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研究生:楊凱倫
研究生(外文):Kai-Lun Yang
論文名稱:透過資料科學強化設計思考:以台灣搬家產業為例
論文名稱(外文):Enriching Design Thinking with Data Science: Using Taiwan Moving Industry as an Example
指導教授:徐士傑徐士傑引用關係
指導教授(外文):Shih-Chieh Hsu
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:159
中文關鍵詞: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
外文關鍵詞:Design ThinkingBig DataObservationUser-generated Content (UGC)InterviewData ScienceOpen DataSentiment AnalysisConjoint AnalysisClustering AnalysisText Mining
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  • 被引用被引用:2
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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。
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.
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
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