In recent years, product emotion and affective design has received encouraging attention from the industry as well as academia all over the world. Several methods and tools exist and used to assist the process of evaluating users' emotional experience, and the proceeding associated procedure. Previous studies involving the assessment of emotion have seen different ways used to represent verbal description of the subjective emotion. Most of them set their basis on several keywords that somehow fit to describe the study domain. However, these have lead to many cases of poor semantic dimension, since a good reference for affinity of words does not exist. This research aimed to develop a full-range of emotional keywords and their affinity cluster by the use of KJ Method. As a result, a total of 820 words were derived and forty-three clusters were generated. The resulting cluster is developed into Kansei Affinity Cluster, which will be a good reference for all studies involving the assessment of emotion. It will benefit the industry as well as academia towards accessing users' subjective emotional experience with product design.
Emotions are compelling human experiences and product designers can take advantage of this by conceptualizing emotion-engendering products that sell well in the market. This study hypothesized that product attributes influence users’ emotions and that the relationship is moderated by the adherence of these product attributes to purchase criteria. It was further hypothesized that the emotional experience of the user influences purchase intention. A laboratory study was conducted to validate the hypotheses using mobile phones as test products. Sixty-two participants were asked to assess eight phones from a display of 10 phones and indicate their emotional experiences after assessment. Results suggest that some product attributes can cause intense emotional experience. The attributes relate to the phone's dimensions and the relationship between these dimensions. The study validated the notion of integrating affect in designing products that convey users’ personalities.
► A multi-objective genetic algorithm based rule mining approach is proposed for affective design. ► Approximate rules for affective design are generated. ► Both types of categorical and quantitative attributes can be dealt with. ► Lower and upper limits of the affective effects of design patterns can be determined. A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.
Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modeling customer satisfaction for affective design. However, ANFIS is incapable of modeling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the ‘out of memory’ error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors.
Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.
Crucial issues for product designers include how to capture consumers’ attention, evoke their pleasurable preferences, and affect their purchase decisions. In this article, we focus on consumers’ affective preferences in relation to visual ergonomics to propose a new hybrid consumer‐oriented model using gray relational analysis (GRA), gray prediction (GP), and the technique for order preference by similarity to ideal solution (TOPSIS). The GRA is used to identify the most influential elements of the product form to help product designers focus their attention more on these elements without compromising the predictive performance. The GP is used in conjunction with the GRA to obtain a better structure for the hybrid consumer‐oriented model, and TOPSIS is performed to determine the optimal alternatives for best matching consumers’ affective preferences. These experimental results show that the new hybrid consumer‐oriented model incorporated with the CAD/CAM system can facilitate the product affective design process.
Affective product design aims at incorporating customers' affective needs into design variables of a new product so as to optimise customers' affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximise customers' affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers' affective responses to the design variables of a new product. Customer survey data are usually fuzzy since human feeling is usually fuzzy, and the relationship between customers' affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this, we extend the existing work on fuzzy regression by first utilising an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method.