Kansei Engineering Research Articles

Research on Design Strategy of Fashion Display Space Based on Kansei Engineering

Research on Design Strategy of Fashion Display Space Based on Kansei Engineering

Kansei engineering for the intelligent connected vehicle functions: An online and offline data mining approach

The big data era enables automakers to mine users’ affective (Kansei) requirements for the car design. However, existing literature mostly applies text mining with users’ online comments, possibly leading to biased results since users without online comments were not considered. To fill in this gap, this paper proposes to jointly analyse users’ online commenting and offline usage big data, and develops a novel framework to efficiently fuse these two datasets for the Kansei engineering of the intelligent connected vehicle (ICV) functions. A behaviour-enhanced large language model is proposed to process users’ online comments; then, users’ Kansei requirements are further jointly analysed with their offline in-cabin behaviour data, by the proposed NLP-MDCEV (natural language process — multiple discrete-continuous extreme value) model, to understand user’s complex discrete and continuous choice decisions in the smart cockpit. In addition, the proposed framework aims to solve the problem of design tasks prioritization, where not all the Kansei requirements can be met if design resources are limited. The proposed framework is applied in the studied new energy vehicle company, with more than nine-months’ online comments and six-months’ offline usage data, where results suggest its merits of economic, efficient, and effective.

Applying Kansei Engineering and Eye Movement to Packaging Design Elements—A Case of Tea Packaging Design

ABSTRACTExcellent tea packaging design wins customers and enhances branding. This report studies tea packaging design elements and gives designers constructive references to efficiently improve tea packaging design. The authors of this study first obtained consumers' perception of tea packaging through card sorting and semantic differential (SD). Second, the consumers' Kansei dimensions were extracted by Procrustes analysis (PA). Third, eye tracking technology thoroughly analysed the consumers' eye movement indexes. This technology analysed eye moment as the subjects were exposed to various packaging design elements in order to identify variance, key design elements and outliers.

Identifying Chinese students’ perceptions of smart classrooms and their relationships with design features

ABSTRACT 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.

Age-appropriate design of domestic intelligent medical products: An example of smart blood glucose detector for the elderly with AHP-QFD Joint KE

BackgroundIn response to the rise of intelligent products and the increasing prevalence of urban "empty nesters," we have developed a product specifically tailored for elderly diabetic patients. This product fulfils functional requirements and addresses stylistic preferences, contributing to the age-friendly evolution of home intelligent medical devices, particularly in intelligent blood glucose monitoring. MethodsOur approach commenced with a comprehensive user experience analysis to ascertain the needs of elderly users regarding home blood glucose meters. This involved constructing a hierarchy of user demands, followed by analysing and prioritising these needs. Utilizing Quality Function Deployment (QFD), we aligned user requirements with design specifications, identifying specific product functionalities and service design elements. Further, we employed Kansei Engineering (KE) to select sample designs that resonate with the concept of sensual imagery, leading to the derivation of specific modelling intentions. Combining these design elements, we proposed product design strategies and conducted practical case studies. The effectiveness of these designs was then assessed through fuzzy evaluation methods, allowing for user feedback. ResultsEmploying the Analytic Hierarchy Process for goal analysis, along with Quality Function Deployment theory and Kansei Engineering, we developed a home intelligent blood glucose meter catered to the elderly. This device not only meets its users' physiological and psychological needs but also provides an operationally convenient, health-conscious, and aesthetically pleasing experience. ConclusionsThis methodology enhances the age-appropriate design of home-based smart glucose monitors for the elderly, offering innovative insights and optimization strategies for designing elderly-centric smart medical products.

Evaluation of electric car styling based on analytic hierarchy process and Kansei engineering: A study on mainstream Chinese electric car brands

To evaluate the fuzzy and uncertain factors brought by human's emotional sensibilities during the design of new energy vehicle models, a scientific design evaluation method was adopted. This study combines the Analytic Hierarchy Process (AHP) and Kansei Engineering to assess the design of new energy vehicle models. It uses both objective criteria and subjective user perceptions. First, it quantified the design imagery vocabulary through Kansei Engineering and identified the target imagery vocabulary. Secondly, the AHP method was used to establish a relationship between design features and imagery vocabulary. Thrid, the study created an evaluation index system for design imagery, and adopted the fuzzy comprehensive evaluation matrix to rank the representative vehicles and brands in the Chinese new energy vehicle market. Based on the weight analysis results, the vehicle functionality (whether it is safe and reliable), the elegance of design, and the intricacy of vehicle structure were identified as the top three aspects that affect the impression of new energy vehicles. Therefore, we need to focus on these aspects during the design of new energy vehicles. Additionally, by integrating objective evaluation and emotional analysis results, we assessed the design for each new energy vehicle brand. The results of this study are important for developing the new energy vehicle market.

Aplikasi kansei engineering untuk desain kemasan kopi excelsa Wonosalam

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.

Applying kansei engineering and text mining to design fresh food logistics services

ABSTRACT The study focuses on identifying the key drivers that can meet consumers’ perceptual demands for fresh food logistics services and how e-retailers balance changing demands. In this light, a service quality improvement approach integrating Kansei engineering and text mining is proposed to span the property and semantic space. The data from the Chinese e-commerce platform is used as 288 service scenarios. Meanwhile, GeoDetector and Kano model are used to build models and find priorities of services. The results show that consumers’ demands are fine-grained and hierarchical. Fresh, fast, affordable, reliable and satisfied demands are the rational. Convenient, thoughtful, pleasant, persistent and surprised demands are the perceptual. Most logistics attributes have a ‘1 + 1 > 2’ effect on consumers’ perceptual demands during the emergency period, and exposure safety and logistics persistence are most significantly affected, having a ‘weak + weak = strong’ effect. Additionally, must-be, one-dimensional, and attractive quality based on the kano model provide more targeted strategies to improve the input-output utility.

Applying TRIZ and Kansei engineering to the eco-innovative product design towards waste recycling with latent Dirichlet allocation topic model analysis

Recycling waste is one of the main methods of sustainable design concept, which can reduce resource consumption and thus create eco-friendly products. Traditional green design has focused on the recycling of waste and the extension of the life cycle of product materials. Some scholars also consider emotional Customer Requirements (CRs) in eco-friendly products, but prioritize appearance over environmental function innovation. The rapid development of internet technology has enabled the proliferation of online product reviews, which can be analyzed to inform eco-innovative product design that meets CRs. Based on the CRs extracted from online reviews, this paper proposes an eco-innovative product design method that aims to recycle waste and meet emotional CRs. Firstly, online reviews are extracted using Latent Dirichlet Allocation (LDA), and CRs for environmental protection functions, appearance, human-computer interaction, and service experience are summarized by integrating sustainable design concepts and emotional design theories. Secondly, the Theory of Inventive Problem Solving (TRIZ) is utilized to suggest targeted solutions for core CRs, innovating product function modules and usage. Thirdly, the combination of Kansei Engineering (KE) and the Interactive Genetic Algorithm (IGA) is utilized for appearance design. Lastly, the research process is explained using the proposed method, with the example of a coffee maker's design, and its effectiveness and practicality are verified. The proposed method aims to recycle waste, optimize resource utilization, achieve environmental protection goals, and provide innovative approaches for advancing the circular economy. Additionally, this method prioritizes environmental sustainability and considers emotional CRs.

The Influence of the Public Lighting Environment on Local Residents’ Subjective Assessment

Sustainable development and energy savings are crucial to the significant worldwide trend in smart city-related research and projects. In this regard, public lighting systems have great energy-saving potential. Nevertheless, while citizen engagement is a key element of most conceptualisations of smart cities, many smart lighting projects and systems fail to take account of the citizen’s viewpoint. Applying a citizen-centric lighting design model, the objective of this study is to examine the affective impressions of local residents of the luminous environments in their areas, taking account of the activities they carry out there. Kansei Engineering is employed to connect luminous design elements with citizens’ affective responses. Lighting environments in 18 urban spaces were evaluated by 310 local residents. The results show that subjective assessments in the evaluation of urban lighting environments can be explained by the following dimensions: Expressive-interesting, Innovative-efficient, Defined-sufficient, Formal-uniform and Glaring. The relationship of these dimensions to urban social activities shows that public lighting should generate, in local residents, sensations consistent with the nature of the activities. Urban lighting must create in the citizen a feeling of innovation (being up-to-date and contemporary) if it is to be seen as energy-saving and caring for the environment. These findings may be valuable for governments, architects, engineers, and lighting designers when developing strategies to ensure their designs are evaluated as being efficient, sustainable, and environmentally friendly.

Designing feelings into lower-limb prostheses - A kansei engineering approach to understand lower-limb prosthetic cosmeses.

Introduction: This study aimed to quantify the relationship between prosthetic users' emotional response to prosthesis aesthetics and specific product properties. Methods: Words representing prosthesis users' emotional response (Kansei) to different aesthetic designs of prostheses were identified via interviews and mood boards. A group of experts consolidated the words into thematic groups, each represented by a single, high-level 'Kansei' word. 53 lower limb prosthesis users completed a questionnaire, rating their perception of 13 aesthetic designs using the 'Kansei' words. Quantification Theory Type 1 was applied to explore the relationship between words and product properties. Sub-analyses assessed for differences based on sex, age and level of extroversion. Results: 5 high-level Kansei words were identified ('Natural', 'Technological', 'Cool', 'Unique' and 'Functional'). The Kansei word 'Natural' had a strong association with realistic looking prostheses while the words 'Technological', 'Cool' and 'Unique' were strongly associated with expressive designs which incorporate hard, colourful covers. The word 'Functional' was not a reliable predictor of product properties. No major differences were observed within sub-grouped categories. Conclusion: Kansei words identified in this study can be used to help guide clients in their aesthetic design choices and to assist designers in achieving the desired response from their products.

Integrated triangular fuzzy KE-GRA-TOPSIS method for dynamic ranking of products of customers’ fuzzy Kansei preferences

As society evolves, companies produce more homogeneous products, shifting customers’ needs from functionality to emotions. Therefore, how quickly customers select products that meet their Kansei preferences has become a key concern. However, customer Kansei preferences vary from person to person and are ambiguous and uncertain, posing a challenge. To address this problem, this paper proposes a TF-KE-GRA-TOPSIS method that integrates triangular fuzzy Kansei engineering (TF-KE) with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, a Kansei evaluation system is constructed based on KE and fuzzy theory. A dynamic triangular fuzzy Kansei preference similarity decision matrix (TF-KPSDM) is defined to quantify customer satisfaction with fuzzy Kansei preferences. Secondly, dynamic objective weights are derived using Criteria Importance Though Intercrieria Correlation (CRITIC) and entropy, optimized through game theory to achieve superior combined weights. Thirdly, the GRA-TOPSIS method utilizes the TF-KPSDM and combined weights to rank products. Finally, taking the case of Kansei preference selection for electric bicycles, results indicate that the proposed method robustly avoids rank reversal and achieves greater accuracy than comparative models. This study can help companies dynamically recommend products to customers based on their Kansei preferences, increasing customer satisfaction and sales.

Evaluation of parametric sedan wheel hub based on Kansei Engineering and regression analysis

<p><strong>Purpose:</strong> Parametric design has become one of the most important means of sedan design. The purpose of this paper is to construct a linear regression model to explore the matching relationship between Kansei words and the morphological elements of sedan wheels and to incorporate perceptual factors into the rational thinking of parametric wheel hub design. <strong>Design/methodology/approach:</strong> To forecast the matching relationship between wheel shape design features and semantics, this research offers a multiple linear regression model. First, using sample similarity matrix data analysis and offline research, 20 typical samples of car wheels were collected. Third, the wheel shape design elements are obtained by applying the semantic difference (SD) method to four groups of users’ evaluation data on the perceived words of the car wheels. Firstly, the wheel shape is divided into six basic features and seventeen subdivided features using morphological analysis. The collected car wheel samples are then researched and coded. Ultimately, a multiple linear regression model is built utilizing shape coding matching to the styles to direct the wheel parametric design process, yielding sedan wheel design schemes with various semantics of style. <strong>Findings:</strong> The results show that the regression models can provide good prediction performance (R<sup>2</sup> values are greater than 0.7). This study shows that the use of multiple regression models can accurately and cost-effectively predict the wheel hub morphological elements that meet the user’s perceptual needs through morphological elements and Kansei words. <strong>Originality/value:</strong> This paper is the first to use a multiple regression model to predict a parametric wheel shape that fits the user’s sensibility, which helps the user to present and feel the design scheme in a digital environment. The different wheel hub schemes generated by parametric design can be virtually displayed in the metaverse, helping users and designers to carry out more convenient scheme selections.</p>

Innovative Design of Ecommerce Mobile Application Using Kansei Engineering and System Usability Scale

Information technology is currently growing at a rapid speed in order to provide users with convenience, one of which is the ease of accessing the internet via mobile phones. The emergence of social media programs such as virtual buddy applications has contributed to the rapid increase in internet users, particularly via mobile phones. Social media, in addition to being a virtual friendship application, also functions as a promotional medium, one of which is the promotion of online shopping applications, hence boosting the number of online purchasing transactions in Indonesia. An online shopping platform was chosen to market university’s products, allowing customers to shop easier and may increase the growth rapidly. As a result, Kansei Engineering is used in this study to develop applications that tap into users’ emotional demands. In addition, to establish the optimum design for the user, the prototype was tested for performance and usability using a System Usability Scale (SUS) questionnaire. According to the study’s findings, the application will be created with the following feelings in mind: appealing, attractive, organised, practical, innovative, quick, effective, accessible, interactive, and unique. According to usability studies, the “friendly concept” application generated better levels of satisfaction than the other design concepts.

A New Coordination Service Design Method for Government Website Considering the Kansei Needs and the Fairness of User Groups

The construction of smart government websites plays a vital role in government management. At present, the design of most government websites rarely considers the emotional needs of users and easily ignores the fairness among user groups. Kansei engineering (KE) is a tool that effectively establishes the relationship between design elements and users’ emotional needs. Therefore, based on KE, we propose a new coordination service design method for government website considering the Kansei needs and the fairness of user groups. In order to calculate the weights of the user groups, we depict the internal consistency of the user group through the evaluation of the Kansei attributes. Considering that user groups have different preferences for Kansei attributes, we construct the utility function of Kansei attributes based on the Kano model. By utilizing the user regret behavior, we further calculate the perceived utility of each alternative, which can comprehensively reflect user’s satisfaction. Then, in light of the evaluation differences between user groups and the opinions of the disadvantaged groups, we discuss these two situations and investigate the corresponding algorithm to determine the fairness of user groups. By combining fairness and perceived utility of user groups, we can select the optimal alternative in the design of government websites. Finally, an example is given to illustrate our proposed method and verify its effectiveness.

Growable design of passenger vehicle interior space based on FAHP and FQFD.

The increasingly shortened development cycle of smart vehicles has led to a qualitative shift in the nature of automotive products. Growing spatial design of vehicle interiors can effectively satisfy users' personalisation preferences and increase their willingness to buy, as well as mitigating the environmental pollution caused by the problem of rapid replacement. Considering the subjectivity and uncertainty of users' emotional needs, this study adopts the FAHP method to comprehensively analyse and rank the SET series of factors, then combines the grey correlation method with the correlation analysis of the areas related to the interior space of the automobile, constructs the sample of the interior space of the automobile and extracts the kansei words of the space sample. Intentional vocabulary mean scores were calculated to factor analyses through kansei engineering, next the fuzzy QFD quality house was built to make affective semantic design associations and derive design weights, which are then used to guide the design and ultimately realise the design of a dynamic automotive interaction scenario. The results of the study show that the integration of different theories can reduce the uncertainties in accessing users' emotional needs. At the same time, it can provide systematic guidance for the interaction design of a growable automobile in terms of multiple dimensions of interior space connectivity, spatial layout, and perceptual experience, as well as provide valuable suggestions for the subsequent development of interior spaces.

Optimization Strategies for the Modular Resource Construction of Art Gallery’s Exhibition Halls Based on Kansei Engineering

Optimization Strategies for the Modular Resource Construction of Art Gallery’s Exhibition Halls Based on Kansei Engineering

Research on Affective Ergonomics of User Preferences towards Train Compartment Design

Trains as public transportation have experienced quite significant passenger growth. One type of train that has just been developed is a compartment, a room to maintain passenger’s privacy during the trip. However, train design needs to pay more attention to passenger preferences. Uncovering this can be done using the Kansei Engineering method, which has successfully increased the value of products such as cars and others. Previously, research had been carried out with the results of favorite compartment designs with a minimalist theme, green color palette, decorative painting, glossy, and dominant wood motif finishing. However, this research has not addressed the affective side’s influence on design specifications more deeply. The research objective is to determine train compartment interior design specifications that suit user preferences and respondent clustering. This research will be carried out using a questionnaire with a semantic differential scale, which is tested using Principal Component Analysis (PCA) and Cluster Analysis. The research results show that user preferences influence the interior design specifications of the compartment and the favorite design samples chosen by most respondents. The results of this research can be a reference for train manufacturers and the general public.

Would boba and food characteristics impact the tasting experience? A Kansei Engineering study towards food mixed with boba

Would boba and food characteristics impact the tasting experience? A Kansei Engineering study towards food mixed with boba

Kansei Engineering Modelling of Intelligent Food Packaging for Beef Products Using Artificial Neural Network Optimisation

Kansei Engineering Modelling of Intelligent Food Packaging for Beef Products Using Artificial Neural Network Optimisation