Abstract
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.
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Integrating Kansei Engineering with Hesitant Fuzzy Quality Function Deployment for Rosewood Furniture Design
Lei Fu, Yiling Lei, Ling Zhu, Yuqi Yan, and Jiufang Lv *
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.
DOI: 10.15376/biores.19.3.6403-6426
Keywords: Rosewood furniture; Furniture design; Hesitant fuzzy analytic hierarchy process; Hesitant fuzzy quality function deployment; Kansei engineering
Contact information: College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China; *Corresponding author: lvjiufang8189@njfu.edu.cn
INTRODUCTION
Rosewood furniture denotes traditional Chinese furniture crafted from high-quality hardwoods such as Pterocarpus erinaceus Poir. and Cassia siamea Lam.. Since the Ming and Qing dynasties, rosewood furniture has sustained popularity, constituting an integral facet of the contemporary Chinese solid wood furniture market. The raw material for rosewood furniture, identified as “Hongmu” in Mandarin, possesses commendable attributes encompassing hardness, color, and texture, thereby enhancing its economic, practical, and aesthetic worth. Nevertheless, in the contemporary context, the product development process of rosewood furniture encounters specific challenges.
Investigations elucidate that the widespread adoption of semi-automated workshops in recent years has engendered a high degree of maturity and advancement in the functionality, structure, and manufacturing processes of rosewood furniture. Discernible differences in performance, quality, and materials among furniture products from various manufacturers have notably diminished. Concurrently, within the prevailing era of the aesthetic economy, the aesthetic qualities of commodities exert influence on consumer perceptions during both the transactional and utilization phases (Böhme 2003). Consumers are increasingly concerned with the psychological impact of a product’s appearance (Demirbilek and Sener 2003). The explicit design features of a product directly influence the user’s first impression (Norman 2004). A good appearance is a key determinant of a product’s success in the market (Bloch 1995). Consequently, today, designers should pay more attention to the aesthetic impact of products (Lu and Hsiao 2022). The home furnishings industry increasingly prioritizes sensory experience design (Huang et al. 2023). However, the imperative for expeditious profitability by numerous rosewood furniture enterprises has resulted in a design decision-making process marked by a dearth of systematicity and precision. The subjective creativity of designers makes it challenging to accurately align the furniture’s form with the emotional preferences of modern consumers, leading to issues of mismatched supply and demand and wasted resources.
In recent years, Kansei Engineering (KE) has gained widespread application in design as a systematic approach utilizing engineering techniques to analyze the intricate relationship between human emotions and product features. Prominent companies, such as Mazda, Toyota, Canon, and Samsung, have effectively integrated KE into the early stages of new product development, resulting in notable success in the market. The fundamental aim of KE is to convert user emotional needs into discernible product engineering characteristics, thereby facilitating design and development teams in the creation of innovative products closely aligned with consumer emotions. Consequently, this study seeks to employ the KE framework to systematize the design decision-making process of rosewood furniture. A systematic review of prior research uncovers certain deficiencies in existing KE methodologies. On the one hand, the correlation between CRs and ECs is key to KE’s ability to transition from the user side to the design side. Specifically, the amalgamation of Cluster Analysis (CA) and T-test for correlating Kansei words with product samples is noted (Yang et al. 2023c). However, this methodology yields only a generalized directional correlation, lacking the establishment of a one-to-one correspondence between user requirements and design features. The resultant analysis is susceptible to interference from irrelevant design elements within the system. While Quantitative Theory Type I (QTT-I) (Hsiao et al. 2010) has been applied for correlating sensory semantics with design elements, its affiliation with multivariate linear regression analysis poses limitations, given that human subconscious thinking does not consistently adhere to linear characteristics (Nagamachi et al. 2006). The application of QTT-I to predict subjective human emotions may introduce biases in experimental outcomes, with the associated complex and time-consuming calculation processes. In response to these challenges, the Backpropagation Neural Network (BPNN) (Woo et al. 2022; Chen and Bian 2024) has been employed to construct a nonlinear mapping model. However, this method heavily depends on sample data, rendering it vulnerable to local minima and lacking a unified network selection method, potentially compromising result predictability. Notably, Fuzzy Quality Function Deployment (FQFD) (Kang and Nagasawa 2023; Wang and Yang 2023) has been proposed in recent years for mapping models between consumer requirements (CRs) and engineering characteristics (ECs). Despite offering user-centered advantages by incorporating triangular fuzzy numbers to align with the inherent fuzziness and non-linear thinking in decision-makers’ evaluation processes, FQFD falls short in capturing hesitations in decision-making processes involving multiple experts. In contrast, Hesitant Fuzzy Quality Function Deployment (HFQFD) (Onar et al. 2016), as the latest extension of traditional QFD (Chen and Sun 2023) and fuzzy QFD, integrates the theory of Hesitant Fuzzy Sets (HFSs). Torra (2010) proposed HFSs due to the difficulty in determining the membership of elements, a challenge that arises from decision-makers’ hesitation in identifying the correct value among possible options. Noteworthy for its comprehensive and meticulous depiction of decision-makers’ hesitations, HFQFD mitigates information loss and enhances correlation accuracy and design quality in comparison to other methods. Consequently, this study opts for HFQFD to construct a hesitant fuzzy correlation matrix.
On the other hand, for the acquisition of precise consumer demands, certain scholars (Yang et al. 2023a) have combined the Analytic Hierarchy Process (AHP) with Factor Analysis (FA) to discern pivotal Kansei requirements. Notably, decision-makers exhibit a heightened inclination towards utilizing interval numbers over exact values in the evaluative process (Öztaysi et al. 2015). Addressing this proclivity, the Hesitant Fuzzy Analytic Hierarchy Process (HFAHP) (Kosamia et al. 2023) proves instrumental in navigating decision-making amidst uncertainty by furnishing hesitant fuzzy linguistic terms (HFLTs). However, a lack of literature is evident regarding the application of HFAHP within the realm of product design for the determination of demand indicator weights. This study integrates the aforementioned method with HFQFD, assimilating critical requirements and their weights derived from FA and HFAHP into the House of Quality (HOQ), thereby constituting an associative model delineating the nexus between CRs and ECs.
In summary, situated within the framework of KE, this investigation introduces an innovative integrated method model encompassing HFAHP and HFQFD, thereby improving the systematicity and precision in the design of rosewood furniture forms. Primarily, FA and HFAHP are employed to capture key Kansei factors and consequential CRs. Lastly, HFQFD is implemented to more accurately articulate CRs into ECs. This methodology facilitates designers in ascertaining optimal design schemes that closely adhere to consumer needs, thereby curtailing trial-and-error expenses and fortifying market competitiveness.
LITERATURE REVIEW
Rosewood Furniture
Rosewood furniture has enjoyed popularity in China since the mid to late Ming Dynasty, undergoing a gradual evolution into two predominant stylistic forms recognized as Ming-style and Qing-style. In the 21st century, New Chinese-style has emerged. Because of its many advantages in terms of appearance, structure, craftsmanship, materials, and cultural significance, rosewood furniture has sustained widespread favor among the Chinese populace over an extended duration. In the 20th century, a multitude of scholars engaged in research on rosewood furniture. However, the predominant focus was on theoretical analysis, with a deficiency in the incorporation of quantitative methodologies and outcomes reflecting innovative design. In the 1940s, German scholar Ecke (1991), through extensive data collection, conducted surveys and studies on the form, structure, and dimensions of traditional Chinese furniture. Kates et al. (1962) conducted a detailed analysis of over 100 pieces of traditional Chinese furniture. In the 1970s, Ellsworth (1971) studied numerous cases of high-quality hardwood furniture. By the 1980s, Wang (1989) initiated the first systematic study of Ming-style furniture in China, analyzing and appreciating the categories, shapes, and materials of many rosewood furniture pieces, sparking a domestic trend in researching traditional furniture. Pu (2012) conducted a systematic study on the history, categories, and artistic features of Chinese rosewood furniture, including furniture forms.
Kansei Engineering
Kansei Engineering (KE), initially introduced by Mitsuo Nagamachi (Nagamachi 1995) during the 1970s, integrates fundamental theories derived from the fields of psychology and engineering. It serves to capture and quantify user emotions, which are then translated into tangible design elements of products, ultimately enhancing consumer satisfaction (Nagamachi 2002). This theory found its early application within Japanese corporations, enabling them to optimize product development, minimize trial-and-error probabilities, and reduce costs. Furthermore, since the inception of Kansei Engineering, it has garnered significant attention from universities around the world, inspiring scholars to apply it in various domains such as transportation, everyday consumer goods, and creative cultural products. This includes innovative design endeavors in form, color, and material choices. For instance, Yang et al. (2023c) employed a combination of Factor Analysis, T-test, and Cluster Analysis to compare the perceptual attributes of fabrics dyed with tea leaves and tea stems.
In addition, certain scholars have engaged in discussions regarding the integration of KE with other theories or methodologies. For example, Liu et al. (2023) developed a crawler program to extract online product reviews and parameters. They computed the evaluation value of Kansei word pairs for various product samples and subsequently employed a BP Neural Network to train a mapping model, leading to an improvement in prediction accuracy. Shieh et al. (2016) introduced Rough Set Theory (RST) in the Kansei Engineering system to design the form and color of toothbrushes.
In recent years, some scholars have applied KE to the design of wooden furniture. For example, Lin et al. (2024) conducted perceptual semantic experiments on Ming and Qing dynasties, and modern Chinese furniture based on Kansei Engineering, and then used the single factor variance method to compare the perceptual images of the three styles of solid wood chairs and deconstruct the form elements of the furniture. QTT-I and multiple linear regression models were used to establish the relationship between morphological elements and perceptual semantics.
EXPERIMENTAL
Research Framework
The general arrangement of this research process is outlined in several phases, as illustrated in Fig. 1. Phase 1 involves preliminary preparations for the formal experiment. This includes collecting and filtering descriptive vocabulary related to the forms of rosewood furniture, gathering and selecting images of rosewood furniture, creating experimental samples, and constructing a deconstruction table for rosewood furniture forms. Phase 2 focuses on identifying key Kansei words. Factor analysis is performed on the questionnaire data to extract key Kansei factors. Phase 3 involves constructing a weight judgment matrix for the Kansei words. HFAHP is used to calculate the weights of each descriptive term. These terms and their weights are then imported into the left and right walls of the HOQ, respectively. Phase 4 involves constructing a mapping model between CRs and ECs. The design features of rosewood furniture are incorporated into the ceiling of the HOQ. Linguistic terms are used in the correlation matrix to express the corresponding fuzzy numbers, determining the strength of the relationships. Finally, the importance and ranking of the form elements are imported into the floor of the HOQ, thus identifying the priority elements for development.
Fig. 1. The method framework for rosewood furniture design
Research Methods
Hesitant Fuzzy Analytic Hierarchy process
The HFAHP can organize diverse indicators within complex systems and construct hierarchical relationship models. Once the key Kansei factors and their corresponding internal Kansei words are identified, the weights and rankings of CRs under different dimensions are determined based on HFAHP. The specific steps of HFAHP can be roughly divided into 6 steps, which are discussed below.
Step 1: After constructing the hierarchical model, experienced experts in the field assess the weights between two indicators. They use the linguistic terms provided in Table 1 to represent the degree of importance of the first indicator compared to the second indicator.
Table 1. Triangular Fuzzy Numbers and Corresponding Linguistic Terms in HFAHP
Step 2: Buckley (1985) indicates that the consistency of 
can be determined by examining the consistency of 
. Therefore, the first step involves converting fuzzy numbers to precise numbers using Eq. 1,
(1)
where l is the left side of triangular fuzzy number, m is the middle side of triangular fuzzy number, and u is the right side of triangular fuzzy number.
Subsequently, the maximum eigenvalue 
is obtained through Eq. 2, and then Eq. 3 is applied to calculate the Consistency Ratio (CR). If CR < 0.1, the matrix is considered logically sound and classified as an effective matrix (Miao et al. 2024). The value of RI is determined based on the number of matrix indicators, as shown in Table 2.
(2)
(3)
Table 2. RI Value
Step 3: The Fuzzy Envelope Approach is employed to combine assessments from all experts (Liu and Rodríguez 2014). The linguistic terms in Table 1 are ranked from the lowest value (S0) to the highest value (Sg). Assuming expert ratings fall between two linguistic terms, denoted as Si and Sj, it holds that S0 ≤ Si ＜ Sj ≤ Sg.
The parameters of the trapezoidal fuzzy membership functions 
are computed using Eqs. 4 through 7:
(4)
(5)
(6)
(7)
The operation of OWA requires a weight vector, where parameter ‘a‘ is defined as the first type of weights within the unit interval [0, 1] and the second type of weights (Filev and Yager 1998).
The first type of weights 
is defined as:
The second type of weights 
is defined as:
where 
and 
.
where g is the number of linguistic terms, j is the highest value within the assessment range, and i is the lowest value within the assessment range.
Step 4: The hesitant fuzzy pairwise comparison judgment matrix was constructed, as illustrated in Eq. 8,
(8)
where 
.
Simultaneously, the reciprocal of 
can be represented as follows:
Step 5: Calculate the fuzzy geometric mean for each row of the matrix using Eq. 9,
(9)
Step 6: Obtain the fuzzy weights 
for each indicator based on Eq. 10. The values of 
are accepted as the maximum parameters for AHI in Table 1.
(10)
Step 7: Apply Eq. 11 to de-fuzzify the trapezoidal values of fuzzy weights, yielding de-fuzzification weights for each indicator:
(11)
Step 8: Normalize the de-fuzzification weights using to obtain normalized weights, ensuring that the sum of all indicator weights within the same hierarchy equals 1.
Hesitant fuzzy quality function deployment
In the application of HFQFD, the initial procedural step entails the establishment of the House of Quality (HOQ). The CRs derived from the HFAHP are introduced on the left side, while the normalized weights associated with CRs are introduced on the right side. Concurrently, ECs are integrated into the upper side. The triangular fuzzy numbers within the HFQFD framework are stratified into seven distinct levels, as delineated in Table 3. Following the methodologies articulated in Eqs. 4 through 7, the aggregated trapezoidal fuzzy correlation is ascertained. Subsequently, this correlation is positioned within the HOQ matrix to signify the magnitude of correlation between CRs and ECs.
Table 3. Triangular Fuzzy Numbers and Corresponding Linguistic Terms in HFQFD
The determination of the absolute importance (AI) for each EC is contingent upon the standardized weights assigned to CRs located on the left side of the HOQ and the correlation strength situated at the center of the HOQ. Utilizing the corresponding fuzzy numbers, AI is derived in accordance with Eq. 12. Subsequently, by employing the analogous procedures delineated in Eq. 11, the process of defuzzification is applied to AI, leading to the derivation of the defuzzification absolute importance (DAI). Ultimately, employing Eq. 13, the relative importance (RI) of ECs is ascertained, and subsequent ranking procedures are executed. The resulting data are then assimilated into the HOQ matrix, thereby acquiring significant ECs. Equations 12 and 13 are as follows,
(12)
(13)
where NWi represents the standardized weight of the ith CRs derived in HFAHP; 
signifies the degree of correlation between the ith CRs and the jth ECs, represented as a set of trapezoidal fuzzy numbers; AIj denotes the fuzzy absolute importance of the jth ECs; DAIj signifies the defuzzification absolute importance of the jth ECs; and RIj denotes the relative importance of the jth ECs.
Preliminary Preparation of Experiment
Kansei words serve as manifestations of consumers’ emotional requirements, and for grasping of the direction in subsequent product development, it is imperative to meticulously select representative Kansei words. Two channels were employed to extensively collect Kansei words related to rosewood furniture. The first involved a direct perusal of relevant books and academic journals, while the second encompassed researching product descriptions and consumer comments from auctions, online shopping platforms, and websites of furniture brand companies. Excluding negatively connotated or undesirable terms, a preliminary compilation of 53 Kansei words describing the form of rosewood furniture was obtained. An expert panel consisting of professionals engaged in research, design, or sales of rosewood furniture was invited to form a focus group. After discussions and exchange of opinions, the Initial word list was subjected to condensation and refinement using the KJ method. Redundant synonyms were eliminated, followed by the removal of terms less closely associated with the morphology of rosewood furniture and those with overly broad and general meanings. The outcome retained a set of 9 effective Kansei words, as illustrated in Table 4.
Table 4. The Reserved Kansei Words
In this study, square tables were chosen as the experimental subjects for investigating the form of rosewood furniture. Square tables represent a fundamental category within the realm of furniture and possess distinct representativeness due to their widespread market demand and versatile applicability in various scenarios in China. First, a comprehensive collection of images of rosewood square tables was gathered from sources such as websites and books. After an initial screening, highly similar images were eliminated, resulting in an initial pool of 60 samples. Subsequently, to reduce respondent burden and enhance the accuracy of research results, a focus group further examined the initial samples. Products with both distinctive morphology and significance were retained, while low-resolution images were discarded. Ultimately, 12 representative samples were obtained. Lastly, the representative sample images underwent uniform processing using the computer image editing software Adobe Photoshop. Standardized and properly sized experimental samples were created, eliminating background and shadow pixels from the 12 sample images except for the product itself. These images were then output as white background JPG files with dimensions of 500 mm x 500 mm and a resolution of 300 PPI. The product was centered within each image. Additionally, to prevent color elements from interfering with respondents’ emotional perception, the saturation of the color images was reduced to -100 to convert them into grayscale form.
To establish an association matrix within the HOQ in subsequent steps, a Morphological Analysis method was employed to pre-construct a form element table for rosewood square tables. The design characteristics of the square table were decomposed, encompassing 7 components. Subsequently, this study analyzed the initial samples collected in the preliminary stage. After compiling all the form element types under each design characteristic, a selection was made of the most frequently occurring ones to construct a form element matrix. Unified specifications for morphological profiles were created using Adobe Illustrator, accompanied by encoding, as shown in Table 5.
Table 5. Design Characteristic Table
Using Likert Scale and FA to Determine the Key Kansei Factors
To begin, a 7-point Likert scale questionnaire was formulated based on the initial selection of 9 Kansei words and 12 experimental samples. A total of 95 participants were enlisted to partake in responding to the questionnaire. Once the data collection phase concluded, a meticulous examination of data quality was conducted. Subsequently, 15 questionnaires characterized by notably brief response durations and logically incongruous answers were excluded from consideration. Ultimately, a set of 80 validated datasets was retained, serving as an objective foundation for identifying key Kansei factors. Effective data matrix can be found in Table 6.
Subsequently, the effective data matrix was imported into the SPSS Statistics 27 software for the purpose of conducting a validity assessment. The suitability of the variables for factor analysis was determined through the KMO and Bartlett’s Test of Sphericity within the context of factor analysis. The test outcomes are as follows: KMO = 0.823, Bartlett’s Test of Sphericity < 0.001. This result proves that the data is suitable for factor analysis.
Table 6. Data Matrix
Table 7. Total Variance Explained
Furthermore, within this section, the principal component analysis method was employed as the extraction technique for factor analysis. This method can reduce dimensionality and clustering Kansei words while retaining their explanatory power. Eigenvalues greater than 1 were set as the criterion for extraction. The rotation method is Varimax with Kaiser normalization. Total variance explained is detailed in Table 7, a scree plot is illustrated in Fig. 2, and a rotated factor matrix is provided in Table 8.
Fig. 2. Scree plot
Table 8. Rotated Factor Matrix
In conclusion, a qualitative analysis was conducted on the graphical and tabular data presented above. Upon observing Table 9, it is evident that by the third factor, the cumulative explained variance percentage reaches 93.996%, exceeding the threshold of 90%. The scree plot indicates the presence of three factors with eigenvalues greater than 1, signifying the extraction of three key factors through this principal component analysis. Analyzing Table 10 reveals that Factor 1 is composed of five Kansei words, namely natural, simple, steady, ethereal, and stately. These terms suggest that the furniture form retains a more natural and simplistic character without excessive artificial intervention. Therefore, it is labeled as “Natural Factor.” Factor 2 consists of three Kansei words, opulent, elegant and exquisite, indicating a distinct presence of artificial decorative features in the furniture form. Consequently, it is named “Artificial Factor.” Factor 3 solely includes the Kansei word “well-proportioned,” highlighting that this factor differentiates itself from the style attributes of Factors 1 and 2. Instead, it characterizes the harmonious relationship of furniture form and proportions, and thus, it is named “Well-proportioned Factor.”
Using HFAHP to Determine the Weight of Kansei Words
Factor 1, denoted as the “Natural Factor,” and Factor 2, termed the “Artificial Factor,” manifest as two distinct styles of furniture form, thus delineating two divergent trajectories in the development of rosewood furniture. Factor 3, referred to as the “Well-proportioned Factor,” assumes the role of evaluating the congruity of furniture form and proportion, serving as an initial assessment criterion for subsequent design proposals.
Initiating the process, a hierarchical model for Kansei words related to rosewood furniture is established. This model comprises three hierarchical tiers: The topmost layer, designated as the goal layer, the intermediate layer known as the criteria layer, housing the two product development paths, and finally, the bottom layer, recognized as the sub-criteria layer, which integrates eight distinct customer’s emotional requirements. This arrangement is visually presented in Fig. 3.
Fig. 3. Hierarchical model of Kansei words
Five experts with extensive experience in using rosewood furniture and engaged in furniture design and research were invited to form a focus group. The focus group conducted pairwise comparisons to determine the importance of Kansei words at the sub-criterion level, constructing a hesitant fuzzy judgment matrix to obtain the weights of the Kansei words. The subsequent narrative employs the scoring matrix calculation process for the five sub-criteria (K11 to K15) under the K1 criteria layer as an illustrative case.
Initially, a hesitant fuzzy judgment matrix was constructed following the standards outlined in Table 1. Subsequently, Eqs. 1 to 3 were utilized to assess the consistency of the matrix, revealing that it passed the consistency test. The fuzzy envelopes for the evaluation of criteria are illustrated in Table 9. The comparison matrix after transformation into trapezoidal fuzzy numbers is presented in Table 10.
Applying Eq. 9 yielded the fuzzy geometric mean for each row of the matrix, followed by the application of Eq. 10 to determine the trapezoidal fuzzy weights for the five sub-criteria. Subsequently, Eq. 11 was employed to defuzzify the five sets of trapezoidal fuzzy weight matrices, resulting in defuzzification weights. Finally, the normalized weights were obtained for the five sub-criteria.
Repeating the above steps, the same procedures were employed to acquire the normalized weights for three sub-criteria (K21 to K23) under the K2 criteria layer, as illustrated in Table 11.
According to the outcomes derived from the normalized weight calculations, the importance ranking of Kansei words under the Natural Factor is K11 (0.333), K12 (0.308), K13 (0.193), K14 (0.095), and K15 (0.071). For Kansei words under the Artificial Factor, the importance ranking is K22 (0.564), K21 (0.252), and K23 (0.183).
Table 9. Fuzzy Envelopes for the Evaluation of Criteria