Data-driven Technology Foresight: Text Analysis of Emerging Technologies

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업·조선공학부, 2018. 2. 박용태.This dissertation argues for new directions in the field of technology foresight. Technology foresight was formulated on the basis of qualitative and participatory research. Initially, most foresight activities were triggered by the prospect of a handful number of experts, but recent studies highlight theoretical paradigm shifts toward a more comprehensive and data-driven approach to creating shared insights on the future of emerging technologies. Much of the research up to now, however, has been descriptive in nature, and a definite method of realizing the notion has not yet been addressed in the existing literature to a large extent. To this end, we have attempted to formalize the concept of data-driven technology foresight by incorporating unconventional data sources – future-oriented web data, Wikipedia data, and scientific publication data – and different analytical tools – Latent Semantic Analysis, IdeaGraph, and Morphological Analysis. Four distinct foresight frameworks were proposed for the proactive management process of emerging technologies: impact identification, impact analysis, plan development, and technology ideation. The study was guided by the following research questions: (1) what kinds of data sources are available on the web and which of those are considered useful in foresight studies? (2) Where could we incorporate these data sources and which techniques are most suitable for the given purposes? (3) Which foresight-related fields would particularly benefit from applying a data-driven approach and what are the positive effects? The proposals outlined should be considered exploratory and open-ended. It is designed to determine the nature of the problem, rather than to offer definitive and conclusive answers. Nevertheless, the proposed scheme may well provide not just a rationale but a theoretical grounding for this newly introduced notion. This dissertation is expected to yield a foothold for the readers to better comprehend and act on this new shift in the field of technology foresight.Chapter 1 Introduction 1 1.1 Emergence of Technology Foresight 1 1.2 Towards a Data-driven Technology Foresight 3 1.3 Problem Statement 6 1.4 Dissertation Overview 8 Chapter 2 Data Sources and Methodologies 15 2.1 Data Sources 15 2.1.1 Future-oriented Web Data 15 2.1.2 Wikipedia Data 17 2.1.3 Scientific Publication Data 19 2.2 Methodologies 21 2.2.1 Latent Semantic Analysis (LSA) 21 2.2.2 IdeaGraph 25 2.2.3 Morphological Analysis (MA) 29 Chapter 3 Foresight for Impact Identification 31 3.1 Introduction 32 3.2 Emerging Technology and its Social Impacts 36 3.2.1 Distinctive Nature of Emerging Technology 36 3.2.2 Technology Assessment 39 3.3 LSA for Constructing Scenarios 43 3.4 Research Framework 44 3.4.1 Step 1: Data Collection 46 3.4.2 Step 2: Scenario Development 49 3.4.2.1 Pre-LSA: Preprocessing Future-oriented Web Data 49 3.4.2.2 LSA: Applying Latent Semantic Analysis 52 3.4.2.3 Post-LSA: Constructing Scenarios 54 3.5 Illustrative Case Study: Drone Technology 55 3.6 Discussion 65 3.6.1 Categorization of Social Impacts 65 3.6.2 Comparative Analysis 72 3.6.3 Implication for Theory, Practice, and Policy 74 3.7 Conclusion 76 Chapter 4 Foresight for Impact Analysis 79 4.1 Introduction 80 4.2 Uncertainty and Complexity 82 4.3 Data-driven Foresight Process 84 4.4 Scenario Building Beyond the Obvious 86 4.4.1 Capturing Plausibility using LSA 90 4.4.2 Capturing Creativity using IdeaGraph 92 4.5 Research Framework 93 4.5.1 Step 1. Pre-Analysis: Data Preparation 94 4.5.1.1 Target Technology Selection 94 4.5.1.2 Data Acquisition 95 4.5.1.3 Data Preprocessing 95 4.5.2 Step 2. Text Analysis: Scenario Building 96 4.5.2.1 General Glimpse using Overt Structures 96 4.5.2.2 Hidden Details using Latent Structures 98 4.5.3 Step 3. Post-Analysis: Analytical Interpretation 101 4.5.3.1 Individual Impact Scenario 101 4.5.3.2 Overall Latent Impacts 101 4.6 Illustrative Case Study: 3D Printing Technology 102 4.7 Discussion 110 4.7.1 Scenarios Beyond the Obvious 110 4.7.2 Comparative Analysis 113 4.8 Conclusion 115 Chapter 5 Foresight for Plan Development 117 5.1 Introduction 118 5.2 Theoretical Paradigm Shift 120 5.2.1 Technology-focused vs. Society-focused 120 5.2.2 Co-evolution of Technology and Society 122 5.2.3 Responsible Development 125 5.3 Methodological Paradigm Shift 127 5.3.1 Participatory Approach 127 5.3.2 Data-driven Approach 129 5.4 Rationale for using LSA 131 5.5 Research Framework 132 5.5.1 Step 1. Envisioning Social Issues 133 5.5.1.1 Collection of Future-oriented Web Data 133 5.5.1.2 Construction of Impact Scenarios 135 5.5.1.3 Conceptualization of Impact Scenarios 137 5.5.2 Step 2. Deriving Technical Solutions 138 5.5.2.1 Collection of Scientific Publication Data 138 5.5.2.2 Construction of Solution Concepts 139 5.6 Illustrative Case Study: Autonomous Vehicle 140 5.7 Discussion 149 5.7.1 Comparative Analysis 149 5.7.2 Major Strengths in Envisioning Social Impacts 152 5.7.3 Major Strengths in Overviewing Solutions 154 5.8 Conclusion 156 Chapter 6 Foresight for Technology Ideation 158 6.1 Introduction 159 6.2 Related Studies 161 6.2.1 Generating Creative Ideas 161 6.2.2 Data-driven Morphological Analysis 163 6.3 Technology Foresight using Wikipedia 165 6.3.1 Wikipedia as a Good Remedy 165 6.3.2 Preliminaries: How to Apply Wikipedia 168 6.4 Research Framework 173 6.4.1 Basic Model 174 6.4.2 Extended Model 175 6.4.2.1 Phase 1: Preliminary Phase 177 6.4.2.2 Phase 2: Dimension Development Phase 177 6.4.2.3 Phase 3: Value Development Phase 179 6.4.2.4 Phase 4: Sub-dimension Development Phase 182 6.5 Illustrative Case Study: Drone Technology 183 6.5.1 Basic Model 183 6.5.2 Extended Model 185 6.6 Comparative Analysis 193 6.6.1 Experimental Setup 193 6.6.2 Comparison of Results 195 6.7 Intrinsic Limitations of Applying Wikipedia 199 6.8 Conclusion 201 Chapter 7 Concluding Remarks 203 Bibliography 211 Appendix 236 Appendix A Result of overt and latent structures of each impact scenario 236 Appendix B Result of Wikipedia-based morphological matrix (basic model) 240 Appendix C Result of Wikipedia-based morphological matrix using superordinate seed terms (extended model) 241 Appendix D Result of Wikipedia-based morphological matrix after applying subordinate value seed terms (extended model) 243 Appendix E Result of Wikipedia-based morphological matrix after developing sub-dimensions (extended model) 247Docto

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p