感性设计过程中的映射方法研究
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摘要
感性设计旨在将顾客的感性需求集成到新产品的设计元素中，以提高顾客满意度。确定感性词汇和设计元素之间的映射关系，是感性设计研究的核心议题。现已提出一些方法来建立感性映射模型，但仍然存在着许多问题亟待解决。本文旨在补充和完善这一感性映射过程的方法体系，具体从以下几个方面进行研究：
（1）在感性设计过程中考虑顾客群体的异质性，引入质量工程中的一种稳健性设计方法来设计感性产品，不仅使该产品大体上符合顾客群体的感性意象，而且使不同顾客对同一产品的感性反应的波动降到最小乃至消除。具体步骤为：首先，提出“感性距离”的概念，用以量化产品的感性质量；然后，将产品的各项感性参数作为控制因子，将顾客之间不一致的个体特征视为噪声因子，将感性距离作为响应变量，应用用单一表进行响应建模的方法，求得产品感性参数的稳健设置。以手机的感性设计为例，对上述方法进行了实际应用。
（2）引入序次Probit回归分析的方法到感性设计领域，用来构建从设计元素到顾客感性的映射知识。并且，本文提出在感性设计的实证研究过程中，为了在序次Probit回归和序次Logit回归模型之中选择一个拟合较好的模型，必须对实际数据分别建模并进行比较研究。对既定的产品轮廓，应用所选模型可以较准确地预测顾客的特定感性响应的具体程度，从而为概念设计的评估提供参考。在实例部分，用手机的感性设计过程进行说明。确定了针对手机产品的四个感性意象尺度和六个关键产品属性。先后运用序次Probit回归和序次Logit回归对所收集的数据建模，以揭示从设计元素到顾客感性的定量关系。在拟合优度、预测准确性、整体显著性、系数显著性、成比例发生比假设检验的显著性水平这五个方面，比较了这两个模型的优劣。结果表明：对于所观测的数据，序次Logit模型比序次Probit模型更为优越。
（3）针对感性挖掘过程，提出了一种提炼关联规则的方法：第一步，设定低水平的支持度和置信度的阈值进行关联规则挖掘，以保留尽可能多的有用信息；第二步，从第一步生成的原始关联规则中提炼出优质的关联规则，在这一步中，设计了一系列具体步骤。在设计师进行概念设计的过程中，所提炼出的优质关联规则可以为其提供指导，使产品设计有据可依。在实例部分，通过沃尔沃卡车驾驶室的感性设计说明了该方法的应用。
Affective product design aims at incorporating customers’ affective needs into designvariables of a new product so as to optimize customers’ affective satisfaction. The coreissue in the field of affective design is to identify the mapping relationships betweenKansei words and design elements. Although some methods have been proposed to buildaffective mapping models, there are still many problems to be solved. This dissertationaims to improve this affective mapping methodology system. The reseach contents andconclusions are as follows:
(1) To consider the heterogeneity in customer group, a robust design method inquality engineering is introduced to design a Kansei product which can satisfy customergroup’s mental imagery and decrease the fluctuation of Kansei reactions to the productamong different customers as far as possible. The concrete precedure is as follows: first,the concept of Kansei distance is proposed to quantify the Kansei quality of a product;then, regarding the Kansei parameters of the product as control factors, the highlyindividualized characteristics among customers as noise factors, and the Kansei distanceas response variable, the approach of response modeling based on single arrays isproposed to obtain the robust settings of a product. A case study of the Kansei design for amobile profile was conducted to illustrate and validate the proposed method.
(2) Ordinal probit regression (OPR) is introduced into the field of affective design todiscover mapping knowledge from design elements to customer affect, and a comparativestudy is always recommended between OPR and ordinal logistic regression (OLR) foravailable data to choose a better fitted model. The discovered mapping relations couldfacilitate the handling of affective information and assist the designer to make trade-offdecisions. A case study of cell phone design was conducted. Four generic affectivedimensions and six key product attributes of the cell phone were identified. OPR and OLRwere applied successively to reveal the quantitative relations from design elements tocustomer affect. The two models were compared in terms of goodness of fit, predictability,global significance, significance of coefficients and significance level of proportionalodds assumption. The findings show that OLR model is superior to OPR model to fit thecollected data.
(3) For the affective mining procedure, a method which could refine association ruleseffectively is presented: at first, a set of raw rules are generated by specifying low valuesfor the support and confidence thresholds; then, these raw rules are evaluated to refine themost meaningful rules, in this step, a series of concrete sub-steps is presented. A casestudy Volvo truck cab design was conducted to illustrate the proposed method.
（1）在感性设计过程中考虑顾客群体的异质性，引入质量工程中的一种稳健性设计方法来设计感性产品，不仅使该产品大体上符合顾客群体的感性意象，而且使不同顾客对同一产品的感性反应的波动降到最小乃至消除。具体步骤为：首先，提出“感性距离”的概念，用以量化产品的感性质量；然后，将产品的各项感性参数作为控制因子，将顾客之间不一致的个体特征视为噪声因子，将感性距离作为响应变量，应用用单一表进行响应建模的方法，求得产品感性参数的稳健设置。以手机的感性设计为例，对上述方法进行了实际应用。
（2）引入序次Probit回归分析的方法到感性设计领域，用来构建从设计元素到顾客感性的映射知识。并且，本文提出在感性设计的实证研究过程中，为了在序次Probit回归和序次Logit回归模型之中选择一个拟合较好的模型，必须对实际数据分别建模并进行比较研究。对既定的产品轮廓，应用所选模型可以较准确地预测顾客的特定感性响应的具体程度，从而为概念设计的评估提供参考。在实例部分，用手机的感性设计过程进行说明。确定了针对手机产品的四个感性意象尺度和六个关键产品属性。先后运用序次Probit回归和序次Logit回归对所收集的数据建模，以揭示从设计元素到顾客感性的定量关系。在拟合优度、预测准确性、整体显著性、系数显著性、成比例发生比假设检验的显著性水平这五个方面，比较了这两个模型的优劣。结果表明：对于所观测的数据，序次Logit模型比序次Probit模型更为优越。
（3）针对感性挖掘过程，提出了一种提炼关联规则的方法：第一步，设定低水平的支持度和置信度的阈值进行关联规则挖掘，以保留尽可能多的有用信息；第二步，从第一步生成的原始关联规则中提炼出优质的关联规则，在这一步中，设计了一系列具体步骤。在设计师进行概念设计的过程中，所提炼出的优质关联规则可以为其提供指导，使产品设计有据可依。在实例部分，通过沃尔沃卡车驾驶室的感性设计说明了该方法的应用。
Affective product design aims at incorporating customers’ affective needs into designvariables of a new product so as to optimize customers’ affective satisfaction. The coreissue in the field of affective design is to identify the mapping relationships betweenKansei words and design elements. Although some methods have been proposed to buildaffective mapping models, there are still many problems to be solved. This dissertationaims to improve this affective mapping methodology system. The reseach contents andconclusions are as follows:
(1) To consider the heterogeneity in customer group, a robust design method inquality engineering is introduced to design a Kansei product which can satisfy customergroup’s mental imagery and decrease the fluctuation of Kansei reactions to the productamong different customers as far as possible. The concrete precedure is as follows: first,the concept of Kansei distance is proposed to quantify the Kansei quality of a product;then, regarding the Kansei parameters of the product as control factors, the highlyindividualized characteristics among customers as noise factors, and the Kansei distanceas response variable, the approach of response modeling based on single arrays isproposed to obtain the robust settings of a product. A case study of the Kansei design for amobile profile was conducted to illustrate and validate the proposed method.
(2) Ordinal probit regression (OPR) is introduced into the field of affective design todiscover mapping knowledge from design elements to customer affect, and a comparativestudy is always recommended between OPR and ordinal logistic regression (OLR) foravailable data to choose a better fitted model. The discovered mapping relations couldfacilitate the handling of affective information and assist the designer to make trade-offdecisions. A case study of cell phone design was conducted. Four generic affectivedimensions and six key product attributes of the cell phone were identified. OPR and OLRwere applied successively to reveal the quantitative relations from design elements tocustomer affect. The two models were compared in terms of goodness of fit, predictability,global significance, significance of coefficients and significance level of proportionalodds assumption. The findings show that OLR model is superior to OPR model to fit thecollected data.
(3) For the affective mining procedure, a method which could refine association ruleseffectively is presented: at first, a set of raw rules are generated by specifying low valuesfor the support and confidence thresholds; then, these raw rules are evaluated to refine themost meaningful rules, in this step, a series of concrete sub-steps is presented. A casestudy Volvo truck cab design was conducted to illustrate the proposed method.
引文
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