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博碩士論文 etd-0622119-104116 詳細資訊
Title page for etd-0622119-104116
論文名稱
Title
透過資料科學強化設計思考:以台灣搬家產業為例
Enriching Design Thinking with Data Science: Using Taiwan Moving Industry as an Example
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
159
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2019-07-19
繳交日期
Date of Submission
2019-07-22
關鍵字
Keywords
設計思考、巨量資料、資料科學、開放資料、訪談法、使用者原創內容、文本挖掘、集群分析、情緒分析、觀察法、聯合分析
Design Thinking, Big Data, Observation, User-generated Content (UGC), Interview, Data Science, Open Data, Sentiment Analysis, Conjoint Analysis, Clustering Analysis, Text Mining
統計
Statistics
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中文摘要
設計思考 (Design Thinking) 是一種問題解決的質性方法論。 21世紀之初,設計思考在商業及媒體中日益普及,使得大眾對於設計思考的興趣有所提升,進而將其應用於實務問題之解決。然而,部分質疑聲浪認為傳統設計思考具有意義模糊、不易被分析、普遍性 、不可驗證性、不確定性與過於簡單之特性,導致其可能發生調查誤差 (抽樣誤差、訪談員誤差等) 、未全面取得所有母體的想法或洞見之問題,而使設計思考無法從更全面、多元的觀點中找到最佳解決方案。
基於巨量資料 (Big Data) 以及資料科學 (Data Science) 的發展,我們得以透過不同途徑取得更多資料來源 (例如:開放資料、巨量資料、使用者原創內容等) ,試圖從資料當中挖掘出群眾之意見或想法,以回應部分觀點對於設計思考之批評。
本研究設計一套整合傳統設計思考方法 (質性方法) 與資料科學 (量化方法) 之方法論,並以台灣搬家產業為例,示範整合質性方法(例如:觀察法、訪談法、腦力激盪、原型設計等)和量化方法(例如:文本挖掘、集群分析、情緒分析、聯合分析等)之設計思考的實務應用,以得出有量化資料支持、可靠、可驗證之服務設計方法。同時提供整合過程中所可能面臨之問題、注意事項、未來學術研究或產業界執行上之相關建議,以期該方法論得以在不同情境中被使用。
Abstract
Design thinking is a problem-solving qualitative approach. The start of the 21st century brought a significant increase in interest in design thinking, which became popularized in the business press. However, some opinions argue that design thinking is ambiguity, unanalyzability, universalizability, unverifiability, uncertain, and simplicity. These criticisms say that design thinking cannot find out the optimal solution from existing results and even may happen with survey error.
Based on the development of big data and data science in recent years, we are capable of accessing much more data from various channels and data sources, for instance, open data, big data, user-generated content (UGC)). By analyzing data, we proposed it can help us deal with design thinking’s problems.
In this research, we design an enriching design thinking methodology which integrates both qualitative approach (e.g., observation, interview, brainstorming, and prototyping) and quantitative approach (e.g., text mining, clustering analysis, sentiment analysis, and conjoint analysis). Besides, we use Taiwan moving industry as an example to demonstrate how we practice. Furthermore, we provide suggestions and notices while executing this methodology, try to design a reliable and valid design thinking process, which also provide, which can be used in industry and future research.
目次 Table of Contents
論文審定書 i
公開授權書 ii
中文摘要 iii
Abstract iv
Table of Contents v
List of Figures vii
List of Tables viii
Chapter 1 Introduction 1
1.1 Problem statement 3
1.2 Research objectives 4
Chapter 2 Literature Review 6
2.1 Design thinking 6
2.1.1 History and development of design thinking 7
2.1.2 Two different points of view of design thinking 7
2.1.3 The process of conducting design thinking 11
2.1.4 Common methods of design thinking 13
2.1.5 Problems of design thinking 16
2.2 Data science 20
2.2.1 History and development of data science 20
2.2.2 The process of conducting data science 20
2.2.3 Typical methods in data science 28
2.2.4 The value of data science 35
2.3 Design science 36
2.3.1 History and development of design science 36
2.3.2 The concept of conducting design science 37
2.3.3 The value of design science 40
Chapter 3 Research Methods and Design 41
3.1 Research methodology 41
3.2 Research framework 47
3.3 Research subjects 51
3.4 Research procedure 53
Chapter 4 Case Analysis 55
4.1 Process of executing a case study 55
4.2 Data science process 57
4.2.1 Problem understanding (expert meeting) 57
4.2.2 Data processing 61
4.2.3 Evaluation and application 89
4.3 Enriching design thinking 89
4.3.1 Enriching design thinking workshop preparation 90
4.3.2 Enriching design thinking workshop 92
4.4 Evaluation 123
4.4.1 Data collection 125
4.4.2 Data analysis and data visualization 129
4.4.3 First-round selection 132
4.4.4 Second-round selection 133
4.4.5 Simulation 134
Chapter 5 Conclusions and Discussion 135
5.1 Conclusion 135
5.2 Contribution 136
5.3 Limitation 136
Reference 137
Appendix 145
參考文獻 References
Addelman, S. (2012). Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics, 4(1), 21-46.
Aggarwal, C. C., & Zhai, C.-X. (2012). Mining text data. New York, United States: Springer.
Archer, J. W. (1985). A novel quasi-optical frequency multiplier design for millimeter and submillimeter wavelengths. IEEE transactions on microwave theory and techniques, 33(8), 741.
Bader, B. W., Berry, M. W., Browne, M., & Castellanos, M. (2004). Survey of text mining: Clustering, classification, and retrieval (1 ed.). Califorina, United States: Springer.
Badke-Schaub, P., Roozenburg, N., & Cardoso, C. (2010). Design thinking: A Paradigm on its Way from Dilution to Meaninglessness. Paper presented at the 8th Design Thinking Research Symposium, Sydney, Australia.
Brown, T. (2008). Design thinking. Harvard business review, 86(6), 84-92.
Buchanan, R. (1992). Wicked problems in design thinking. Design issues, 8(2), 5-21.
Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of marketing, 46(3), 44-53.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (1999). CRISP-DM 1.0: Step-by-step data mining guide. Retrieved from https://www.the-modeling-agency.com/crisp-dm.pdf
Coles, R., & Norman, E. W. (2005). An exploration of the role values plays in design decision-making. International Journal of Technology and Design Education, 15(2), 155-171.
Collopy, F. (2009). Thinking about design thinking. Retrieved from https://www.fastcompany.com/1306636/thinking-about-design-thinking
Cooper, R., Junginger, S., & Lockwood, T. (2009). Design thinking and design management: A research and practice perspective. Design Management Review, 20(2), 46-55.
Croft, W. B., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice (Vol. 520). New York, United States: Pearson.
Cross, N. (1982). Designerly ways of knowing. Design studies, 17(3), 221-227.
Cross, N. (2011). Design thinking: Understanding how designers think and work. New York, United States: Berg.
Daugherty, T., Eastin, M. S., & Bright, L. (2010). Exploring consumer motivations for creating user-generated content. American Academy of Advertising, 8(2), 16-25.
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management, 37(6), 817-842.
Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of engineering education, 94(1), 103-120.
Eekels, J., & Roozenburg, N. F. (1991). A methodological comparison of the structures of scientific research and engineering design: Their similarities and differences. Design studies, 12(4), 197-203.
Fayyad, U. (1998). Data mining and knowledge discovery in databases: Implications for scientific databases. Paper presented at the 9th International Conference on Scientific and Statistical Database Management, Olympia, United States.
Friendly, M., Denis, D. J., & Truman, H. S. (2001). Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization. Paper presented at the 28th Annual Conference of the Gesellschaft für Klassifikation, Dortmund, Germany.
Fuller, R. B., & McHale, J. (1963). World design science decade, 1965-1975: Five two year phases of a world retooling design proposed to the international union of architects for adoption by world architectural schools. Illinois, United States: Southern Illinois University.
Funk, M. S. (2002). Problem solving skills in young yellow-crowned parakeets. Animal Cognition, 5(3), 167-176. doi:10.1007/s10071-002-0149-4
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2008). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012.
Green, P. E., & Srinivasan, V. (1993). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of marketing, 54(4), 3-19.
Green, P. E., Wind, Y., & Carroll, J. D. (1973). Multiattribute decisions in marketing: A measurement approach (Vol. 973): Dryden Press.
Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (1998). Multivariate data analysis (Vol. 5). New Jersey, United States: Pearson.
Hall, E. T. (1998). Beyond culture (Vol. 5). New York, United States: Anchor Books.
Halvey, M. J., & Keane, M. T. (2007). An Assessment of Tag Presentation Techniques. Paper presented at the 16th International World Wide Web Conference, Banff, Canada.
Hassi, L., & Laakso, M. (2011). Design Thinking in the Management Discourse: Defining the Elements of the Concept. Paper presented at the 18th International Product Development Management Conference, Delft, Netherlands
Hayashi, C., Yajima, K., Bock, H. H., Ohsumi, N., Tanaka, Y., & Baba, Y. (2013). Data science, classification, and related methods: Proceedings of the fifth conference of the international federation of classification societies (ifcs-96), kobe, japan, march 27–30, 1996: Springer.
Hevner, A. R. (2004). Design science research in information systems. Management Information Systems Quarterly, 28(1), 75.
Hevner, A. R. (2004). A three cycle view of design science research. Scandinavian journal of information systems, 19(2), 4.
Hevner, A. R. (2007). A three cycle view of design science research. Scandinavian journal of information systems, 19(2).
Iivari, J. (2007). A paradigmatic analysis of information systems as a design science. Scandinavian journal of information systems, 19(2).
Jen, N. (2017). Design Thinking is Bullshit. Paper presented at the 99U Conference, New York, United States.
Johansson, U., & Woodilla, J. (2010). How to Avoid Throwing the Baby out with the Bath Water: An Ironic Perspective on Design Thinking. Paper presented at the 26th European Group for Organizational Studies Colloquium, Lisbon, Portugal.
Johansson‐Sköldberg, U., Woodilla, J., & Çetinkaya, M. (2013). Design thinking: Past, present and possible futures. Creativity and innovation management, 22(2), 121-146.
Joseph, F., Anderson, R. E., & Tatham, R. L. (1987). Multivariate data analysis with readings: Macmillan Publishing Company.
Jr, N., F, J., Chen, M., & Purdin, T. D. (1990). Systems development in information systems research. Journal of management information systems, 7(3), 89-106.
Kelley, T., & Kelley, D. (2013). Creative confidence: Unleashing the creative potential within us all. New York, United States: Crown Business.
Kimbell, L., & Street, P. E. (2009). Beyond Design Thinking: Design-as-Practice and Designs-in-Practice. Paper presented at the 5th Centre for Research on Socio-Cultural Change Conference, Manchester, England.
Lawson, B. (2005). How designers think (4 ed.). New York, United States: Routledge.
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203-1205.
Leskovec, J., Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets (2 ed.): Cambridge University Press.
March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision support systems, 15(4), 251-266.
Meyer, D., Hornik, K., & Feinerer, I. (2008). Text mining infrastructure in R. Journal of statistical software, 25(5), 1-54.
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., & Euler, T. (2006). Yale: Rapid Prototyping for Complex Data Mining Tasks. Paper presented at the 12th Special Interest Group on Knowledge Discovery and Data Mining Conference, Philadelphia, United States.
Mitra, P., Murthy, C. A., & Pal, S. K. (2002). Unsupervised feature selection using feature similarity. IEEE transactions on pattern analysis and machine intelligence, 24(3), 301-312.
Naur, P. (1966). The science of datalogy. Communications of the Association for Computing Machinery, 9(7), 485.
Naur, P. (1974). Concise survey of computer methods. New York, United States: Petrocelli Books.
Norman, D. (2010). Design thinking: A useful myth. Retrieved from https://www.core77.com/posts/16790/design-thinking-a-useful-myth-16790
Nussbaum, B. (2009). Latest trends in design and innovation--and why the debate over design thinking is moot. Retrieved from https://www.bloomberg.com/news/articles/2009-07-30/latest-trends-in-design-and-innovation-and-why-the-debate-over-design-thinking-is-moot
Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the “IT” in IT research—A call to theorizing the IT artifact. Information systems research, 12(2), 121-134.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3), 45-77.
Provost, F., & Fawcett, T. (2012). Data Science for Business: What you need to know about data mining and data-analytic thinking (1 ed.). California, United States: O'Reilly Media.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Rittel, H. W., & Webber, M. M. (1974). Wicked problems. Man-made Futures, 26(1), 272-280.
Rossi, M., & Sein, M. K. (2003). Design research workshop: A proactive research approach. Institute for Research on Innovation and Science, 26, 9-12.
Rowe, P. G. (1991). Design thinking. Massachusetts, United States: MIT press.
Salton, G., Wong, A., & Yang, C.-S. (1975). A vector space model for automatic indexing. Communications of the Association for Computing Machinery, 18(11), 613-620.
Schön, D. A. (1987). Educating the reflective practitioner: Toward a new design for teaching and learning in the professions. San Francisco, United States: Jossey-Bass.
Sebastiani, F. (2002). Machine learning in automated text categorization. Association for Computing Machinery Computing Surveys, 34(1), 1-47.
Simon, H. A. (1972). Theories of bounded rationality. Decision and organization, 1(1), 161-176.
Simon, H. A. (1996). The sciences of the artificial. London, England: MIT Press.
Steinbach, M., Karypis, G., & Kumar, V. (2000). A Comparison of Document Clustering Techniques Paper presented at the 6th Special Interest Group on Knowledge Discovery and Data Mining Conference, Minnesota , United States.
Stickdorn, M., Hormess, M. E., Lawrence, A., & Schneider, J. (2018). This is service design doing: Applying service design thinking in the real world. California, United States: O'Reilly Media.
Takeda, H., Tomiyama, T., Veerkamp, P., & Yoshikawa, H. (1990). Modeling design process. AI magazine, 11(4), 37.
Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing and Management, 43(5), 1216-1247.
Walls, J. G., Widermeyer, G. R., & El Sawy, O. A. (1992). Assessing information system design theory in perspective: How useful was our 1992 initial rendition? Journal of Information Technology Theory and Application, 6(2), 43-58.
Weiss, S. M., Indurkhya, N., Zhang, T., & Damerau, F. (2010). Text mining: Predictive methods for analyzing unstructured information (1 ed.). New York, United States: Springer Science & Business Media.
Wilkinson, L., & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179-184.
Winter, R. (2009). Interview with Alan R. Hevner on “design science”. Business and Information Systems Engineering, 1(1), 126-129.
Zhang, Y., Jin, R., & Zhou, Z.-H. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1(1-4), 43-52.
Zhao, Y., & Karypis, G. (2005). Topic-Driven Clustering for Document Datasets. Paper presented at the 6th Society for Industrial and Applied Mathematics International Conference on Data Mining, Newport Beach, United States.
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