产品感性评价系统的模糊D-S推理建模方法与应用

建立了用户对产品的感性评价与模糊D-S证据理论的关联模型;利用产生式规则来表达用户感性评价知识;以产品的部件外形来构成推理的证据部分;以感性词汇集及相应的语意差分值来定义规则的目标集.通过模糊D-S证据理论的规则合成算法,最终获得了用户对整体产品评价及可靠度.该方法能有效地解决感性认知中的"未知性"及评价日标的单一性问题,并在以汽车为例的概念设计中得到较好应用.     

Incorporating affective customer needs for luxuriousness into product design attributes

In a highly competitive market, customers' product affection is a critical factor to product success. However, understanding customers' affective needs is difficult to grasp; product design practitioners often misunderstand what customers really want. In this study we report our experience in developing and using an affective design framework that identified critical affective features customers have on products and are systematically incorporated into product design attributes. To identify key affective features such as luxuriousness, we utilized the Kansei engineering methodology. This approach consists of three steps: (1) selecting related affective features and product design attributes through a comprehensive literature survey, expert panel opinion, and focus group interviews; (2) conducting evaluation experiments; and (3) developing Kansei models using multivariate statistical analysis and analyzing critical product design attributes. To demonstrate applicability of the proposed affective design framework, 30 customers and 30 product design practitioners participated in an evaluation experiment for car crash pads, and 44 customers and 20 designers participated in an evaluation experiment for two interior room products (wallpapers and flooring materials). The evaluation experiments were conducted via systematically developed questionnaires consisting of a 7‐point semantic differential scale and a 100‐point magnitude estimation scale. The results of the experiments were analyzed using principal component regression and quantification theory type I method. Using the analyzed survey data, the relationship between luxuriousness and related affective features and product design attributes were identified. This relationship indicated that there was a significant difference in the perception of luxuriousness between customers and designers. Consequently, it is expected that the results of this study could provide a foundation for developing affective products. © 2009 Wiley Periodicals, Inc.     

A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling

Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.     

The Impact of Image Dimensions toward Online Consumers’ Perceptions of Product Aesthetics

Marketers and industrial designers devote considerable attention to the visual attributes of products, based on the premise that the visual appearance of products influences consumers’ judgments of the products’ attributes. This research investigated consumers’ perceptions about particular types of innovative products (revolutionary technology‐driven products), with 275 consumers sample purchased from an independent marketing company. To achieve the main goal, interrelations among image of product and aesthetics of product have been examined using structural equation modeling with two psychological moderators: consumer innovativeness and needs for uniqueness. The results of this study provide evidence that individual differences in uniqueness motivation moderated how online consumers’ perceptions of a product's image characteristics influenced perceptions of value showing consumers’ need for uniqueness was more influential toward perceptions of the product's value than perceptions of functional value. Consequently, these findings expand understanding of the consumer characteristics that respond to perceptions of products’ epistemic value.     

Selecting representative affective dimensions using Procrustes analysis: an application to mobile phone design

Collecting affective responses (ARs) from consumers is crucial to designers aspiring to produce an appealing product. Adjectives are frequently used by researchers as an affective means by which consumers can describe their subjective feelings regarding a specific product design. This study proposes a Kansei engineering (KE) approach for selecting representative affective dimensions using factor analysis (FA) and Procrustes analysis (PA). A semantic differential (SD) experiment is used to examine consumers' ARs toward a set of representative product samples. FA is employed to extract the underlying latent factors using an initial set of affective dimensions. A backward elimination process based on PA is used to determine the relative significance of adjectives in each step according to the calculated residual sum of squared differences (RSSDs) to finally obtain the ranking of the initial set of adjectives. Additionally, the results of the proposed approach are compared to the method that combines FA and two-stage cluster analysis (CA). A case study of mobile phone design is provided to demonstrate the analysis results.     

Price-aware matrix factorization model for personalized recommendations

Recommendation methods aim to assist consumers in their decision-making process to find products that they are quite likely interested in. Current recommendation methods generally use online consumer reviews or ratings to predict consumers’ preferences for products. In e-commerce transactions, price plays a significant role in consumers’ purchase decisions. And each consumer's preference for product prices is specific. In this paper, we propose a novel price-aware recommendation method based on the matrix factorization model which fully considers the effect of price. We distill the price preferences of consumers from ratings and discover consumers’ real preferences for products. We further calculate the price sensitivities of consumers, not only considering the difference in price preference between a consumer and others but also focusing on the consumers’ price preferences in a specific price range. The final predicted ratings are calculated by adding the term of price effect into the matrix factorization framework. The results show that the proposed method has achieved high accuracy and performed better than several existing methods in both rating prediction and Top-N recommendation. And the products we recommend for consumers are more in line with their price preferences. Moreover, we find that, to some extent, the proposed method can solve the long-tail product recommendation problem with the consideration of the price effect.     

A classification-based Kansei engineering system for modeling consumers’ affective responses and analyzing product form features

In the product design field, modeling consumers’ affective responses (CARs) for product form design is very helpful for developing successful products. It is also important for product designers to identify critical product form features (PFFs) to aid them in producing appealing products. In the present paper, a classification-based Kansei engineering system (KES) is proposed for modeling CARs and analyzing PFFs in a systematic manner. First, single adjectives are collected as initial affective dimensions for consumers to evaluate a set of representative products in the first questionnaire experiment. Factor analysis (FA) combined with Procrustes analysis (PA) is then used to extract representative affective dimensions. Second, these representative adjectives are regarded as class labels for consumers to describe their affective responses toward product form design. A large set of product samples are analyzed and their PFFs are encoded into numerical format. In the second questionnaire experiment, consumers are asked to assign one most suitable class labels to each product samples. A multiclass support vector machine (SVM) classification model is constructed for relating CARs and the PFFs. Optimal training parameters of SVM can be determined by a two-step cross-validation (CV). Third, support vector machine recursive feature elimination (SVM-RFE) is applied to pin point critical PFFs by wither using overall ranking or class-specific ranking. The relative importance of each PFF can be also analyzed by examining the weight distribution of the PFFs in each elimination step. A case study of digital camera design is also given to demonstrate the effectiveness of the proposed method.     

产品外形设计中客户感性认知模型及应用

为了更好地利用客户的感性认知支持产品外形设计活动,提出一种产品外形设计中客户感性认知与产品外形特征关联模型.在分析产品外形设计中所涉及的客户感性认知特点的基础上,建立产品外形特征要素与客户感性认知要素的关联模式;以认知行为为标准,利用特征匹配实现了客户感性认知的识别与相似性分析;提出了基于模糊认知图的客户感性认知与产品外形特征关联模型,利用蚁群聚类算法确定模糊认知图的结构及邻接矩阵,从而获取客户感性认知以指导产品的外形设计.最后通过实例验证了该模型的有效性和实用性.     

Relationship between eye fixation patterns and Kansei evaluation of 3D chair forms

Understanding how to induce Kansei (emotion or affect) in consumers through form is critical in product design and development. Conventional Kansei evaluations, which involve subjectively evaluating the overall form of a product, do not clarify the effects of the individual parts of a product on people’s Kansei evaluation. A microscale analysis of eye movement of people looking at product form may redeem this flaw in subjective evaluation. However, simultaneously recording eye movement when people making Kansei evaluation is challenging, previous studies have typically investigated either the relationship between form and eye movement or the relationship between form and Kansei separately. The eye movement of people while performing Kansei evaluations on product forms still has not been clarified. To address this issue, the present study used an eye tracking system to analyze the changes in the fixation points of people performing various Kansei evaluations. Twenty participants were recruited for 8 Kansei evaluations on the form of 16 chairs by using the semantic differential (SD) rating, while their eye movements on these evaluations were tracked simultaneously. Through factor analysis on the data of Kansei evaluations, two principal factors, valence (pleasure) and arousal, were extracted from the 8 Kansei scales to constitute a Kansei plane which is compatible to Russell’s circumplex model (plane) of affect By adopting the factor scores of the 16 chairs as coordinates, the 16 chairs were mapped into the Kansei plane. Further analysis on the eye fixation on the chairs located in this plane concluded the following results: (a) Pleasure had a more significant effect on the participants’ visual attention compared to arousal; the participants required more fixation points when evaluating the chair form that induced displeasure. (b) The participants typically fixated on two parts of the chairs during their Kansei evaluations, namely the seat and the backrest, indicating that seats and backrests are the two primary features people consider when evaluating chairs. The results clarify the effect of various Kansei on eye movements; thereby enable predicting people’s Kansei evaluations of product forms through analyzing their eye movement.     

Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

The modified Kansei Engineering-based application for sustainable service design

Considering the human-centered design, this paper shows a modified-integrated approach of how to quantify the impact of perceived Kano's attractive services on perceived emotional satisfaction (Kansei), followed by the formulation of innovative ideas for sustainable services using TRIZ (known as Theory of Inventive Problem Solving). The Kano's attractive service attribute is deemed to be a significant emotional booster (known as Kansei). Kansei Engineering (KE) is used to highlight the level of customer emotional satisfaction due to perceived service offerings. For the past seven years, there has been a rapid concern in Kansei Engineering (KE) in services. However, previous research of KE has mainly focused on the improvement and analysis of general service domains. There is little attention to sustainable services. Hence, this study provides a modified KE-based approach and aims to understand and satisfy customer emotional needs (Kansei) considering the social, environmental and economic performance. An empirical study in an international airport lounge and lobby services was conducted to confirm the applicability of the proposed model. Purposive sampling through in-depth-interview and face-to-face questionnaires which involved 100 valid subjects was used. Theoretically, these studies show the importance of Kansei's role in sustainable service development, highlighting more innovative and breakthrough solutions with less contradiction and “true-meaning” of Kansei. Practically, it provides a guideline for service designer and manager in identifying which attractive-based service attributes need to be prioritized considering Kansei satisfaction.     

基于感性工学的文具产品形态设计研究

随着社会发展,消费者对于产品的精神和情感需求越来越高。因此,如何有效的获取消费者的心理情感需求,并进行有效转化至产品设计之中,成为设计中的新课题。感性工学正是在这种情形下产生,其旨在探求消费者情感与产品特性的对应关系,服务于消费者。本文将以文具设计为例,简述感性工学在文具设计中的应用。     

Search product and experience product online reviews: An eye-tracking study on consumers' review search behavior

This study uses eye-tracking method to investigate consumers' online review search behavior by suggesting that it needs to consider the type of product reviewed. A review-product congruity proposition was testified through a self-report survey and an eye-tracking experiment. The proposition states that consumers of search products expect to seek attribute based reviews, while consumers shopping for experience products tend to seek experience based reviews. Two experiments were conducted in the human factors & ergonomics laboratory of Beihang University, China and all subjects are college students. The results of our first empirical experiment support our hypotheses by showing consumers' more active and positive responses to attribute based reviews when shopping for search products and to experience based reviews when purchasing experience products. The second experiment was conducted with eye tracking method to gain further insights. We found that consumers of search products are attracted and engaged more deeply by attribute based reviews. However, when they browse experience products, the difference of their fixations on experience based reviews and attribute based reviews is not significant, and thus the proposition is partially supported. This study extends our current understanding of consumers' online review search behavior by subsuming product type, which is necessary and helpful, and provides references on the classification and presentation of reviews to facilitate consumers' product judgement and decision making. Moreover, comparison of traditional empirical method and eye-tracking method can help deepen our understanding of complex consumer online shopping behavior.     

A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction

Due to the continuous release of new products, manufacturers are paying attention to customer-oriented design of products that meet user needs to minimize the risk of their products being rejected by the market. Due to the ambiguity of user cognition, it is difficult to accurately obtain the user's preference for individual productions. To respond to the challenge, we propose an engineering scientific research method of interactive genetic algorithm with the interval arithmetic based on hesitation and fuzzy kano model(FKM) to explore the emotional needs of users for product forms and drive product modeling evolution design. Through expert interviews, the morphological characteristics and perceptual images factors of the products attracting users are investigated. In order to identify the user's satisfaction relationship with the perceptual images, we use FKM to analyze the product image style that meets the user's kansei needs accurately and selects 5 factors which is attractive attributes. Meanwhile, we attempt to transform this 5 factors into evaluation carrier to guide the evolution direction of product styling in HIIF-IGA, and then optimized four electric bikes with scores over 8.8 so that it could realize user demand-driven product evolution design. To handle users' ambiguity, the FAHP method is used to quantify the user's emotional imagery criterion and create a product evolution design system platform, which can automatically generate product styling design scheme in line with user preferences. This experimental results show that the proposed method can help enterprises effectively improve customer satisfaction and reduce the cost and time of product development.     

Product selection in Internet business: a fuzzy approach

In this paper, we propose a methodology which helps customers buy products through the Internet. This procedure takes into account the customer's level of desire in the product attributes, which are normally fuzzy, or in linguistically defined terms. The concept of fuzzy number will be used to measure the degree of similarities of the available products to that of the customer's requirements. The degrees of similarities so obtained over all the attributes give rise to the fuzzy probabilities and hence the fuzzy expected values of availing a product on the Internet as per the customer's requirement. Attribute‐wise the fuzzy expected values are compared with those of the available products on the Internet and the product that is closest to the customer's preference is selected as the best product. The multi‐attribute weighted average method is used here to evaluate and hence to select the best product.     

Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered

Eye tracking probes user's perception of real-time reaction to products, while conventional methods (i.e. interviews, focus group, questionnaires and so on) have generally failed because they depend on users' willingness and competency to describe how they feel when they are exposed to a product. Two tasks were designed to explore the indexes of eye movement that can reflect user experience of product, and analyse the attention captured by product attributes and goal-oriented. In task one, participants just browsed two smart phone pictures and evaluated the whole user experience. Binary choices were used in task two to ask participants to select the smart phone picture with higher user experience and then click the mouse. The results showed that in the browsing task, participants had shorter time to first fixation for the smart phone picture with higher level of user experience than the lower. And pupil dilated significantly when participants browse smart phone picture with lower level of user experience. In goal-oriented task, participants' attentions were dominated by visual perception of task driven, mainly reflected on longer fixation time and larger pupil diameter when looking at the smart phone with higher level of user experience. These results support the notion that we cannot assess product design just by several eye-movement indexes without considering the effects of visual attention mechanism.Relevance to industryThe appearance of product plays an important role to attract user's attention and stimulate their intention to experience. And vision is the main channel for users to obtain product information. Hence a thorough research on the inherent mechanism of vision perception can provide technical support for product designers, which in turn can attract more consumers to experience the product, even buy it. Moreover, the seller can find out the real buyers and predict their desired products by tracking user's eyes.     

Building an understanding of how winning products emerge when open and proprietary products coexist: Evidence from the RepRap community

Online innovation communities have altered the nature of collaborative innovation. Within these communities, coexistence of open and closed source offerings is becoming commonplace, though potential diffusion and product advantages from each form are not well understood. Patterns of derivative innovation within these communities affect designers' focus; thus, this work is grounded in the attention‐based view. Beyond open vs. closed source development, we find that the presence of sibling designs (designs based on the same source material) and self‐remix (iteration on material by the same designer) have notable diffusion and product effects. Diffusion effects are investigated using 354 co‐existing open and closed source 3D printers from the RepRap community, while a subset of these printers is used for an analysis of key product attributes: value and ease of use. While previous researchers have argued for an early stage open source diffusion advantage, this is not observed here. However, customers perceive open source products to have value advantages, while closed source offerings are easier to use. Sibling designs have a diffusion advantage, particularly early on. Self‐remixes have both diffusion and product advantages. By better understanding these contextual elements of derivative innovation, designers' attention can be shaped to achieve desired outcomes.     

基于产品感性定制模型的顾客协同设计系统

为使产品定制模型更加适合缺少相关领域专业知识的大众消费者,建立了基于感性工学的产品感性定制模型。引入配件感性性能指数、产品感性性能矩阵对产品感性性能进行量化。使用层次分析法实现了求解与顾客对产品感性性能需求对应的产品工程配置的方法。并应用产品感性定制模型,构建了基于Web和虚拟现实技术的顾客协同设计系统。