning from Past Forecasts and Assessments 29312.5 Visions 29512.6 A Final Word 295References 296Appendix A Case Study on Forecasting Dye-Sensitized Solar Cells 297A.1 Framing the Case Study 297A.1.1 Characterizing the Technology 298A.1.2 Dye-Sensitized Solar Cells 299A.2 Methods 299A.2.1 Engaging Experts and Multipath Mapping 299A.2.2 Developing the TDS 300A.2.3 Tech Mining (Chapter 5) and Science Overlay Mapping 304A.2.4 Trend Analyses 310A.2.5 Cross-charting and Social Network Analyses 311A.3 The Rest of the Story 313A.3.1 Market Forecasts 314A.3.2 Scenarios 315A.3.3 Technology Assessment 315A.3.4 Further Analyses and Communicating Results 316References 316Index 319