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題名：情感反應尺度篩選與產品外型特徵關係之研究
作者：王宗興
作者(外文)：Tsung-HsingWang
校院名稱：國立成功大學
系所名稱：工業設計學系碩博士班
指導教授：謝孟達
學位類別：博士
出版日期：2013
主題關鍵詞：感性工學系統；情感反應尺度篩選；集群分析；普魯斯特分析；數量化一類；Kansei engineering system；Affective response dimension selection；Cluster analysis；Procrustes Analysis；Quantitative Theory Type I.
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本論文係基於感性工學系統(Kansei engineering system)，對於消費者的情感反應(consumers’ affective responses)與產品外型特徵 (product form features)兩者之間的關係進行研究，近年來它已成為工業設計領域的重要研究議題。對於產品外型的呈現，消費者的情感反應是影響他們是否決定購買的主要原因。在產品設計領域，研究者經常會提出形容詞來闡述消費者主觀的心理感受，這些形容詞也代表了消費者的情感反應，然而在這些形容詞中卻存在著相似性與模糊性，這使得研究者在語意差異實驗中要挑選適合的形容詞有些困難。為了要挑選出代表性的形容詞來描述消費者的情感反應，本研究提出了因素分析(Factor analysis)、集群分析(Cluster analysis)、普魯斯特分析(Procrustes analysis)與KJ(Kawakita Jiro)方法。研究初期所蒐集的形容詞以語意差異法(semantic differential method)作為研究的前測實驗，來取得消費者情感反應之數據，因素分析用來找出潛在的主要因素構面，集群分析則用於萃取出代表性的形容詞；同時使用普魯斯特分析的排序規則來決定形容詞的優先性，另一方面，也使用KJ法來挑選形容詞。為了比較此三種方法的差異性，本研究以手機產品為例進行分析，並以自行車與數位相機的兩種例子加以說明；研究後期，則進行此三種方法的比較，從比較的結果顯示，普魯斯特分析是能夠挑選出代表性形容詞較適合的方法。此外為了萃取關鍵的產品外型特徵，形態分析法用於產品外型特徵的分類預測，數量化一類理論(Quantitative Theory Type I)則用於建構消費者情感反應與產品外型特徵兩者之間線性迴歸的關連性。在線性回歸方程式中，從產品外型特徵(product form features)的細目得點能夠反應出對於消費者情感反應(Consumers’ affective responses)的影響程度；對於產品外型特徵的相對重要性，也可以藉由分析外型特徵的權重來獲得。使用數量化一類理論，不僅可以挑選出關鍵的產品外型特徵，而這些外形特徵對消費者情感反應的影響也可以被分析出來，不管是使用形態分析法的分類模式或是線性迴歸模式中的關鍵外型特徵挑選，都對產品開發的過程有很大的幫助，同時藉由方法論的使用可擴及至產品設計領域的實際應用。
This study deals with the relationship between Consumers’ Affective Responses (CARs) and Product Form Features (PFFs) arising in the context of Kansei Engineering System (KES).This study deals with the relationship between consumers’ affective responses (CARs) and product form features (PFFs) arising in the context of Kansei engineering system (KES). In recent years, it has been an important research issue in the field of industrial design that CARs to the appearance of a product seems greatly influencing consumers’ purchasing decisions and interest rates. In the product design field, researchers often provide adjectives so that consumers can express their subjective feelings. Nevertheless, among the similarity and the vagueness of the chosen adjectives, it is unlikely to choose suitable adjectives for Semantic Differential (SD) experiments.In recent years, it has been an important research issue in the industrial design field. Consumers’ affective responses to the appearance of a product will greatly influence their purchasing decisions. In the product design field, researchers often provide adjectives so that consumers can express their subjective feelings. However, there exists both similarity and vagueness among these adjectives, which make it difficult to choose suitable adjectives for semantic differential (SD) experiments. In order to select distinguished adjectives for describing CARs, some methods based on Factor Analysis (FA), Cluster Analysis (CA), Procrustes Analysis (PA) and KJ method were adopted.In order to select representative adjectives for describing CARs, some method based on factor analysis (FA), cluster analysis (CA), Procrustes analysis (PA) and KJ method were proposed. Previously, Semantic Differential (SD) method has been applied to collect the CARs data. In this study, FA was used to decide latent factor dimensions. Then, CA was applied to extract the representative adjective vocabularies, as well as PA to decide adjective vocabularies according to the sorting rule. KJ method was also applied to select suitable adjective vocabularies. Previously, the selected adjectives can be used in the semantic differential (SD) method as the prior test experiment to obtain the CARs data. Then, FA was used to decide the latent factor dimensions, CA to extract the representative adjectives vocabulary. In the meanwhile, the PA was also used to decide adjective the priorities of these adjectives according to the sorting rule. On the other hand, the KJ method was also used to select the adjectives vocabulary. In order to compare the favorability among these three methods (FA/CA, FA/PA, KJ), this study has proposed an example of mobile phone for analysis. In addition, two examples of bicycle and digital camera were raised to illustrate. Finally, these three methods were used to compare the effectiveness.To compare the differences in these three methods, this study proposed an example of mobile phone for analysis. In addition, two examples of bicycle and digital camera were raised to illustrate. Finally, these three methods were used to compare the effectiveness. From the result of compared, PA is the most suitable method of selecting CARs. For selecting critical form features, a feature selection method, a shape analysis method is adapted to develop the classification based on PFFs approach. Quantitative Theory Type I (QT1) was used to create the liner regression relevance between CARs and PFFs. In linear regression equation, the category scores have reflected the degree of influence for CARs. For the relative importance of the product appearance, we can also analyze the characteristics of the shapes by the weight to obtain.From the results of with comparedcomparison, PA is the a suitable method of for selecting CARs. For selecting critical form features, a feature selection method, i.e. shape analysis method, is adapted to develop the classification based PFFs approach. Quantitative Theory Type I (QT1) was used to create the linear regression relevance between CARs and PFFs. In linear regression equation, the category score can reflect the degree of influence for CARs. To the relative importance of the product appearance, we can also analyze the characteristics of shape by the weight to get. By applying QT1, not only the critical PFFs can be selected but also the influence over producing specific CARs can be extracted. It is recognized that either the methodology of classification model or regression based on PFFs is beneficial to future products development process. Meanwhile, this methodology also extends the actual application in product design field.By Applied applying QT1, not only the critical PFFs can be selected but also their influence to produce specific CARs can be extracted. Either the methodology of classification model or the regression method based PFFs are is beneficial to the product development process. In the meanwhile, the use of the methodology, extends the actual application in product design field.
Alcantara, E., Artacho, M. A., et al. (2005). Application of product semantics to footwear design. Part I—Identification of footwear semantic space applying differential semantics. International Journal of Industrial Ergonomics, vol. 35, 713-725.
Andrade, J. M., Gomez-Carracedo, M. P., et al. (2004). Procrustes rotation in analytical chemistry, a tutorial. Chemometrics and Intelligent Laboratory Systems, vol. 72, 123-132.
Andrew, B. A. ＆ Marzano, S. (1995). New Objects, New Media, Old Walls. 1995 Philips Coperate Design, Eindhoven, The Netherlands.
Ball, Geoffrey H. ＆ Hall, David. J. (1967). A clustering technique for summarizing multivariate data. Behavioral Science, vol. 12, no. 2, 153-155.
Bloch, P. H. (1995). Seeking the Ideal Form: Product Design and Consumer Response. Journal of Marketing, vol. 59, no. 3, 16-29.
Breemen, Ernest J. J. van. ＆ Sudijono, S. 1999(b), The Role of Shape in Communicating Designers' Aesthetic intents, Proceeding of the 1999 ASME Design Engineering Technical Conferences, September 12, 1999, Las vegas, Nevada.
Broom, G. M., Casey, S., et al. (1997). Toward a concept and theory of organization-public relationships. Journal of Public Relations Research, vol. 9, 83-98.
Carlosena, A., Andrade, J.M., et al. (1995). Procrustes Rotation as a Way To Compare Different Sampling Seasons in Soils. Analytical Chemistry, vol. 67, no. 14, 2373-2378.
Carroll, J. B. (1959). Review of the measurement of meaning. Canadian Journal of Psychology/Revue canadienne de psychologie, vol. 12, 138-139.
Chang, H. C., Lai, H. H., et al. (2005). Expression Modes Used by Consumers in Conveying Desire for Product Form: a Case Study of a Car. International Journal of Industrial Ergonomics, vol. 36, 3-10.
Chen, D., Wang, J., et al. (2010). A Search algorithm for clusters in a network or graph. International Journal of Digital Content Technology and its Applications, vol. 4, no. 6, 115-122.
Chen, K. H. (1995). Form Generation and Style Association. Doctor of Philosophy
in Design dissertation, 26-27. Institute of Design, Illinois Institute of Technology, Chicago, IL, U.S.A.
Chen, K. H. ＆ Owen, C. L. (1997). Form Language and Style Description.. Design Studies, vol. 18, 249-274.
Chen, C. H., Khoo, L. P., et al. (2005). PDCS — a product definition and customisation system for product concept development. Expert Systems with Applications, vol. 28, 591–602.
Chuang, M. C., Chang, C. C., et al. (2001). Perceptual factors underlying user preferences toward product form of mobile phones. International Journal of Industrial Ergonomics, vol. 27, 247-258.
Chuang, M. C. ＆ Ma, Y. C. (2001). Expressing the expected product images in product design of micro-electronic products. International Journal of Industrial Ergonomics, vol. 27, 233-245.
Coxhead, P. ＆ Bynner, J. M. (1981). Factor analysis of semantic differential data. Quality ＆ Quantity, vol. 15, 553-567.
Creusen, Marielle E. H. ＆ Schoormans, Jan P. L. (2005). The Different Roles of Product Appearance in Consumer Choice. The journal of product innovation management, vol. 22, 63-81.
Demir, C., Hindmarch, P., et al. (1996). Procrustes Analysis for the Determination of Number of Significant Masses in Gas Chromatography-Mass Spectrometry. Analyst, vol. 121, 1443-1449.
Fan, L., Liu, C., et al. (2010). The application research of high-dimensional mixed-attribute data clustering algorithm. International Journal of Digital Content Technology and its Applications, vol. 4, no. 2, 131-134.
Genno, H., Ishikawa, K., et al. (1997). Using facial skin temperature to objectively evaluate sensations. International Journal of Industrial Ergonomics, vol. 19, 161-171.
Hajime, O., Kazuhisa, K., et al. (1990). Idea Processor and the KJ Method. Journal of Information Processing, vol. 13, no. 1, 44-48.
Han, S. H. ＆ Hong, S. W. (2003). A systematic approach for coupling user satisfaction with product design. Ergonomics, vol. 46, 1441-1461.
Han, S. H. ＆ Kim, J. (2003). A comparison of screening methods: selecting important design variables for modeling product usability. International Journal of Industrial Ergonomics, vol. 32, 189-198.
Han, S. H., Kim, K. J., et al. (2004). Identifying mobile phone design features critical to user satisfaction. Human Factors and Ergonomics in Manufacturing, vol. 14, 15-29.
Harshman, R. A. (1970). Foundations of the PARAFAC procedure: models and conditions for an explanatory multimodal factor analysis. UCLA, Working Papers in Phonetics, December 16, 1970.
Hayashi, C. (1976). Method of Quantification. Toyokeizai, Tokyo.
Hsiao, K. A. ＆ Chen, L. L. (2006). Fundamental dimensions of affective responses to product shapes. International Journal of Industrial Ergonomics, vol. 36, 553-564.
Hsiao, S. W. ＆ Chen, C. H. (1997). A semantic and shape grammar based approach for product design. Design studies, vol. 18, 275-296.
Hsu, S. H., Chuang, M. C., et al. (2000). A semantic differential study of designers' and users' product form perception. International Journal of Industrial Ergonomics, vol. 25, 375-391.
Hurley, J. R. ＆ Cattell, R. B. (1962). The Procrustes Program: Producing Direct Rotation to Test a Hypothesized Factor Structure. Computers In Behavioral Science, vol.7, 258-262.
Isaacson, W. (2011). Steve Jobs: A Biography. Little Brown Book Group.
Jindo, T., Hirasago, K., et al. (1995). Development of a design support system for office chairs using 3-D graphics. International Journal of Industrial Ergonomics, vol.15, 49-62.
Jindo, T. ＆ Hirasago, K. (1997). Application studies to car interior of Kansei engineering. International Journal of Industrial Ergonomics, vol.19, 105-114.
Jones, J. C. (1992). Design Methods. New York: Van Nostrand Reinhold.
Kawakita, J. (1986). KJ Method, Tokyo: Chuokoron-Sha.
Kim, J. O. ＆ Mueller, C. W. (1978). Factor Analysis: Statistical Methods and Practical Issues. Newbury Park: Sage Publication.
Krzanowski, W. J. (1987). Selection of variables to preserve multivariate data structure, using principal components. Applied Statistics, vol. 36, 22-33.
Kwahk, J. ＆ Han, S. H. (2002). A methodology for evaluating the usability of audiovisual consumer electronic products. Applied Ergonomics, vol. 33, 419-431.
Lee, SeungHee., Stappers, P. J., et al. (1999). Extending of Design approach based on Kansei by Dynamic Manipulation of 3D Objects. 1999 4th Asian Design Conference.
Liu, Y. ＆ Shen, Y. (2010). Data clustering with CAT swarm optimization. Journal of Convergence Information Technology, vol. 5, no. 8, 21-28.
McDonagh-Philp, D. ＆ Lebbon, C. (2000). The emotional domain in product design. The design journal. vol. 3, no. 1, 31–43.
Mondragón, S., Company, P., et al. (2005). Semantic Differential applied to the evaluation of machine tool design. International Journal of Industrial Ergonomics, vol. 35, 1021-1029.
Nagamachi, M. (1989). Kansei Engineering. Tokyo: Kaibundo Publisher.
Nagamachi, M. (1995). Kansei Engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, vol. 15, 3-11.
Nagamachi, M. (2001), Workshop 2 on Kansei Engineering, Proceedings of International Conference on Affective Human Factors Design, Singapore.
Nagasawa, S. (2004), Present State of Kansei Engineering in Japan. 2004 IEEE Internaional Conference System, Man and Cybernetics, 333-338.
Nakada, K. (1997). Kansei engineering research on the design of construction machinery. International Journal of Industrial Ergonomics, vol. 19, 129-146.
Osgood, C. E., Suci, C. J., et al. (1957). The Measurement of Meaning. Champaign, IL: University of Illinois Press.
Park, J. ＆ Han, S. H. (2004). A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. International Journal of Industrial Ergonomics, vol. 34, 31-47.
Petiota, J. ＆ Yannou, B. (2004). Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics. International Journal of Industrial Ergonomics, vol. 33, 507-525.
Picard, R. W., Vyzas, E., et al. (2001). Toward Machine Emotional Intelligence:Analysis of Affective Physiological State. IEEE transactions on pattern analysis and machine intelligence, vol. 23, no. 10, 1175-1191.
Punj, G. ＆ Stewart, D. W. (1983). Cluster Analysis in Market Research: Review and Suggestions for Application. Journal of Marketing Research, 134-148.
Sahmer, K., ＆ Qannari, E. M. (2008). Procedures for the selection of a subset of attributes in sensory profiling. Food Quality and Preference, vol. 19, 141-145.
Sahmer, K., Vigneau, E., et al. (2006). A cluster approach to analyze preference data: Choice of the number of clusters. Food Quality and Preference, vol. 17, 257-265.
Salvador, M., Pedro, C., et al. (2005). Semantic Differential Applied to the Evaluation of Machine Tool Design. International Journal of Industrial Ergonomics, vol. 35, 1021-1029.
Schutte, S., Eklund, J., et al. (2004). Concepts, methods and tools in Kansei engineering. Theoretical Issues in Ergonomics Sciences, vol. 5, 214-231.
Seva, R. R., Duh, H. B-L., et al. (2007). The marketing implications of affective product design. Applied Ergonomics, vol. 38, 723–731.
Shieh, M. D., Wang, T. H., et al. (2011). Affective response dimension selection for product design: A comparison of cluster analysis and Procrustes analysis. International Journal of Digital Content Technology and its Applications, vol. 5, no. 1, 305-318.
Shieh, M. D., Wang, T. H., et al. (2011). A Clustering Approach to Affective Response Dimension Selection for Product Design. Journal of Convergence Information Technology, vol. 6, no. 2, 197-206.
Shieh, M. D., ＆ Yang, C. C. (2008). Classification model for product form design using fuzzy support vector machines. Computers ＆ Industrial Engineering, vol. 55, 150-164.
Shieh, M. D., ＆ Yang, C. C. (2008), Multiclass SVM-RFE for product form feature selection. Expert Systems with Applications, vol. 35, 531-541.
Shinya, N. (2004), Present State of Kansei Engineering in Japan. 2004 IEEE Internaional Conference System, Man and Cybernetics, 333-338.
Thurstone, L. L. (1947). Multiple Factor Analysis. Chicago: University of Chicago Press.
Vigneau, E., ＆ Qannari, E. M. (2003). Clustering of variables around latent components. Communications in Statistics-Simulation and Computation, vol. 32, 1131-1150.
Wallace, D. R. (1991). A computer model of aesthetic industrial design, Massachusetts Institute of Technology.
Yamamoto, K., Yoshikawa, T., et al. (2005). Division method of subjects by individuality for stratified analysis of SD evaluation data. In IEEE International Symposium on Micro-NanoMechatronics and Human Science. Nov. 7-9, 29-34.
Yang, C. C. (2008). A Study on Variable Selection for Kansei Engineering System. VDM Publishing House Ltd.
Zwicky, F. (1967). The morphological Approach to discovery,invention, research and construction. New method of thought and procedure, symposion on
methodologies，Psadena, 316~317.
 
 
 
 
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