Form design of product image using grey relational analysis and neural network models

This paper presents a new approach to determining the best design combination of product form elements for matching a given product image represented by a word pair. A grey relational analysis (GRA) model is used to examine the relationship between product form elements and product image, thus identifying the most influential elements of product form for a given product image. A grey prediction (GP) model and a neural network (NN) model are used individually and in conjunction with the GRA model, in order to predict and suggest the best form design combination. An experimental study on the form design of mobile phones is conducted to evaluate the performance of these models. Based on expert surveys, the concept of Kansei Engineering is used to extract and evaluate the experimental samples, and a morphological analysis is used to extract form elements from these sample mobile phones. The evaluation result shows that all the NN-based models outperform the GP-based models, suggesting that the NN model should be used to help product designers determine the best combination of form elements for achieving a desirable product image. The GRA model can be incorporated into the NN model to help designers focus on the most influential elements in form design of mobile phones.     

Research on construction and application of gene network model for form design based on consumer’s preference

With developments in science and technology, product innovation is the key to enterprise performance in the competitive market. In consumer demand-oriented modern product design, effectively combining product design with the esthetic perceptions and preferences of consumers is a problem that urgently needs to be addressed. However, the traditional design method is completely dominated by designers without user participation. The purpose of this study was to give designers an impetus for restructuring and upgrading a design. A gene network design model was developed with the advantages of a gene network and neural network. This allowed the target elements of a product design to be obtained using a nonlinear network. Finally, a case study was used to show the detailed procedure of the design model. To reveal the advantages of the proposed model, it was compared with other methods such as a gene-based design method, an emotional design method, and a fuzzy Kano design method. The results showed that the proposed model was more efficient and scientific, and provided consumers with a multidimensional evaluation system to determine the optimal design schemes.     

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.     

走在销售曲线前头——设计领先才能赢得市场

新世纪的市场竞争是设计实力的竞争。只有当我们设计出来的产品是适当的,能够起到满足消费者的有效需求时,才能够有效的促进销售。这一切都必须建立在良好设计的基础之上。在产品的价格、质量和功能都类似的情况下,个性化的设计越来越受到消费者的欢迎。设计师对产品功能的策划,正是体现着设计师对市场和产品研究的深度和一个设计师的创造力。     

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 methodology for brand feature establishment based on the decomposition and reconstruction of a feature curve

For creative products, maintaining original brand elements and features in a new product is an important issue in the design process as brand features are conceived and generated for longevity. However, current methods rely on designers’ abilities, and the size of forms is easily affected when shape morphing is applied, causing limitations in computer-aided design. In order to focus on design while preserving key features, a systematic method for presenting brand features is proposed in this article. In this method, the feature curves of the brand features of a company are decomposed with defined feature parameters, which were then used to reconstruct the feature curve of the designed product in the design stage by using a residual modified gray prediction model. A classic vehicle configuration design is taken as an example to show the implementation procedure of the proposed method. With residual modification, this method can also assimilate other forms from the original form database, and generate new forms based on gray prediction. The results show that brand features can be retained in the newly designed product based on the proposed method. Though vehicle design is taken as the example, this method can also be used to develop designs for many other the brand features. For classic products with historical value, this method can generate new forms that maintain original brand features, thereby satisfying customers’ needs for brand authenticity.     

产品包装与品牌推广分析

品牌如同人一样,拥有性格特征。有关品牌特征方面的研究已经证明了其对消费者自我表达,消费者行为和品牌忠诚度所具有的影响力。产品怎样从包装上抓住用户的心理及想要表达的情感特征并成功推广品牌成为现今无论是设计师还是设计企业都不可忽视的问题。     

产品创新中的人性化设计与个性化设计

文章通过论述产品创新中人性化设计的发展与定位,个性化设计由人性化设计开始及个性化设计可行性以及实现途径,说明创新设计的过程不仅仅是设计师借助技术和发挥想象力的过程,还是设计师与使用者不断沟通表达消费者需求的过程。设计师要充分考虑产品的使用群体的审美心理因素,满足用户的物质与精神需求,并在此前提下将个性和风格融入进去,为产品创新设计起到指导作用,从而提高产品吸引力和企业核心竞争力。     

The impact of consumer preferences on the accuracy of collaborative filtering recommender systems

Despite the omnipresent use of recommender systems in electronic markets, previous research has not analyzed how consumer preferences affect the accuracy of recommender systems. Markets, however, are characterized by a certain structure of consumers’ preferences. Consequently, it is not known in which markets recommender systems perform well. In this paper, we introduce a microeconomic model that allows a systematical analysis of different structures of consumers’ preferences. We develop a model-specific metric to measure the recommendation accuracy. We employ our model in a simulation to evaluate the impact of the structure of the consumers’ preferences on the accuracy of a popular collaborative filtering algorithm. Our study shows that recommendation accuracy is significantly affected by the similarity and number of consumer types and the distribution of consumers. The investigation reveals that in certain markets even random product recommendations outperform the collaborative filtering algorithm.     

A two-phased SEM-neural network approach for consumer preference analysis

A fundamental task in the design of consumer products is consumer preference analysis. The primary focus of this task is establishing a mapping relationship between product parameters/attributes and consumer preferences. The key to connect the consumer space and the design space are user perceptions of the product. Among the many existing methods, the Structural Equation Model (SEM) is one of the most used methods because it explains the causal relationship between the input and the output variables explicitly. However, the relationship obtained from the conventional SEM is linear, which is usually not the case in practice. Fortunately, the Artificial Neural Network (ANN) provides a new perspective for building nonlinear models because of its nonlinear nature. Therefore, a two-phased SEM-NN approach for consumer preference analysis is introduced for identifying and mapping how product attributes affecting the fulfillment of user perceptions and ultimately their preferences. In this model, the consumer preference analysis is conducted in two phases: influence path construction, and path coefficient revision. The proposed method can reserve the original SEM topology that reflects the causal relationship between variables while using the training algorithm of ANN to obtain more accurate path coefficients. This model could help the designers to identify and map how product attributes affecting the consumer preferences, and to better understand the factors that affect user perceptions and the inner relationships between them. To demonstrate effectiveness of the model, a case study of smartphone is presented. It is shown that the SEM-NN model can make full use of the causal analysis of SEM and the nonlinear nature of ANN and ultimately provides more reliable results of consumer preference analysis.     

Exploring the Relationship Between Green Consumption Value,Satisfaction, and Loyalty to Hybrid Car in Elderly Consumers

The study aimed to examine the relationship between green consumption value, satisfaction, and loyalty of driving hybrid cars among elderly consumers. Data were collected from a cross‐sectional survey of 314 elderly consumers who purchased hybrid cars in the United States. A partial least squares analysis revealed that elderly consumers’ social, price, and quality values positively influenced the satisfaction of their hybrid car experience, and their satisfaction significantly influenced their loyalty of hybrid car. The relationship between green consumption value, satisfaction, and loyalty toward driving hybrid cars among elderly consumers revealed insight into their value orientations toward the hybrid car. Special efforts are suggested in promoting hybrid car use to elderly consumer groups. Marketers should pay attention to changing beliefs and increasing perceived values of driving a hybrid car for consumers to encourage them to use green products.     

Crowd intelligence: Analyzing online product reviews for preference measurement

The proliferation of product review websites produces a large, publicly accessible information resource for firms that seek to understand consumers’ preferences. To facilitate product design or improvement, we propose a novel econometric preference measurement model, the modified ordered choice model (MOCM), to extract aggregate consumer preferences from online product reviews. Moreover, to categorize customer requirements on the basis of the aggregate consumer preferences estimated by the MOCM model, we extend the Kano model and propose a marginal effect-based Kano model (MEKM). We empirically evaluate the effectiveness of the proposed MOCM model and demonstrate the utility of the proposed MEKM model.     

感性消费的情感诉求和产品设计的战略

研究及把握消费者的感性消费心理,能注重了解客户,把握消费者心理需求从而推出适合于消费者的产品设计和战略。使设计出的产品是企业、社会和消费者真正需要的产品,不仅给企业、社会带来良好的经济效益,也会成为消费者真正解决生活中的问题与需求。     

A quantitative aesthetic measurement method for product appearance design

Product appearance is one of the crucial factors that influence consumers’ purchase decisions. The attractiveness of product appearance is mainly determined by the inherent aesthetics of the design composition related to the arrangement of visual design elements. Hence, it is critical to study and improve the arrangement of visual design elements for product appearance design. Strategies that apply aesthetic design principles to assist designers in effectively arranging visual design elements are widely acknowledged in both academia and industry. However, applying aesthetic design principles relies heavily on the designer’s perception and experience, while it is rather challenging for novice designers. Meanwhile, it is hard to measure and quantify design aesthetics in designing artefacts when designers refer to existing successful designs. In this regard, this study aims to introduce a method that assists designers in applying aesthetic design principles to improve the attractiveness of product appearance. Furthermore, formulas for aesthetic measurement based on aesthetic design principles are also developed, and it makes an early attempt to provide quantified aesthetic measurements of design artefacts. A case study on camera design was conducted to demonstrate the merits of the proposed method where the improved strategies for the camera appearance design offer insights for concept generation in product appearance design based on aesthetic design principles.     

浅谈基于情感的产品设计

信息时代的到来,人们越来越注重情感消费的需求。产品设计要在满足基本功能的情况下,充分研究目标消费群的情感特征,提炼出能和目标消费群产生美好情感共鸣的设计元素,运用到设计中,满足消费者的情感需求。     

Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective

With the popularity of social media, extracting consumer preferences from online consumer-generated content is of vital importance for product/service providers to develop tailored marketing strategies. However, existing approaches face difficulties analyzing consumer preferences over different attributes of alternatives (restaurants, hotels, etc.), which hinders product/service providers from comprehensively understanding consumer choice decisions. To address this issue, we solve for the consumer preferences over the attributes represented by attribute weights based on consumers’ historical data, including text comments and overall ratings. Specifically, for each comment and a corresponding rating, we first employ sentiment analysis to calculate values of the attributes, and then develop a quadratic programming model to solve for the weights. Based on a stream of a consumer’s text comments and overall ratings, we can correspondingly obtain a stream of weights indexed by the comment time. We then model this stream of weights as hesitant judgments and employ a hesitant multiplicative programming method to solve for the final weights that fit the consumer’s preferences over attributes at the highest satisficing level. In the application of recommendation, our approach not only provides insights into the consumer’s preferences but also has higher prediction power compared with some state-of-the-art methods.     

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.     

Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process

This paper focuses on the product diffusion in a competitive automobile market. Since purchasing a car is costly, the consumers in the market tend to behave like rational decision makers. They naturally compare the attributes of cars (e.g., brand preference, fuel economy, safety, comfort) and make overall decisions. In this paper, we propose an agent-based (AB) diffusion model consisting of tens of thousands of interacting agents. In the model, an agent represents a consumer and bases its multi-attribute decision-making on fuzzy TOPSIS. The decision-making process integrates three purchasing forces: expert’s product information provided by mass media, subjective weights on product attributes assigned by individual consumers, and social influence (i.e., information delivered from a consumer’s neighbors who have already adopted products). The AB model executes the agents and observes the collective behavior. In this sense, the model can assist in the analysis of the complex market dynamics. We conducted an empirical study to verify the performance of the AB model.     

Integrating aesthetic and emotional preferences in social robot design: An affective design approach with Kansei Engineering and Deep Convolutional Generative Adversarial Network

Recently, many companies have increasingly emphasized product appearance aesthetics and emotional preference-based design to enhance the competitiveness and popularity of their products. Identifying the interaction between product appearance and customer preferences and mining design information from the interacting context play essential roles in affect-related design approaches. However, due to the complexity of the aesthetic and emotional perception process, obtaining such design information from the interacting context is challenging. This paper proposes an affective design approach based on the Kansei engineering (KE) method and a deep convolutional generative adversarial network (DCGAN) following the research trend of merging KE with computer science techniques in recent years. A case study of the social robot design is conducted to verify the effectiveness of this approach. Appearance aesthetic and emotional preference evaluations are adopted by the KE method first to identify the crucial features in two categories: (1) The physical features of the outer shape, head and color for aesthetics; (2) The emotional features of intelligent, interesting and pleasant for preference perceptions. Based on a manually created social robot image dataset, the DCGAN model is trained to automatically generate novel design images. Then several professional designers are involved to fine-tune the generated images in detail. The experimental results show that the newly designed social robots tend to obtain positive aesthetic and preference evaluations. Practically, such an affective design approach can help industrial design companies identify customers’ psychological requirements and support designers in creating new products innovatively and efficiently.