過往消費者對於一樣產品的要求大多是希望實用且好用，亦即能夠滿足理性上的需求，但是隨著經濟的發展以及生活品質的上升，人們開始追求感性上的滿足，產品的開發也就必須同時符合消費者感性的期望。因此，「了解消費者如何看待一樣產品並根據這些感性反應設計產品」便成為了現代的設計者急需解決的課題，感性工學也就應運而生。
感性工學為了將感性與設計連結，提出了一系列的資料蒐集流程以及分析方法，通常會歷經五個步驟：（1）選定目標產品；（2）製作語意區辨量表；（3）定義設計參數；（4）執行互動實驗；（5）分析與推論。雖然感性工學的研究大多依照這五個步驟執行，但是由於每個步驟中可以執行的方式並非只有一種，各自的使用時機也未盡明確，導致感性工學的研究產生了一些瓶頸與限制。為了解決這些問題，本研究透過文獻的蒐集與探討，發現在「定義設計參數」的步驟中尚沒有一套公認的標準方法，且每個方法都有一定的限制，如果定義出的設計參數因此而不具代表性或是過多，將可能造成互動實驗的有效性不足或效率不彰。因此，本研究以頭戴式耳機為標的產品，期望透過品質管理的方法預先篩選設計參數，以協助感性工學的互動實驗能夠有效地直接鎖定對於使用者的感性期望較有貢獻性的設計參數進行更詳細的探討。
本研究將透過感性工學、品質機能展開以及狩野模式等三種手法分別決定影響頭戴式耳機舒適度的最佳設計參數組合及重要性排序，再予以比較成效。在感性工學的部分，一開始會從社群網站、官方網站等管道蒐集與頭戴式耳機舒適度有關的形容詞彙，並透過使用者以及專家觀點篩選，製作成雙極的語意區辨量表；設計參數的選定則透過與頭戴式耳機設計專家討論、決定五種與舒適度有關的設計參數，最後透過10副耳機的互動實驗與數據分析得出這五種設計參數對舒適度的影響與排序。在品質機能展開的部分，本研究將以感性工學方法所蒐集而來的形容詞彙當作品質機能展開中的顧客聲音，並邀請專家評量前述之五種設計參數與顧客聲音的關係，最後計算出各設計參數的加權分數以及重要程度排序。狩野模式則將透過網路問卷調查頭戴式耳機使用者對前述之五種設計參數的存在意義進行評價，進而歸類為魅力型、一維型、必須型、無差異型、或反向型之品質特性，然後轉換為量化的顧客滿意係數與不滿意係數、予以排序。最後分別比較品質機能展開、狩野模式等兩個替代方法與感性工學的排序結果，根據吻合程度發現狩野模式的不滿意係數較有助於加速感性工學的定義設計參數步驟並維持分析的正確性。
此外，本研究根據較佳的替代方法─即狩野模式的不滿意係數─篩選出較為少量的設計參數，並挑出對應的6副頭戴式耳機產品樣本、再次執行感性工學之互動實驗，以驗證篩選過後的分析結果與原始分析結果的一致程度；結果發現，分析所得最具貢獻的設計參數與原始的分析結果相近，即確認「使用狩野模式的不滿意係數篩選設計參數是一套可以接受的替代方法」，且狩野模式的不滿意係數可以節省36.7%的時間成本以及39.3%的金錢成本，對於產品成本較高、互動時間較長的感性工學研究尤其有幫助。

In the past, customers’ demands on products were utility and usability which can fulfill rational requirements. However, along with the improvement of economics and quality of life, people starts to pursue emotional satisfaction on products. That is to say, product development must fulfill rational and emotional expectation of customers at the same time. Therefore, how to understand the emotional needs of customers and develop products based on them become crucial lessons for modern designers. For this reason, kansei engineering emerged to meet this requirement.
In kansei engineering, a series of data collection processes and analysis methods were proposed in order to link users’ emotion with design parameters. There are five steps in kansei engineering: (1) choosing the target product, (2) making the semantic differential scale, (3) defining design parameters, (4) carrying out interaction experiment, and (5) analyzing and inferring the results. Most kansei engineering researchers followed these five steps, but there are still some limitations and bottlenecks. In each step, there could be multiple alternatives to choose from, but there is no clear guideline about the appropriate option under a specific condition. Through literature reviews, this study found out that there is no standard method in “defining design parameters,” and each method has its own limitation. If the design parameters are hence not representative or excessive, it will reduce the effectiveness and efficiency of the experiment. Therefore, taking headsets as an example, this study used quality management methods to help filter design parameters in advance. In this way, it will be allowed to explore deeply on the more important parameters and hence make the kansei evaluation more effective.
In this study, kansei engineering, quality function deployment (QFD), and Kano model were adopted to determine the ranking of design parameters according to how each influences the users’ wearing comfort of headsets. By comparing the ranking results of these three methods, the better alternative for kansei engineering can be identified. In kansei engineering, adjectives related to wearing comfort were first collected from social media and official websites of manufactures. After being filtered from views of users and experts, the semantic differential scales were developed. Five design parameters related to wearing comfort were determined through discussion with experts of headset design. At last, optimal combinations and importance ranking of design parameters are inferred based on the results of interaction experiment with 10 headsets. In QFD, the adjectives collected in kansei engineering were considered as voice of customer (VOC), while experts were invited to rate the relationship between VOC and design parameters. By calculating the weighted score of each design parameter, the importance ranking was defined. As for Kano model, design parameters were evaluated through asking about users' feelings in terms of the extent of fulfilment and non-fulfilment of a specific design parameter. Each design parameter was then categorized as an attractive (A), one-dimensional (O), must-be (M), indifference (I) or reverse (R) quality. The percentages of these five categories can be further calculated as customers’ coefficients of satisfaction and dissatisfaction to get the importance ranking of design parameters. Finally, by comparing the results of kansei engineering with the result of QFD and the result of Kano model, the coefficient of dissatisfaction in Kano model was found to be able to better help filter design parameters efficiently, as well as ensuring the accuracy of data analysis.
In addition, in order to validate the effectiveness of the filtering process, the 3 design parameters with higher importance according to the coefficient of dissatisfaction in Kano model were considered to run a validation experiment with the corresponding 6 headset samples. Results showed that ranking of the three design parameters is similar with that obtained in the original experiment. It is hence inferred that the use of coefficient of dissatisfaction in Kano model is an acceptable method for filtering design parameters. Moreover, it helped save 36.7% of time and 39.3% of budget for the kansei evaluation. More specifically, it will be more helpful for kansei evaluation with a higher cost of product collection and longer duration of product interaction.

一、緒論 1
1.1 研究背景與動機 1
1.2 研究目的與範圍 4
1.3 研究架構與流程 5
二、文獻探討 9
2.1 感性工學 9
2.1.1 製作語意區辨量表 11
2.1.2 定義設計參數 13
2.1.3 執行互動實驗並記錄使用者的反應 14
2.1.4 評估結果分析與推論 16
2.1.5 小結 20
2.2 品質機能展開與狩野模式 21
2.2.1 品質機能展開 21
2.2.2 狩野模式 23
2.2.3 品質機能展開與狩野模式在感性工學上的應用 27
2.2.4 小結 27
2.3 目標產品的選擇 28
2.4 小結 29
三、研究方法 30
3.1 研究流程設計 30
3.2 實驗準備 32
3.2.1 定義設計參數 32
3.2.2 感性詞彙語意區辨量表 35
3.2.3 品質機能展開量表 42
3.2.4 狩野模式正反向問題問卷 46
3.3 決定設計參數的重要性：傳統感性工學實驗 48
3.3.1 研究參與者 48
3.3.2 資料蒐集 49
3.3.3 資料分析 51
3.4 決定設計參數的重要性：品質機能展開 52
3.4.1 研究參與者 52
3.4.2 資料蒐集 53
3.4.3 資料分析 54
3.5 決定設計參數的重要性：狩野模式 54
3.5.1 研究參與者 55
3.5.2 資料蒐集 55
3.5.3 資料分析 56
3.6 結果比較 57
3.7 有效性驗證實驗 59
四、研究結果 60
4.1 感性工學分析結果 60
4.1.1 實驗參與者基本資料 60
4.1.2 相關分析 60
4.1.3 主成分分析 61
4.1.4 數量化理論一類 63
4.2 品質機能展開分析結果 66
4.3 狩野模式分析結果 69
4.4 設計參數的重要性排序結果比較與驗證實驗之產品樣本挑選 71
4.4.1 各方法之結果比較 71
4.4.2 驗證實驗之樣本挑選 72
4.5 驗證實驗分析結果 74
4.5.1 實驗參與者基本資料 75
4.5.2 相關分析 75
4.5.3 主成分分析 76
4.5.4 數量化理論一類 77
4.5.5 原始感性工學與驗證實驗之結果比較 79
4.5.6 效益分析 80
五、討論 83
5.1 驗證實驗與原始感性工學實驗之差異 83
5.2 以狩野模式的不滿意係數篩選設計參數之可行性 85
5.3 狩野模式與品質機能展開結果之差異 90
5.4 設計參數對於舒適度、主要意象的重要性排序 92
5.4.1 形容詞彙相關性 92
5.4.2 主成分分析 93
5.4.3 數量化理論一類 94
5.4.4 夾持力重要性排序 96
六、結論 97
6.1 主要發現 97
6.1.1 狩野模式為較能幫助初步篩選設計參數的方法 97
6.1.2 使用狩野模式的滿意係數或不滿意係數之時機 98
6.1.3 感性工學研究之新實施流程 99
6.2 研究貢獻與應用 101
6.3 研究限制與未來方向 101
參考文獻 104
附錄一、 相似感性詞彙篩選問卷 110
附錄二、 設計參數與感性詞彙之相關程度量表 113
附錄三、 國立清華大學研究倫理審查委員會審查核可證明書 117

中文部分：
1. 王茂駿、林昱呈、王明揚（2002）臺灣地區人體計測資料庫手冊。新竹市：中華民國人因工程學會。
2. 王振琤、林銘泉、陳子昌（2009）應用品質機能展開於戶外休閒服飾設計程序之發展。設計學報，10(2)，頁29-42。
3. 台灣網路消費者對「耳機/耳麥」購買行為與通路品牌分析。紅門互動。2015年10月，取自：https://www.smartm.com.tw/article/31363737cea3
4. 台灣縣市人口按性別及五齡組。中華民國內政部戶政司。2018年12月，取自：https://www.ris.gov.tw/app/portal/346
5. 吳貴彬、陳相如（2004）品質機能展開法於新產品發展之應用. （未出版之碩士論文），私立樹德科技大學工業管理系，高雄市。
6. 李春長、林雨築（2006）公部門服務品質之研究-以品質機能展開技術應用於地政事務所之分析。住房經濟學雜誌，15(1)，頁43-58。
7. 施韋名、莊明振（1995）眼鏡造形與感覺意象對應關係之研究 （博士論文，國立交通大學，應用藝術研究所）。取自臺灣博碩士論文系統。（編號：084NCTU3509004）。
8. 莊明振、陳俊智（2009）產品形態特徵與構成關係影響消費者感性評價之研究─ 以水壺的設計為例。設計學報，9(3)，頁43-58。
9. 陳俊智、李依潔（2009）應用 KANO 品質模式探討文化產品設計之魅力因子。設計學報，13(4)，頁25-41。
10. 黃延聰（譯）（2011）新產品管理（原作者：Crawford, C. M.）。臺北市：華泰文化。（原著出版年：2008）。
11. 董維、張瑞觀、梁榮達（2009）由周邊路徑效果探討特定網路廣告態度形成：人機互動, 情緒及一般網路廣告態。電子商務學報，11(1)，頁143-172。
12. 管倖生、林彥呈（2009）以感性工學程序建構網頁設計系統之研究。設計學報，7(1)，頁59-74。
13. 鄧維兆、李友錚（2006）台北市立美術館關鍵觀眾服務品質屬性之確認：Kano 模式之應用。博物館學季刊，20(4)，頁27-45。
外文部分：
1. Akao, Y. (1990). Quality function deployment: integrating customer requirements into product design. Productivity press.
2. Bahn, S., Lee, C., Nam, C.S., & Yun, M.H. (2009). Incorporating affective customer needs for luxuriousness into product design attributes. Human Factors and Ergonomics in Manufacturing & Service Industries, 19(2): 105-127.
3. Berger, C. (1993). Kano's methods for understanding customer-defined quality. Center for Quality Management Journal, 2(4): 3-36.
4. Bidin, S.A.H. & Lokman, A.M. (2018, March). Enriching the Comfortability Emotion on Website Interface Design Using Kansei Engineering Approach. In International Conference on Kansei Engineering & Emotion Research (pp. 792-800). Springer, Singapore.
5. Bongard-Blanchy, K., Bouchard, C., & Aoussat, A. (2013). Limits of Kansei-Kansei unlimited. International Journal of Affective Engineering, 12(2): 145-153.
6. Bouchereau, V. & Rowlands, H. (2000). Methods and techniques to help quality function deployment (QFD). Benchmarking: An International Journal, 7(1): 8-20.
7. Chen, H.Y., Tu, J.C., & Wang, K.C. (2017). Research into the Application of Kansei Engineering to the Modeling Aesthetic Design of a Numerical Control Machine Tool. International Journal of Affective Engineering, 16(3): 191-202.
8. Chen, M.C., Hsu, C.L., Chang, K.C., & Chou, M.C. (2015). Applying Kansei engineering to design logistics services-A case of home delivery service. International Journal of Industrial Ergonomics, 48: 46-59.
9. Clegg, B., Wang, T., & Ji, P. (2010). Understanding customer needs through quantitative analysis of Kano's model. International Journal of Quality & Reliability Management, 7: 173-184.
10. Desmet, P. (2003). Measuring emotion: Development and application of an instrument to measure emotional responses to products. In Funology (pp. 111-123). Springer, Dordrecht.
11. Desmet, P.M.A. (2010). Three levels of product emotion. In Proceedings of the international conference on Kansei engineering and emotion research (pp. 236-246).
12. Dikmen, I., Birgonul, M.T., & Kiziltas, S. (2005). Strategic use of quality function deployment (QFD) in the construction industry. Building and environment, 40(2): 245-255.
13. Ekman, P. & Friesen, W.V. (1978). Facial action coding systems. Consulting Psychologists Press.
14. Fukushima, K., Kawata, H., Fujiwara, Y., & Genno, H. (1995). Human sensory perception oriented image processing in a color copy system. International Journal of Industrial Ergonomics, 15(1): 63-74.
15. Glenday, C. (2000). Guinness world records. Guinness World Records.
16. Govers, C.P. (1996). What and how about quality function deployment (QFD). International journal of production economics, 46: 575-585.
17. Hartono, M. & Chuan, T.K. (2011). How the Kano model contributes to Kansei engineering in services. Ergonomics, 54(11): 987-1004.
18. Hartono, M. (2011). A proposed integrative framework of Kansei Engineering and Kano model applied to services. In Proceedings of The 2nd International Research Symposium in Service Management-Service Imperatives in the New Economy (pp. 484-492). Universitas Pembangunan Nasional" Veteran" Yogyakarta, Indonesia.
19. Park, C.H., Jun, Y.M., Kang, T.J., & Kim, J.H. (2007). Development of a tool to measure the pressure comfort of a cap (II)—by the analysis of correlation between objective pressure and subjective wearing sensation. Textile Research Journal, 77(7): 520-527.
20. Holbrook, M.B. & Hirschman, E.C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2): 132-140.
21. Hsu, Y.L., Huang, C.C., Yo, C.Y., Chen, C.J., & Lien, C.M. (2004). Comfort evaluation of hearing protection. International Journal of Industrial Ergonomics, 33(6): 543-551.
22. Hughes, G.D. (1969). Some confounding effects of forced-choice scales: SAGE Publications Sage CA: Los Angeles, CA.
23. Izard, C.E. (1979). The Maximally Discriminitive Facial Movements Coding System, MAX. University of Delaware.
24. Kaiser, S. & Wehrle, T. (2001). Facial expressions as indicators of appraisal processes. Appraisal processes in emotion: Theory, methods, research: 285-300.
25. Kammerlind, P. & Schütte, S. (2001). Integrating Kansei engineering and QFD in product development. In Proceedings of Q-MOD Conference, Linköping.
26. Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-be quality (in Japanese). Journal of Japanese Society for Quality Control , 14(2): 147-156.
27. Kittidecha, C. Marasinghe, A. C., & Koichi, Y. (2016). Application of Kansei engineering to tactile sense in the Thai food wrapping materials. Journal of Applied Packaging Research, 8(2): 2-8.
28. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology. 22(140): 5-54.
29. Lin, H., Luo, S., Ying, F., Shan, P., Zou, W., Yi, H., & Lin, L. (2019). A Study on the Perception of Wireless Headphone Form Design Based on Kansei Engineering. In International Conference on Applied Human Factors and Ergonomics (pp. 369-380). Springer, Cham.
30. Lin, J. (2009). Design Optimization of Headband for Headphone (Unpublished master’s thesis). Washington State University, United States.
31. Lin, Y.-C., Yeh, C.-H., & Wei, C.-C. (2013). How will the use of graphics affect visual aesthetics? A user-centered approach for web page design. International Journal of Human-Computer Studies, 71(3): 217-227.
32. Llinares, C. & Page, A.F. (2011). Kano’s model in Kansei Engineering to evaluate subjective real estate consumer preferences. International Journal of Industrial Ergonomics, 41(3): 233-246.
33. Lu, J.-M., Tsai, Y.-H., & Liang, Y.-L. (2019). The Effect of Time Passage on Kansei Evaluation. In International Symposium on Affective Science and Engineering ISASE2019 (pp. 1-4). Japan Society of Kansei Engineering.
34. Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
35. Matzler, K. & Hinterhuber, H.H. (1998). How to make product development projects more successful by integrating Kano's model of customer satisfaction into quality function deployment. Technovation, 18(1):25-38.
36. Miyazaki, K. (1993). A modeling of design recognition in Kansei Engineering. Japanese Journal of Ergonomics, 29: 196-197.
37. Mizuno, S. & Akao, Y. (1978). Quality function deployment: a company wide quality approach. JUSE Press, Tokyo.
38. Nagamachi, M. & Lokman, A.M. (2016). Innovations of Kansei engineering. CRC Press.
39. Nagamachi, M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1): 3-11.
40. Nagamachi, M. (2008). Perspectives and the new trend of Kansei/affective engineering. The Total Quality Management Journal, 20(4): 290-298.
41. Nagamachi, M. (2011). Kansei/affective engineering and history of Kansei/affective engineering in the world. Kansei/affective Engineering, 13: 1-12.
42. Osgood, C.E., Suci, G.J., & Tannenbaum, P.H. (1957). The measurement of meaning (No. 47). University of Illinois Press.
43. Pearson, K. (1896). VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London. Series A, containing papers of a mathematical or physical character, 187: 253-318.
44. Pouliot, F. (1993). Theoretical issues of Kano’s methods. Center for Quality of Management Journal, 2(4): 28-36.
45. Schütte, S. & Eklund, J. (2005). Design of rocker switches for work-vehicles—an application of Kansei Engineering. Applied Ergonomics, 36(5): 557-567.
46. Schvaneveldt, S. J. Enkawa, T., & Miyakawa, M. (1991). Consumer evaluation perspectives of service quality: evaluation factors and two-way model of quality. Total Quality Management, 2(2): 149-162.
47. Shahin, A., Pourhamidi, M., Antony, J., & Park, S.H. (2013). Typology of Kano models: a critical review of literature and proposition of a revised model. International Journal of Quality & Reliability Management, 30(3): 341-358.
48. Sharma, S. & Sharma, S. (1996). Applied multivariate techniques. John Wiley & Sons Inc.
49. Shergian, A. & Immawan, T. (2015). Design of innovative alarm clock made from bamboo with kansei engineering approach. Agriculture and Agricultural Science Procedia, 3: 184-188.
50. Shitara, M., Yoshida, H., Kamijo, M., Fujimaki, G., & Yamaguchi, H. (2017). Investigation into Hand Movements to Assess Material Properties of Wood. International Journal of Affective Engineering, 16(3): 173-182.
51. Spearman, C. (1904). "General Intelligence," objectively determined and measured. The American Journal of Psychology, 15(2): 201-292.
52. Suits, D.B. (1957). Use of dummy variables in regression equations. Journal of the American Statistical Association, 52(280): 548-551.
53. Swan, J.E. & Combs, L.J. (1976). Product Performance and Consumer Satisfaction: A New Concept: An Empirical Study Examines the Influence of Physical and Psychological Dimensions of Product Performance on Consumer Satisfaction. Journal of Marketing, 40(2): 25-33.
54. Tama, I.P., Azlia, W., & Hardiningtyas, D. (2015). Development of customer oriented product design using Kansei engineering and Kano model: Case study of ceramic souvenir. Procedia Manufacturing, 4: 328-335.
55. Vieira, J., Osório, J.M. A., Mouta, S., Delgado, P., Portinha, A., Meireles, J.F., & Santos, J.A. (2017). Kansei engineering as a tool for the design of in-vehicle rubber keypads. Applied Ergonomics, 61: 1-11.
56. Wang, H.H. (2015). A product planning of e-sports headphone by blending replication zmet with QFD. In DS 80-5 Proceedings of the 20th International Conference on Engineering Design (ICED 15) Vol 5: Design Methods and Tools-Part 1, Milan, Italy, 27-30.07. 15 (pp. 075-082).
57. Wang, T.X., Huang, W.M., Aw, J.E., He, L.W., & Vettorello, M. (2016). Comfort fitting using shape memory polymeric foam. Journal of Testing and Evaluation, 45(4): 1201-1212.
58. Ward Jr., J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301): 236-244.
59. Yamamoto, K.I. (1992). Japans Automotive Industry: It's Strength, Special Lectures by Top Management, Faculty of Business Administration, Asia University, Tokyo.
60. Yan, H.B., Huynh, V.N., Murai, T., & Nakamori, Y. (2008). Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis. Information Sciences, 178(21): 4080-4093.
61. Zwick, F. (1967). The morphological approach to discovery, invention, research and construction. In New Method of Thought and Procedure: Symposium on Methodologies, Pasadena, May, pp. 316–317.