Education is very important in realizing a strong and advanced nation. So, the process must be carried out early. Therefore, Early Childhood Education is the main foundation for the development of a ...nation's generation. While the standards of Early Childhood Education are not evenly distributed, in fact, there are still many that are lacking, especially in the learning media of Educational Game Tools/Alat Pembelajaran Edukatif (APE). In this research, an Educational Learning Tool will be made in the form of a puzzle using the Kansei words and Kensei engineering methods. The results of this study indicate that the child's response is very good and very helpful in the learning process. The combination of the Kansei words and Kansei engineering methods produces a unique educational game tool that makes children more interested in participating in learning
Designing a soccer shoe that fits specific customer’s requirements can improve satisfaction. However, there is no sufficient information to bridge the semantic needs and design characteristics of ...soccer shoes. Hence, this study is aimed at integrating multiple technologies with semantic customer requirements, shoe-form categories, and appearance designs, and developing a practical Kansei soccer shoe recommendation system. The psychological responses of a customer and perceptions of soccer shoe products are evaluated using Kansei engineering method. Based on a factor analysis, customers’ requirements are classified as aesthetic, functional, and comfortable. A total of 203 soccer shoe images were used to evaluate and categorise the external shoe form into nine design elements using the Kawakida Jirou method, and the weight assigned to each shoe-form category under Kansei semantic adjectives was determined using grey system theory. The quantification theory Type 1 method was used to determine the priority of the design elements. The results indicate that 10 pairs of semantic adjectives used by customers are related to soccer shoe forms. Furthermore, the design priority of each design element of the form category under the three types is reported. A soccer shoe recommendation system that can generate suggested soccer shoe samples for customers is developed by integrating the above-mentioned technologies. A validation experiment was conducted to verify the feasibility of the proposed system. The overall satisfaction of the recommended samples generated via the system is 87.08%, as reported by 80 participants. The findings prove that a new soccer shoe business model can be launched using the proposed system, linking the semantic customer requirements to the soccer shoe-form categories.
To enhance affective experience and customer satisfaction in the intelligent cockpit of new energy vehicle (NEV-IC), this article proposes a novel method that combines the visual sequence data of eye ...movements with the sentiment prediction using improved Long Short-Term Memory (LSTM). Specifically, we used eye-tracking technology to capture users' visual sequence of design morphology for NEV-IC. We then adopted entropy-TOPSIS to compute the ranking of morphological components based on experts’ opinions, establishing the coupling between users' visual perception and experts' opinion to obtain the key morphological dataset of NEV-IC based on user visual sequence. To tackle the shortcomings of LSTM, meanwhile, we employed the sparrow search algorithm (SSA) to optimize the hyperparameters of the LSTM model. Moreover, an attention mechanism has been introduced to address LSTM's difficulty in preserving key information when processing the sequential data, enabling a stronger focus on critical sequential features within the user's visual path. To assess the efficacy of the proposed SSA-LSTM-Attention model, a dataset incorporating user emotional imagery was constructed, within the research framework of Kansei engineering (KE). This dataset, in conjunction with the morphological dataset of visual sequential features, was applied to our model. The study results indicated that compared to traditional machine learning models like BP neural network (BPNN), support vector regression (SVR), and LSTM, our model performed better in capturing the nonlinear relationship between user sentiment and design features. Additionally, it exhibited higher predictive accuracy, better generalization ability and stronger robustness.
•User-generated comments were utilized for Kansei engineering on exterior design.•Deep learning natural language processing model was developed for scraped data.•Hybrid Apriori + SEM method was ...proposed for better prediction and interpretability.•Case study on 1805 automobiles, 287 brands, and 369105 comments was presented.•We found new energy and fuelled vehicle users have unique design requirements.
New energy vehicles (NEVs) such as electronic cars represent a major trend in the automobile industry, where most their exterior designs still follow those of convention fuelled vehicles (FVs). It is important to investigate whether NEV users have unique requirements that differ from those of traditional users. Kansei engineering is a practical tool for perceptual demand analysis. However, the conventional method requires questionnaires or surveys to perform limited data collection. In this study, we utilised massive internet data to collect user Kansei requirements for NEV exterior design. The Scrapy crawler was adopted for data collection and a bidirectional long short-term memory, conditional random field, and multilayer perceptron framework was developed for text mining. To quantify design features and Kansei image scores, a hybrid Apriori + structural equation model (SEM) system is proposed, where the data-driven Apriori algorithm can explore the hidden relationships in big user generated comments, while the SEM model captures the users’ behaviour and decision procedure so that to provide interpretable results. In addition, the association rules mined from user comments by Apriori can facilitate the specification of a complicated SEM model, substantially reducing the modelling and calibration effort. Goodness-of-fit results suggest that the proposed model outperforms conventional models. A case study on 1805 automobiles, 287 brands, and 369105 comments was conducted and the results suggest that some design features that would increase the Kansei image scores for conventional FVs may have the opposite effect on NEVs. Discussions on engineering and managerial insights are presented and the discovered rules and relationships are employed to develop a design-aided system.
Currently, the coffee business is becoming a trend in the market. So determining the right packaging design is an important thing to plan. The product development method that has been proven optimal ...is Kansei engineering. This study compares the results of developing ready-to-drink coffee packaging (cold and hot) with the Kansei engineering method based on consumer emotions. Kansei engineering is a powerfull method that can translate consumer needs into design elements. This method is supported by several multivariate statistical methods and artificial intelligence methods. The results obtained for a cold coffee drink are 25 packaged samples and 30 Kansei words, while 20 samples and 20 Kansei words for a hot coffee drink. The packaging design concepts for a cold coffee drink obtained from Kansei word extraction using the principal component analysis (PCA) method are "standard-attractive" and "unique-general." While the packaging design concepts for a hot coffee drink using the term frequency-inverse document frequency (TFIDF) method and factor analysis are "premium-modern" and "natural-elegant." based on the quantification theory type-1 (QTT1) method, the packaging design elements from the “unique-general” concept for a cold coffee drink are obtained, namely: wooden packaging lids with distinctive bottle mouths, The neck of the pack is straight and short, The packaging body resembles a sake bottle, the bottom of the pack is slightly concave in the middle, plastic packaging materials, and uninformative label design. While the “premium-modern" concepts for a hot coffee drink using the rough set method has packaging elements, namely, there is a sleeve or none at all, with a reclosable hole top, flat bottom, rounded/convex body, direct print decoration, and with decorative fonts. In contrast, the “natural-elegant” concept has packaging elements without reclosable holes, straight body, paper cup material, light colors, no image, and decorative fonts.the emotional interpretation of the object strongly influences the application of Kansei engineering.
Visual packaging attributes such as brand name, logo, color, packaging form, writing style and graphics are used for product identification and affect the buying decision (willing-to-buy). Framework ...systematic development of Kansei Engineering using Artificial Neural Networks, an imitation of the human brain used to modelling the learning process of customer assessments. This research aims to analyze the development of packaging designs of Excelsa Wonosalam Coffee based on Kansei Engineering. Kansei Engineering is widely used for developing product designs oriented to the Kansei (emotion) of the customer and relating them to product attributes. Data was obtained by conducting surveys of coffee customers and literature studies. The sampling technique used purposive sampling. The packaging design attributes are graphics, color and shape. The combination of Kansei Engineering and Artificial Neural Networks generate packaging design preferences obtained from the modelling of customer assessment of available attributes. Coffee packaging design preferences involve ringin contong graphic, red color, and squircle shape. These design preferences could be a consideration for coffee entrepreneurs for packaging design innovations. This research is expected to provide added value for industry and SMEs to create more attractive designs.
The user experience, often known as UX, is the key factor in determining a product success and increase user’s acquisition. However, this field of research lacks conceptual and practical models to ...follow when designing pervasive technologies such as mobile augmented reality (MAR). To convey a pleasant UX, it is necessary to identify the contributing factor and the components that influence the enhancement of the MAR design. The findings of the study indicated that emotions are the main factor that drives the user’s perception and hence, their choice and pleasure. This paper presents a preliminary model for designing an emotional UX mobile augmented reality application with the use of Kansei engineering approach. Ultimately, this model will provide insight into the design fundamentals that influence the user experiences. The outcomes of this study will assist researchers and designers in shaping the emotional user experience design.
Extant research on hotel open innovation rarely explores innovative ideas from customer-generated online reviews and pays little attention to consumers' affective needs. To bridge these gaps, this ...study aims to identify service innovation opportunities by mining online reviews from the perspective of customers' affective needs. Specifically, we adopt Kansei Engineering, an effective tool for extracting users' affective needs, to develop the research framework. The opportunity algorithm is also introduced to quantify the innovation opportunity levels of different service attributes. By analyzing 317,518 online reviews of luxury hotels crawled from Ctrip.com, we find that the service attributes with high innovation opportunity levels include Cleanliness, Facility, and Room attributes; the findings further reveal which innovative initiatives may trigger consumers' positive emotions, e.g., providing automated robot services, offering fragrant scents and ergonomic pillows/beds in hotel rooms, etc. This work advances hospitality open innovation research, and practical implications and methodological contributions are also discussed.
•A framework for exploring open innovation in hotel service from online reviews.•Identifies innovative opportunities from perspective of customer affective needs.•The proposed CBOW-KMeans can help efficiently discover hotel service attributes.•Provides attribute-level innovation opportunity and specific innovation strategies.
•A computerized method to automatically extract Kansei knowledge based on natural language processing has been proposed.•A Kansei knowledge tree of connections between product features and user ...perceptions has been drawn for emotional design.•The findings in this study could accelerate the traditional user survey process and clarify users’ emotional needs.•Suggestions to guide the adjustment of product design and assist the user-centered product emotional design were provided in this paper.
With the rapid development of the economy, product design has gradually shifted to emotional design that focuses on satisfying users’ emotional needs. Kansei engineering is the commonly used method in product emotional design, the first and vital stage of which needed to be addressed is the acquisition of Kansei knowledge. Considering the development of natural language processing technology and online shopping, a computerized method to extract Kansei knowledge from online product reviews is firstly proposed in this article, and a relational extraction method to establish the relationship between product features and user perceptions is further provided. This article analyzes and extracts the Kansei words of 10 mice respectively using the proposed computerized method, taking the mouse as the case study. Then three evaluation indicators including diversity, effectiveness, and concentration are defined to assess the method, which evaluates the superiority with the advantage of 19.03% in diversity, 6.91% in effectiveness, 22.18% in the concentration and 8.9 times higher in the total score compared with traditional method. Furthermore, taking the best-selling mouse for example, the relational extraction method is applied to extract the relationship between the user concern and the user attitude, establish the relational table, draw Kansei knowledge tree, and finally model connection between product features and user perceptions. By utilizing natural language processing technology and integrating Kansei engineering, linguistics and computer science, it could be considered that the results of this article can accelerate the traditional user survey process, clarify users’ emotional needs, guide the adjustment of product design, and assist the user-centered product emotional design.
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