Search

The protection and inheritance of regional cultural heritage are important for local economic development and artistic prosperity. Excellent cultural and creative products (CCPs) can better preserve and promote the unique allure of Liao Dynasty Pagoda culture. The CCP's current development process relies heavily on designers’ subjective experiences, which can lead to a disconnect between CCPs and user needs. This work proposes a Kansei-inspired product design approach for the Liao Dynasty Pagoda culture by integrating multimodal physiological signals and the interactive genetic algorithm (IGA). First, multimodal physiological signalling experiments are conducted to identify cultural elements that resonate with users. Second, Kansei words are extracted from big data to understand users’ emotional responses to CCPs. Factor analysis (FA) helps identify users’ pivotal Kansei needs, whereas the analytic hierarchy process (AHP) determines the relative weights of these needs. Finally, the designer creates the initial morphological features on the basis of the extracted cultural elements. The interactive genetic algorithm with hesitancy-based interval individual fitness (IGA-HIIF) is then employed to construct the morphological design evolution system. This system predicts the most user-satisfying combination of design features, allowing the designer to refine, model, and render the final design. [ABSTRACT FROM AUTHOR]

To deeply understand the psychological and emotional needs of the elderly population, this study proposes a seat design method combining Kansei engineering (KE), analytic hierarchy process (AHP), and quality function development (QFD). The method aims to fulfill the functional needs of the seat and focuses on capturing the emotional imagery of the elderly group, thus enhancing the emotional experience of the users. Factor analysis (FA) was used to conceptualize the user's perceptual vocabulary data, AHP to assess the relative importance of these perceptual words, and morphological analysis to deconstruct the characteristics of the seat components. Finally, the mapping relationship between user perceptual data and design features was established through QFD. The FA and AHP results showed that the emotional needs of the elderly for seating are primarily focused on the sense of lightness (32.5%), simplicity (45.7%), and sophistication (21.8%); and QFD analysis results show that among seating components, the No. 6 seat back, No. 1 seat cushion, No. 2 door post, and No. 2 seat leg have the highest importance and can best meet the emotional needs of elderly users. This design method effectively improves the product's suitability for the elderly and provides a valuable reference for related product design. [ABSTRACT FROM AUTHOR]

Purpose: This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design. Design/methodology/approach: This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research. Findings: At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends. Originality/value: This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design. [ABSTRACT FROM AUTHOR]

Humanoid robots, characterized by their anthropomorphic design, have become increasingly common in various service areas. Nevertheless, the majority of current affective designs of humanoid robots primarily concentrate on the physical appearance while overlooking its (audiovisual) integration with voice. In this study, we propose simultaneously designing the appearance and voice of humanoid robots using Kansei Engineering, an effective method for optimizing the affective design of products. We first selected representative humanoid robots with different appearances and voices and constructed kansei space to capture users' affective needs for these robots. Then, we decomposed appearances and parameterized voices to extract design features and orthogonalized these design features to generate prototypes. After that, we conducted an evaluation experiment to acquire users' affective evaluations on the combinations of appearance and voice. Based on the data, relationship models between design features and users' kansei images and holistic preferences were constructed using the back-propagation neural network. Furthermore, optimization design models were formulated and resolved through the genetic algorithm. Also, we conducted a validation experiment, and the results demonstrated that the optimized design schemes look harmonious in appearance, sound warmth in voice, and achieve a high level of audiovisual compatibility. The results suggest that the proposed approach can effectively optimize the audiovisual affective design of humanoid robot appearance and voice. Moreover, it can not only provide methodological support for the affective design of robots and other voice-based smart products but can also help to improve the affective experience quality and facilitate the application of robots in service areas. [ABSTRACT FROM AUTHOR]

Toddlers experience physical accidents in the house at a rate of 90 percent. Based on observations, the living room was the most dangerous part of the building compared to other parts. That condition made the parents feel that the safety of their little one was not fully supported. This study aims to design a living room that is safe and comfortable for Toddlers. The design output supports the interior designers in creating a safer Toddler environment. Kansei Engineering guides the identification of living room design for parents looking for their toddlers' safety. The Kansei words gathered from the study are comfortable, multifunction, safe, easy to clean, simple, large capacity, and harmonious. These Kansei words injected into the concept design, which is the harmonious design concept and the simple design concept, by using Principal Component Analysis. The harmonious design has a wood table (X5.1) with a sofa (X7.2) and TV (X9.1) and no flower vase (X11.2) as its decoration, while the simple design has a plastic table (X5.2) with a wood seat (X7.1) and with flower vase (X11.1) but no TV (X9.2). The insights gathered were designed with Blender 3D and integrated with virtual reality Oculus simulation through the SketchFab website.

Despite the promising prospects of blockchain technology, research remains primarily confined to the analysis-design phase, highlighting a gap in support for actual implementation. This study aims to develop a framework for assessing the implementation readiness of a blockchain-based traceability system, using the Kintamani coffee agroindustry as a case study. The research commenced with a structured literature analysis to create an assessment model and lay the groundwork for survey questionnaire distribution among potential users. Utilizing Kansei Engineering, the data were analyzed using quantification theory type I (QTT1) to identify the critical elements influencing users’ adoption intentions. The findings led to the proposition of a new framework integrating the assessment model with the system development life cycle (SDLC). Analysis of 59 articles revealed that TAM, TOE, DOI, and TRI are the predominant theories used to assess blockchain technology adoption. The resulting assessment model comprises three contexts, two characteristics with 14 variables, and analyzed data showing individual characteristics—particularly elements OPT1, INS3, and DIS1—as pivotal, evidenced by their high partial correlation coefficients of 0.92 108, 0.89 168, and 0.89 050, respectively. An integrated participatory development (IPD) approach was suggested to enhance system development and improve user quality of life, interaction, and communication effectiveness in the SDLC. This novel method proposed bolstering blockchain systems’ real-world implementation success by adapting development iterations to user needs. It is anticipated to apply to other case studies, offering broader implications. Future research could explore different data analysis methods or case studies to enhance these findings through sensitivity comparisons and bias analysis.

To deeply understand the psychological and emotional needs of the elderly population, this study proposes a seat design method combining Kansei engineering (KE), analytic hierarchy process (AHP), and quality function development (QFD). The method aims to fulfill the functional needs of the seat and focuses on capturing the emotional imagery of the elderly group, thus enhancing the emotional experience of the users. Factor analysis (FA) was used to conceptualize the user’s perceptual vocabulary data, AHP to assess the relative importance of these perceptual words, and morphological analysis to deconstruct the characteristics of the seat components. Finally, the mapping relationship between user perceptual data and design features was established through QFD. The FA and AHP results showed that the emotional needs of the elderly for seating are primarily focused on the sense of lightness (32.5%), simplicity (45.7%), and sophistication (21.8%); and QFD analysis results show that among seating components, the No. 6 seat back, No. 1 seat cushion, No. 2 door post, and No. 2 seat leg have the highest importance and can best meet the emotional needs of elderly users. This design method effectively improves the product’s suitability for the elderly and provides a valuable reference for related product design.

Ruang Normal Coffee UMKM implements online buying and selling in marketing their products, but these UMKM do not yet have distribution packaging to protect the product during delivery. Based on the field survey, the distribution problems were the coffee cup packaging being dented, leaking, difficult to carry, and unattractive. The distribution packaging is currently considered not following consumer preferences because it is only in the form of clear plastic. This study aims to design packaging for distributing MSME coffee drinks in the Normal Room according to consumer preferences. The method used is Kansei Engineering (KE). KE implementation is supported by decision-making methods in determining packaging design concepts and packaging design elements. The term frequency-inverse document frequency (TFIDF) and principal component analysis (PCA) methods determine the design concept. In contrast, the Rough Set method determines the design elements. Sampling of respondents using purposive sampling with a minimum number of 30 respondents. The results of this study were that there were 21-word pairs based on TFIDF extraction and 40 packaging samples. The design concept was obtained through PCA analysis of the two main components that have a variance value of 1, namely PC-1 produces the concept of "challenging-modern" and PC-2 produces the concept of "old-fashioned-practical." The combination of design elements from rough set analysis for the “challenging-modern” concept is cardboard/corrugated material, blunt end handles, folding cartons, tray/box type without additional features, sans-serif fonts, and illustrations. Meanwhile, the "old fashioned-practical" concept consists of kraft materials, round handles, sleeve shapes, inner stands or trays/boxes, pop-up designs, classic concepts, monochrome, and illustrations. The final step is implementing the design elements into the 3D packaging mockup model. This research has an impact on the development of science and technology in the packaging design process by considering consumer feelings using quantitative methods that are more objective

Background: User reviews of online shopping platforms can truly reflect users' feelings about the use of products. They have the advantages of large sample size, wide range and uniform distribution, and can help optimize the perceptual model of product design. Methods: The web crawler crawls user comments, and TFIDF-SP quantifies them. The principal component analysis method selects the perceptual evaluation index, the morphological analysis method disassembles the product into several main structures, and the BP neural network constructs the perceptual optimization model. Results: Taking the paint tray as an example, according to the trained BP neural network, the shape factor combination with the highest perceptual evaluation value is predicted. The experimental results verify the accuracy of the model. Conclusion: The model based on BP neural network has the ability to quickly and accurately combine the best form factors, improves the design efficiency of product design perceptual optimization, improves the rationality of product design, and provides a new idea for consumer demand market-oriented product design. [ABSTRACT FROM AUTHOR]

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users' outdoor activity duration preferences and emotional experiences. OMS, as a key element in modern urban design, significantly enhances residents' quality of life and promotes public health. Accurately understanding and predicting users' emotional needs is the core challenge in optimizing OMS. In this study, the Kansei Engineering (KE) framework is applied, using fuzzy clustering to reduce the dimensionality of emotional descriptors, while RST is employed for attribute reduction to select five key design features that influence users' emotions. Subsequently, the PSO-SVR model is applied to establish the nonlinear mapping relationship between these design features and users' emotions, predicting the optimal configuration of OMS design. The results indicate that the optimized OMS design significantly enhances users' intention to stay in the space, as reflected by higher ratings for emotional descriptors and increased preferences for longer outdoor activity duration, all exceeding the median score of the scale. Additionally, comparative analysis shows that the PSO-SVR model outperforms traditional methods (e.g., BPNN, RF, and SVR) in terms of accuracy and generalization for predictions. These findings demonstrate that the proposed method effectively improves the emotional performance of OMS design and offers a solid optimization framework along with practical guidance for future urban public space design. The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users' emotional needs into the design process. The method contributes new knowledge to the field of urban design, promotes public health and well-being, and provides a solid foundation for future applications in different urban environments. [ABSTRACT FROM AUTHOR]

Red sour soup (RSS) is a traditional food with rich cultural connotations and nutritional value, unique to the Kaili region of Guizhou Province, China. However, the existing packaging design lacks cultural characteristics and visual appeal, which greatly limits its market potential. This study proposes a systematic research framework for RSS packaging design by integrating NLP, Kansei engineering, and semiotics. First, we mined consumers' dual requirements by analyzing online reviews from e-commerce and tourism platforms using NLP and LDA. Second, we used Kansei engineering to construct a mapping model between consumers' requirements and design elements. Notably, in the semantic space, we innovatively introduced the concept of a semantic network, considering the relationships between Kansei words for the first time. Finally, we proposed integrating the four dimensions of semiotics into the four stages of design, guiding the application of batik culture in packaging design. Based on this framework, we created a set of RSS packaging designs that integrate modern design concepts and traditional Miao cultural elements. The new design showed significant attractiveness in testing, with 100% of subjects preferring it, fully demonstrating the effectiveness and consumer acceptance of our approach. This study provides new methods and ideas for packaging in the food industry, which has positive significance for the modernized marketing of traditional foods. [ABSTRACT FROM AUTHOR]

Jiangyong Women's Book contains rich folk traditions and aesthetic ideas, and by tapping into its cultural genes and exploring its use in furniture design, it is possible to realize the mingling of tradition and modernity and enhance cultural memory. Firstly, the Jiangyong Women's Book was analyzed and the gene map was constructed under the research framework of perceptual engineering, the questionnaire survey was adopted to evaluate the representative samples of Jiangyong Women's Book Cultural and Creative Furniture to obtain the perceptual vocabulary through clustering analysis. Then, the attribute simplification and importance degree of the collected perceptual vocabulary were calculated through the rough set theory of RST to arrive at the core vocabulary. In the end, the quality function deployment (QFD) tool was adopted to translate the user's perceptual imagery into the design elements of the Jiangyong Women's Book Cultural and Creative Furniture to form a design proposal. In summary, the integration of cultural genes into furniture design for imagery design can strengthen its cultural characteristics, and also provided a design path that can be drawn upon for furniture innovation design. [ABSTRACT FROM AUTHOR]

Accurately grasping users' Kansei needs and rapidly transforming them into product design solutions are key factors in enhancing product competitiveness and sustainability. This paper proposes a product appearance design method based on Kansei engineering and AI image generation technology, integrating other approaches, with household indoor hydroponics as the research subject. First, the web crawler is used to obtain product image samples and user online reviews, and factor analysis (FA) is applied to quickly extract users' Kansei needs. Second, product morphology is used to deconstruct and encode product appearances. Partial least squares regression (PLSR) is then employed to map and quantify the relationships between Kansei needs and design elements, yielding optimal design solutions and one-dimensional sketches. These sketches are subsequently used as controlled conditions in Stable Diffusion (SD), combined with a team-trained Lora model, to generate two-dimensional colored sketches in batches. Finally, evaluations verify that the generated design solutions are satisfactory and meet users' Kansei needs. The results indicate that the proposed product appearance design method not only holds significant implications for the sustainable development of Kansei engineering in product design but also greatly enhances the efficiency of the design process, providing new insights into integrating new technologies and scientific research methods in the field of product design. [ABSTRACT FROM AUTHOR]

This study aims to develop a design process model within the scope of the Design Thinking approach with Kansei Engineering support and to experientially apply this model. Additionally, another aim of the research is to investigate the affective/emotional preferences of female users regarding visual and functional aspects of outerwear designs, and subsequently develop alternative design model proposals in alignment with users' preferences. In this study, the 'Stanford d.school Design Thinking Model' was utilized. Research data was obtained through a survey consisting of two sections, involving the opinions of female consumers aged between 18 and 60 residing in Istanbul. The first section includes a Likert-type scale to examine users' outerwear design preferences. In the second section, a semantic differential scale was prepared to evaluate jacket, coat, and overcoat designs using Kansei words. The reliability of the measurement instrument was established through calculated Cronbach's Alpha coefficients, presented after each scale, confirming the reliability of both scales for this sample. The Kansei evaluation shows that the Design Thinking process model makes it possible to create designs that meet users' emotional needs. [ABSTRACT FROM AUTHOR]

Taking classes in a smart classroom is a novel experience for most college students in China, and comprehensive research on how Chinese students perceive smart classroom environments is limited. This study explored students' perceptions of smart classroom environments in plain language through a survey involving 358 students. The semantic difference and Kansei Engineering methods were employed to investigate students' perceptions of learning and physical environments and their relationships with the design features of smart classrooms. Statistical analysis revealed six factors of subjective assessments with a total interpretation rate of 66.29% and five design features of smart classrooms with a total interpretation rate of 59.40%. No significant differences were found across all the factors based on gender, but there were significant differences in students' technology self-efficacy according to each factor of subjective evaluation and design feature. All factors of the subjective assessments and design features exhibited significant correlations. Furthermore, regression models revealed a significant relationship between affective perception and other dimensions of subjective assessments and an important connection between the active learning environment and all design features. Thus, this study provides an empirical foundation for understanding students' perceptions of smart classrooms and offers a user-based perspective for designing smart classrooms. [ABSTRACT FROM AUTHOR]

With people's concern for health and the development of medical technology, medical products for children are gradually appearing in pharmacies and online stores. However, the appearance of most children's household medical products tends to meet the needs of adults, which leads to low acceptance of medical products. This study aimed to explore 10- to 16-year-old children's visual and tactile perception of different materials by researching the relationship between the psychological quantities of visual and tactile perception and the physical quantities of the material surface. Based on the theory of kansei engineering, we measured the physical quantities of nine materials used in insulin syringe products and administered a perceptual questionnaire test for children. By correlating subjective perceptions with the physical attributes of the materials' surfaces, we determined a strong correlation between the visual and tactile psychological properties and the properties of the materials' surfaces. Children clearly perceive materials, and materials with lower roughness can elicit calmness, while materials with higher gloss elicit negative emotions. This paper establishes an evaluation model and provides a scientific selection method for surface materials in different children's household medical products. [ABSTRACT FROM AUTHOR]

Existing studies have recognized the significance of ambiance in influencing tourist behavior and preferences. However, little attention has been given to exploring tourist preferences for specific ambiances throughout the day. This study aims to fill this gap by examining whether temporal preferences for ambiance exist in tourism. We used the Kansei Engineering method to identify the specific ambiance tourists expect at different times of day: morning, afternoon, and evening. A survey of 200 domestic tourists in Yogyakarta, Indonesia, was conducted using semantic differential questionnaires. The results suggest five distinct constructs of ambiance that reflect tourist expectations at different times of the day. The results confirm the existence of temporal preferences for ambiance across the day. Specifically, tourists visiting Yogyakarta prefer peaceful, nature-centric attractions in the morning, cultural experiences and popular places in the afternoon, and romantic ambiances in the evening. Further analysis reveals that this pattern aligns with natural circadian rhythms. This study provides valuable insights for stakeholders to improve tourist experiences by aligning offerings with these temporal preferences.

Visual appearance (shapes, colors, materials, and surfaces) applied to product design can provide users with emotional and positive affective responses. The desire of such users will provide essential guidelines for companies to develop products, in this case, trains, in the form of private spaces called compartments. Trains are in great demand for long trips, so there is a need for market segmentation. However, current research only focuses on designs that examine functionality needs. Lack of study of design based on visual appearance, research on user preferences, and more scientific evaluation of design is needed. Therefore, this study aims to translate the user's affective response into the interior design specifications of train compartments with the Kansei Engineering method. This research consists of 5 steps: (1) Determining product semantics (Kansei Words assignment); (2) Define product properties (items and categories), as many as eight design samples; (3) Distributing questionnaires to 150 respondents (75 men and 75 women); (4) Data analysis with multivariate statistics, KMO, Barlett's Test, Principal Component Analysis, and clustering of respondents; (5) Evaluate the results of the most optimal design specifications. The results of this study obtained design recommendations: straight shape, studio green color, pine wood HPL material texture, and doff surface. In addition, several user clusters were formed based on gender, age, and monthly income to segment train compartments when commercialized. This research is expected to be helpful for the wider community and the development of the interior design of train compartments. ABSTRAK: Penampilan visual (bentuk, warna, bahan dan permukaan) yang digunakan pada reka bentuk produk dapat memberikan tindak balas emosi dan tindak balas afektif positif daripada pengguna. Keinginan pengguna akan memberikan garis panduan penting bagi syarikat untuk membangunkan produk, dalam kes ini kereta api dalam bentuk ruang peribadi yang dipanggil petak ruang kereta api. Kereta api mendapat permintaan yang tinggi bagi perjalanan jauh, jadi terdapat keperluan untuk pembahagian pasaran. Walau bagaimanapun, penyelidikan semasa hanya memberi tumpuan kepada reka bentuk yang mengkaji keperluan fungsi sahaja. Terdapat kekurangan kajian mengenai reka bentuk berdasarkan penampilan visual, penyelidikan mengenai keutamaan pengguna, dan penilaian reka bentuk yang lebih saintifik diperlukan. Oleh itu, kajian ini bertujuan untuk menterjemahkan tindak balas afektif pengguna ke dalam spesifikasi reka bentuk dalaman petak ruang kereta api menggunakan kaedah Kansei Engineering. Penyelidikan ini terdiri daripada 5 langkah: (1) Menentukan semantik produk (penentuan patah kata Kansei); (2) Tentukan sifat produk (item dan kategori), sebanyak lapan sampel reka bentuk; (3) Mengagihkan soal selidik kepada 150 responden (75 lelaki dan 75 perempuan); (4) Analisis data dengan statistik multivarian, KMO, Ujian Bartlett, analisis komponen utama, dan kelompok responden; (5) Penilaian keputusan spesifikasi reka bentuk paling optimum. Hasil kajian mencadangkan reka bentuk: bentuk lurus, warna hijau studio, tekstur bahan HPL kayu pain, dan permukaan doff. Di samping itu, beberapa kluster pengguna dibentuk berdasarkan jantina, umur dan pendapatan bulanan bagi memudahkan pembahagian petak ruang kereta api apabila dikomersialkan. Kajian ini dijangka berguna kepada masyarakat yang lebih luas dan pembangunan reka bentuk dalaman petak ruang kereta api pada masa hadapan.

This paper proposes a furniture design method combining Kansei engineering (KE) and shape grammar (SG), to explore how the diagonal ridge elements of classical Suzhou-style buildings can be applied to furniture design and to explain how the styling elements match the cultural imagery in furniture products. Suzhou-style armchairs and cultural elements of diagonal ridges were collected, the most suitable armchairs were selected for incorporating such elements, and their shapes were deconstructed along with characteristic interpretations. A factor bank of diagonal ridge elements was constructed, first through Kansei word selection and evaluation experiments, and then through factor analysis which determined the main cultural elements of the design. The shape grammar theory was applied to design and innovate the selected armchair samples, achieving three design solutions. The solutions were then comprehensively evaluated, and the optimal one was used for the final physical product. The results of the study showed that users had clear subjective feelings about the design incorporating diagonal ridge elements, with their Kansei on three aspects: aesthetic style, decorative complexity, and structural balance. The approach used in this work blends furniture products with cultural imagery on diagonal ridges, providing a feasible methodological reference and an empirical case for cultural sustainability through furniture design.

Psychological and physiological cognitive measurements were combined with Kansei engineering theory to study the shape of the traditional Chinese official-hat chair, a kind of Ming-style chairs, while investigating people’s perceptual cognitive process. This work employed focus groups to obtain 5 groups of representative words and 22 typical samples for evaluation experiments. After data preprocessing, principal component analysis (PCA) was used to extract three main components: "Concise-Ornate", "Soft-Strong", and "Elegant-Vulgar". In terms of component weight calculation, the analytic hierarchy process (AHP) was initially used to calculate the subjective cognitive weight of different components, and then eye-tracking technology was used to assist in calculating the physiological cognitive weight. Finally, combining the subjective and objective weights, the weights of the components were determined as: backrest (41.02%) > stretcher (18.29%) > handrail (11.64%) > top rail (11.14%) > outside stick (8.64%) > inside stick (5.32%) > foot rail (3.95%). The four main components—backrest, stretcher, handrail, and top rail—along with perceptual image evaluations, were selected to establish a multivariate linear regression equation, thereby constructing a mathematical mapping relationship between perceptual image and main design elements. This provides theoretical support for designers in creating different perceptual images.

To enhance the scientific rigor of design decisions and develop new rosewood furniture that aligns with user emotions, this study integrates the strengths of the Hesitant Fuzzy Analytic Hierarchy Process (HFAHP) and Hesitant Fuzzy Quality Function Deployment (HFQFD) within the framework of Kansei Engineering (KE). This method accurately translates Consumer Requirements (CRs) into Engineering Characteristics (ECs). First, the KJ Method was used to screen and categorize Kansei words, create product sample images, and deconstruct the form of rosewood furniture using morphological analysis. Second, after collecting valid questionnaires using a 7-point Likert scale, Factor Analysis (FA) was employed to extract three key Kansei factors. Third, HFAHP was utilized to calculate the weights of the Kansei words. Fourth, HFQFD was applied to construct a hesitant fuzzy correlation matrix between CRs and ECs, determining the priority of design elements for rosewood furniture. Finally, using a square table as an example in the design practice, the optimal Scheme No. 9, which highly meets consumer emotional needs and features harmonious form combinations, was selected. This study enhances the emotional value of rosewood furniture, optimizes the design decision-making process, and improves contemporary consumer satisfaction.

The purpose of this study was to produce a sago rice packaging design that suits consumer preferences using the Kansei Engineering method. This study employed a survey method and quantitative analysis. The Kansei words selected in this study included communicative, transparent, informative, practical, safe, brightly colored, attractive, innovative, size-appropriate, unique, strong, creative, distinctive, patterned, and protective of the product (15 words in total). The design elements and sub-elements identified from 10 samples of analog rice packaging were packaging materials (aluminum foil and plastic), labels (digital printing and stickers), packaging forms (standing pouches, side gusset pouches, and sachets), and colors (many colors (>3 colors), few colors (2-3 colors), and plain). The packaging design desired by consumers, based on conjoint analysis, reflected the Kansei words practical, attractive, innovative, unique, creative, distinctive, and patterned. Consumers preferred packaging made of aluminum foil, labeled with digital printing, in the form of a standing pouch, and featuring many colors (>3 colors).

Smart electric vehicles (SEVs) hold significant potential in alleviating the energy crisis and environmental pollution. The augmented reality (AR) dashboard, a key feature of SEVs, is attracting considerable attention due to its ability to enhance driving safety and user experience through real-time, intuitive driving information. This study innovatively integrates Kansei engineering, factor analysis, fuzzy systems theory, analytic hierarchy process, grey relational analysis, and factorial experimentation to evaluate AR dashboards' visual imagery and subjective preferences. The findings reveal that designs featuring blue planar and unconventional-shaped dials exhibit the best performance in terms of visual imagery. Subsequent factorial experiments confirmed these results, showing that drivers most favor blue-dominant designs. Furthermore, in unconventional-shaped dial designs, the visual effect of vertical 3D is more popular with drivers than horizontal 3D, while the opposite is true in round dials. This study provides a scientific evaluation method for assessing the emotional experience of AR dashboard interfaces. Additionally, these findings will help reduce the subjectivity in interface design and enhance the overall competitiveness of SEV vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the current trend of social aging, the travel needs of the elderly are increasingly prominent. As a means of urban transportation, low-speed new energy vehicles (NEVs) are widely used among the elderly. Many studies are devoted to exploring the function of cars and the travel modes that meet the needs of older people. However, in addition to product performance, the Kansei needs of users also play a key role in communication between enterprises and users. Therefore, the problem of how to improve car shapes in the initial stage of design to meet the Kansei needs of elderly users remains to be solved. In order to fill this gap, the design of low-speed NEVs are selected as the study objects so as to explore the relationship between the visual perception of elderly users and car design; thus, a design method for the form of elderly-oriented cars is proposed. Firstly, using the research framework of Kansei engineering, factor analysis is used to cluster elderly-oriented Kansei factors. Second, the cars' appearances are deconstructed by morphological analysis, and the key design features affecting elderly-oriented satisfaction are identified by a rough set attribute reduction algorithm. Finally, support vector regression is used to establish a mapping model of elderly-oriented Kansei factors and the key design features to predict the elderly-oriented form design of optimal low-speed NEVs. The research results show that selecting "Hub6", "Headlight9", "Car side view2", "Rearview mirror9", and "Front door10" in the form deconstruction table for low-speed NEVs can elicit optimal emotions in elderly users. The research results enable enterprises to more effectively understand the emotional cognition of elderly users related to the form of low-speed NEVs and improve the purchase desire and satisfaction of elderly users, providing references and guidance for the elderly-oriented design and development of intelligent transportation tools. [ABSTRACT FROM AUTHOR]

Under the promotion of combined medical and elderly care (CMEC) policies, the market demand for elder-friendly smart medical products as convenient intelligent healthcare devices is growing. However, most studies on elderly-friendly smart medical products focus on functional enhancement and cost control, and there is a lack of research on the sustainable innovative design of elder-friendly smart medical products from the perspective of elderly emotional needs. Therefore, this paper proposes an integrated framework based on the fuzzy Kano model, Kansei engineering, and theory of inventive problem solving (TRIZ) for mapping the complex and dynamic emotional needs of the elderly to product design parameters to produce innovative solutions, ensuring the sustainability of the design process and the innovativeness of the design solutions of elder-friendly smart medical devices. We verified the effectiveness and applicability of this integrated framework through a case study involving sustainable innovation design of a smart blood pressure device. The results of this study are of considerable theoretical and practical significance for promoting the development of the market for elder-friendly smart medical products under the policy of CMEC, meeting the needs of the elderly for healthcare devices and improving their quality of life. [ABSTRACT FROM AUTHOR]

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users' needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users' needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences. [ABSTRACT FROM AUTHOR]

It is crucial to understand and meet the multi-dimensional affective image needs of users for product form in a user demand-oriented product development model. Multi-objective evolutionary algorithms based on decomposition will be introduced into the field of kansei engineering to carry out research on product form optimisation design based on multi-objective evolutionary algorithms. A constrained multi-objective discrete optimisation model was established using the kansei engineering prediction model constructed through machine learning techniques as the objective function, and a reference vector guided evolutionary algorithm was used to solve it. The superiority of this method was verified by comparing it with other commonly used solving methods in this field. Combining entropy weight method and TOPSIS, select the optimisation design scheme that best meets the multi-dimensional affective needs of users from the obtained pareto set. Taking the train as an example, the proposed method was explained. The results indicate that the optimisation scheme obtained by this method can achieve the improvement and optimisation of product form in multiple affective dimensions. Meanwhile, a comparative study on the applicability of multi-objective evolutionary algorithms in the form optimisation problem of different affective dimensions is carried out to provide reference and suggestions for subsequent product design research. [ABSTRACT FROM AUTHOR]

In order to explore consumers' perceptual perceptions of Hanfu collar and lapel styling, 20 Hanfu collar and lapel styling were selected, 8 pairs of perceptual adjectives were collected using the semantic differential method, and a questionnaire was created using a Likert scale to collect consumers' perceptual evaluations of the Hanfu collar and lapel samples, and the results of the questionnaires were subjected to mean, factor, and cluster analysis using SPSS 29. 0. The results show that there are some differences in the perceptual evaluation of Hanfu collar and lapel shapes due to the different Hanfu collar and lapel shapes. Consumers' perceptual evaluations of 20 Hanfu collar and lapel shapes are mainly affected by the style factor and temperament factor, and can be categorized into three classes by the method of K-mean clustering. Different clusters of Hanfu collar and lapel styling have different perceptual evaluation characteristics. [ABSTRACT FROM AUTHOR]

Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers' emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE's flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei's validity and the proposed solution's robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework. [ABSTRACT FROM AUTHOR]

Copyright of Revista Entramado is the property of Universidad Libre Seccional Cali and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Kansei engineering is a critical method for designing products that meet functionality, usability, and pleasurability, essential elements for business success. Despite its significance, there is limited understanding of how this method has evolved in recent years. This study aims to analyze the development of Kansei Engineering research from 2018 to 2022 using the Scopus database. The research methodology involved stages of identification, screening, filtering, and inclusion, resulting in 41 articles for detailed analysis out of an initial 215. The results indicate that 85% of Kansei Engineering research focuses on tangible products, with 83% categorized as type 1 studies, and 56% not integrating other methods. Additionally, 88% of the studies use only Kansei words, and 41% visualize design results as 3D images, with 95% not considering unique aspects. This dominance of tangible product design and the lack of integration with other methods suggest a need for diversification in research approaches. Furthermore, the high reliance on Kansei words and 3D visualizations points to a potential area for innovation and expansion in research techniques. This review highlights a significant research gap in Kansei Engineering studies, emphasizing the need for more diversified approaches. By identifying these gaps, the study provides a clear direction for future research, recommending that Kansei Engineering should explore beyond the predominant trends and consider integrating with other methods and unique aspects. This can enhance the method's application in industrial engineering and lead to more comprehensive and innovative product designs. Future research should aim to fill these gaps, ensuring that Kansei Engineering continues to evolve and contribute effectively to the field of product design and development. [ABSTRACT FROM AUTHOR]