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Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design
International Journal of Industrial Ergonomics 56 (2016) 97e105
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International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon
Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design* Yu-Ming Chang a, Chun-Wei Chen b, * a b
Department of Creative Product Design, Southern Taiwan University, No. 1, Nantai St., Yongkang City, Tainan County 710, Taiwan, ROC Department of Industrial Design, National Cheng Kung University, No. 1, Dasyue Rd., East District, Tainan City 701, Taiwan, ROC
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 January 2015 Received in revised form 18 July 2016 Accepted 24 September 2016
In most studies on Kansei, the product form analysis model is based on the external features or elements of the product components. However, such an approach cannot completely convert consumer emotional perceptions into design elements. Therefore, this study combined the ergonomic technology used in Kansei engineering with the unique cognitive ability of humans to identify patterns and establish an emotional perception model that can integrate the overall interrelations of the constituent elements. Steering wheel design was used as the object of examination. Three types of adjectives were applied to describe the constituent elements. The first type was esthetic factors and involve external esthetics. The second type comprised two pairs of adjectives, sturdy/delicate and lightweight/heavy, called operational strength factors because they relate to form and strength. The third type comprised simplistic/changeful and artificial/spontaneous, called modernity factors because they pertain to the modern sense of beauty of the parts. Multiple linear regression analysis was used to construct a Kansei engineering model and compare the performance of individual elements and the product as a whole. The results show that the R2 values in the overall model were greater than those in the element-oriented model, indicating that the integrated model outperformed the element-oriented model in variance explanation. The differences between the numerical values of the adjective pairs classic/fashionable (esthetic factors), sturdy/delicate (operational strength factors), and simplistic/changeful and artificial/spontaneous (modernity factors) were significant, demonstrating that the overall model is useful in predicting how consumers make assessments according to emotional perceptions. The R2 increase of the modernity factors was the most obvious, indicating that the overall model assesses modernity more accurately. Comparing results and verifying test samples demonstrated that the overall model is more useful in predicting consumer appraisals that are based on emotional perception. Relevance to industry: This study determined that esthetics, operational strength, and modernity are the three most crucial factors in the emotional perceptions and preferences of consumer regarding steering wheel design. The results demonstrate that a model that integrates constituent elements can evaluate consumer behavior and assist product designers in understanding consumers. © 2016 Elsevier B.V. All rights reserved.
Keywords: Constituent factor Overall interrelation Kansei engineering Design assessment Car steering wheel
1. Introduction 1.1. Background and study motivation The car part that a driver interacts with most directly and
* This study combined the ergonomic technology used in Kansei engineering with the unique cognitive ability of humans to identify patterns and establish an emotional perception model that can integrate the overall interrelations of the constituent elements. Steering wheel design was used as the object of examination. * Corresponding author. E-mail address: [email protected] (C.-W. Chen).
http://dx.doi.org/10.1016/j.ergon.2016.09.010 0169-8141/© 2016 Elsevier B.V. All rights reserved.
frequently is neither the appearance nor power system of the car, but rather its interior design, of which the steering wheel is the most fundamental component. In addition to the shape of the steering wheel, the gripping experience and the convenience during operation are also crucial. In an era of emotional consumption, the sense of satisfaction with a product that a consumer derives and that leads to a purchase decision is no longer merely a consideration regarding function or practical use. Sense perception is applied to comprehensively and perceptually assess before a purchase decision is made (Chang et al., 2005). When a consumer wants to buy something, he/she will have a kind of feeling and image (Kansei in Japanese) in his/her mind. If the consumer's
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feeling could be implemented in the new product, he/she would be more satisfied with the product (Nagamachi, 2002). Affective design has received much attention from both academia and industries. It aims at incorporating customers' affective needs into design elements that deliver customers' affective satisfaction. The main challenge for affective design originates from difficulties in mapping customers' subjective impressions, namely Kansei, to perceptual design elements (Jiao et al., 2006). In today's competitive environment, satisfying customer needs has become a great concern of almost every company (Cross, 2000). While there are various customer needs, the functional and affective needs have been recognized to be of primary importance for customer satisfaction (Khalid, 2001). Following the progress achieved in the auto industry in recent years, consumer demand for quality in various aspects of automobiles has increased. The control panel in new cars often features an LCD screen that can integrate DVDs, stereos, GPS devices, digital TVs, and smartphones. In some high-end models, the steering wheel is equipped with a dial control featuring consolidated functions. Further examples such as LCD screens in headrests and constant air conditioning control suggest that the trend of installing entertainment devices in cars is irreversible. However, car design is complex and changing, involving considerations of interactions among the driver, the vehicle, and the environment. Within Taiwan and abroad, the comfortability of seats and driving is of particular interest. However, one other crucial factor has often been neglected: the geometric elements of the driver (human) and the design of the steering wheel (object). The turn and angle design for the geometric structure of the steering wheel affects driver comfort. Drivers hold the steering wheel until the journey ends; An understanding of the pressure patterns that are appropriate for the human body (that is, the patterns that reduce discomfort or improve what is called comfort) can make product design and usage very satisfying and fulfilling (Goonetilleke, 2000). Car owners look beyond functionality to consider emotional design features, car designers may need to learn how to include emotional design features as a design procedure (Helander et al., 2013). Therefore, control devices built into the steering wheel have drawn the attention of drivers. Most drivers, particularly those who drive for work, such as taxi drivers, often experience neck, shoulder, lower back, or waist pain after driving a long distance or for a long period (Chien, 2006). Hence, in addition to appropriate operating environments and comfortable seats, drivers require a well-designed steering wheel. Worldwide vehicle sales surpassed 80 million in 2013, and the market continued to grow in 2014, which led to a positive outlook for profitability in the global automotive industry. The two major automotive markets of China and the United States are expected to continue growing. Furthermore, as automobile companies continue to innovate, they will attract more drivers to purchase their products. Thus, more attention will be paid to how these automobile companies innovate and make safer vehicles catering to the needs and interests of drivers. Quantitative data on the relationship between design elements and user evaluations is useful to product designers and managers in formulating design strategies (Hsu et al., 2000). Therefore, this study identified the effects of steering wheel devices on driver perception to design a comfortable entertainment system with quality steering wheel interfaces.
Yamamoto, emphasized the “contribution of automobiles to cultural creativity” and explained his theory of car-driving culture. He proposed applying Kansei engineering in designing automobile interiors to satisfy the needs and emotional demands of car users and to increase the comfort of automobiles. Through Kansei engineering, designers can access objective data and more comprehensively understand consumer preferences and perception (Chuang and Chen, 2004). During exploration of product appearance and consumer impressions, Kansei engineering is a crucial cut-in point. Developing product design and theory according to consumer feelings and perceptions (psychological impressions) is now common (Chang et al., 2005). Through engineering approaches, human emotional perceptions are quantified to establish a functional relationship between the quantity of the emotional perception and the physical quantity (form) of the stimulus that triggers the emotional perception; this relationship is the foundation of design development (Zheng, 1993). In other words, Kansei engineering is being used to convert customer demands and feelings into product design elements and realize the ideas in the minds of consumers (Nagamachi, 1995a) (Nagamachi, 1995b) (Jindo et al., 1994). In most studies on Kansei, the external design and features of products have formed the core of analysis. Therefore, Kansei engineering can be defined as a technology that quantifies and presents the feelings that people have toward objects (products) and that explores design elements that match certain emotions that people have. The word “Kansei” can be interpreted as people's feelings toward or impressions of an object, representing their psychological expectations and perceptions. Businesses in Japan have widely applied Kansei engineering in various fields, such as in the design of cars, office chairs (Jindo et al., 1995), interior design of cars (Jindo and Hirasago, 1997), vehicle interior image (Tanoue et al., 1997), and color planning for product appearance (Fukushima et al., 1995). However, from the perspective of cognitive psychology, further analysis suggests that ambiguity, uniqueness and dominance are three important aspects to consider when designing and developing icons.(Goonetilleke et al., 2001). Examining consumer recognition and perception of a product by identifying only the features of the object cannot explain human cognitive behavior completely. The overall interrelations between components are also critical. Thus, this study developed a Kansei engineering model to analyze and determine the interrelations of constituent elements. The three main objectives were to (1) investigate consumer design preferences, (2) identify the types of emotional perceptions toward product form, and (3) understand the viability and capacity of the two emotional perception analysis systems regarding the constituent elements and their overall interrelations.
1.2. Study objectives
In the development of products, methods enabling the designer to create an appropriate image for a product so that it may communicate with the user are always critical issues (Chuang and Ma, 2001). Pictures of steering wheels of different designs were obtained from books, magazines, and almanacs for use as the stimulation test samples. Because the chief objective of this study was to examine the correlations among the constituent elements,
The software and hardware installed in automobiles, such as GPS and mobile phone hands-free devices, are increasingly diverse, increasingly complicating driving. In a speech at the World Automotive Technology Conference and the U.S. Automaker Seminar in 1986, former Mazda Motor Cooperation president, Kenichi
2. Methods of study and implementation In this study, two Kansei engineering systems were developed to analyze the design of car steering wheels according to the overall interrelations of constituent elements. The results were interpreted and predictive capacities were compared. The steps of the development are as follows: 2.1. Stimulation test sample selection
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participants, the researcher first removed all brand logos from the samples and converted the images into 30 cm 30 cm black-andwhite prints. A focus group research method was then used to select representative samples. The focus team (product design specialists) comprised six graduate students from the Graduate Institute of Industrial Design at National Cheng Kung University. Four students were male, two were female, all were industrial design majors in undergraduate and graduate programs, and three had more than two years of practical design experience. To avoid discrepancy regarding form vs content, the focus team, through direct visual perception, classified the test samples according to the visual characteristics of the images. To classify the 114 test samples by form contrast, the KJ method was adopted. This method is used for qualitative analysis based on intuition and for innovating through teamwork. Because this method is used for analyzing how participants perceive the images, it enables the characteristics of images to be more clearly understood (Yang, 2003) The team members classified the samples with similar appearances into groups and to select 45 representative samples for the emotional perception survey. The team members discussed and divided the samples into 12 groups. The representative samples were then chosen (Fig. 2). Despite the logo removal, interference from preconceptional impressions remained in the overall assessment and interpretation. Consequently, the representative samples were traced to produce linear sketches (Fig. 3) that were used as the final representative test samples.
Fig. 1. 114 Test samples initially collected.
Fig. 2. Final representative test samples.
2.2. Vocabulary selection
Fig. 3. Linear sketches of final representative test samples.
structural relations, and quantity of emotional perception toward the product, color was considered. As shown in Fig. 1, 114 samples were collected. To prevent brand names from influencing
Eighty contrasting pairs of adjectives suitable for describing product impressions were selected from related professional publications, magazines, and research papers for the emotional perception vocabulary (Table 1). The focus team discussed the qualities of the steering wheel samples to be tested, and applied the Delphi method to the adjective pairs that had received more than half of the votes. The Delphi method is a structured technique for decision-making. During the process of collecting messages, this method is used to obtain relatively objective messages, opinions, or insights from specialists who make independent, repeated, and subjective judgements. Eliminated unsuitable pairs of adjectives, and retained the 20 most appropriate pairs (Table 2). Ten participants (five men and five women; all graduate students at the Graduate Institute of Industrial Design at National Cheng Kung
Table 1 Eighty adjectives. Smooth Simple Quiet Warm Classy Sturdy Sensible Attractive Unique Geometric
Sophisticated Harmonious Ordinary Male Formal Intricate Generous Safe Changeful Organic
Natural Heavy High-class Fashionable Avant-garde Magnificent Elegant Classic Expensive Lively
Conventional Modern Comfortable Young Trendy Bright Lightweight Soft Sexy Dull
Noble Innovative Round Casual Plain Thin Introverted Rational Monotonous Complex
Technological Simplistic Neutral Exclusive Dynamic Passionate Rich Steady Embellished Minimal
Delicate Artificial Futuristic Professional Tasteful Dazzling Valuable Intimate Common Cold
Low-key Contemporary Novel Pleasurable Luxurious Tough Loud Friendly Reserved Rugged
Table 2 Twenty contrasting pairs of adjectives. SophisticatedeSmooth ExclusiveeCommon ComplexeMinimal ClassiceFashionable FuturisticeConventional
IntrovertedeMagnificent CasualeFormal SofteTough IntricateeRugged QuieteLoud
SimplisticeChangeful EmbellishedePlain LivelyeDull ColdeWarm High-classeOrdinary
ArtificialeNatural SteadyeLightweight ReservedeAvant-garde ThineHeavy GeometriceOrganic
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Table 3 Cluster analysis of the 20 contrasting pairs of adjectives. Ward's linkage method Stage
Cluster combined Cluster 1
Cluster 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
15 11 12 7 7 1 7 2 12 4 1 10 2 1 6 4 1 1 1
20 17 13 19 14 5 11 3 18 15 9 16 12 7 10 8 6 4 2
Agglomeration schedule Coefficients
25.000 50.000 76.000 102.500 136.000 174.000 215.400 258.900 304.233 349.900 404.567 468.567 540.533 613.717 691.717 778.300 972.232 1188.933 1748.000
Stage cluster first appears Cluster 1
Cluster 2
0 0 0 0 4 0 5 0 3 0 6 0 8 11 0 10 14 17 18
0 0 0 0 0 0 2 0 0 1 0 0 9 7 12 0 15 16 13
Next stage
10 7 9 5 7 11 14 13 13 16 14 15 19 17 17 18 18 19 0
University) applied the 20 pairs of adjectives to the 45 steering wheel samples, and a preliminary SD test was conducted. Ward's hierarchical clustering method was used to analyze the results (Table 3). From the cluster dendrogram (Fig. 4), 10 final representative pairs of adjectives were chosen (Table 4) to alleviate the burden of the participants in the official SD test. 2.3. Product form deconstruction and description The form of a product is the aggregate result of the components,
such as the texture, position, hub, and number of spokes of a steering wheel. In emotional perception research and application, product form deconstruction and analysis are often based on such a morphological approach. Through a systematic analysis, this approach analyzes all possible product form. To solve problems in product design, relevant independent factors (items) are listed according to the functions or features of the product. Thus, on the basis of each independent factor, all possible solutions (category) are listed. This study, however, also considers the overall interrelations between the components. The focus team discussed the two aspects of the constituent elements (the form and style of each component) and their overall interrelations (the corresponding position, connection, and ratio of the components) and to deconstruct the features of the 45 test samples to form a new design. After ample discussion and analysis, the focus team identified the steering wheel morphological features and constituent relations that influenced emotional perception assessment (Table 5).
2.4. Emotional perception assessment of appearance data To confirm the relations between the appearance of steering wheels and the emotional perception vocabulary, an SD survey was administered to 30 junior year students from the Department of Multimedia and Entertainment Science at Southern Taiwan University of Technology. The 30 participants all had a driving license, were students enrolled in continuing education programs, and were aged 22e28 years. The 10 pairs of adjectives listed in Table 2 were used as a measure and a 9-point Likert scale (1e9 points from left to right) was used to evaluate the 45 steering wheel test samples.
Fig. 4. Dendrogram for cluster analysis of the 20 contrasting pairs of adjectives.
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Table 4 Adjective pairs used in SD test. Simplistic -Changeful Sturdy - Delicate
Artificial - Spontaneous Classic - Fashionable
Unique - Common Lightweight - Heavy
Casual - formal Futuristic - Conventional
Embellished - plain Classy - Ordinary
Table 5 Morphological analysis.
Morphological Feature
Overall Interrelation
Item
Category
Number of Spokes
A1. 3 spokes
A2. 4 spokes
Touch of spokes
B1. Uneven surface design
B2. No decorative uneven surface design
Positioning of spokes
C1.90/180
C2.90/145
C3.90/130
Wheel rim material
D1. Leather cover
D2. Walnut veneer cover
D3. Composite cover
Hub surface
E1. Unibody
E2. Jointed
Hub shape
F1. Water drop- shaped
F2. Pyramidal
F3. Trapezoidal
F4. Semi- circular
F5. Diamond shaped
F6. Round
Spoke-rim joint
G1. Gapless joint
G2. Gapped joint
Visible areas of center, spokes and rim
H1. Approximate visible areas
H2. Unequal visible areas
Switch positions in relation to the hub
I1. Symmetrical on both sides of the hub
I2. Surrounding the center
3. Results and discussion 3.1. Emotional perception assessment results Initially, the average numbers of times the 30 participants applied the adjectives were established and factor analysis was conducted. Table 6 shows that the correlation coefficient from the KMO and Bartlett's test achieved a significance level of 0.714 (sig ¼ 0.000). According to principal component analysis, three elements with an eigenvalue exceeding 1 were extracted. Table 7 shows that the three types of factors together explained 76.043% of the total variance. To ensure that the statistical data acquired using the questionnaire in this study exhibited satisfactory reliability, a reliability analysis of the data was conducted to determine the Cronbach's a value to be 0.711 (Table 8), and the overall statistics of the items were examined (Table 9). According to DeVellis' (1991) suggestion that a
Table 6 KMO and Bartlett's test.
3.2. Emotional perception assessment mode of element-oriented and overall models: construction and comparison
KMO and Bartlett's test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. Chi-Square df Sig
Cronbach's a of between 0.65 and 0.70 indicates moderate reliability, and that a Cronbach's a of between 0.70 and 0.80 entails high reliability, the questionnaire results of the present study exhibited high reliability. Through varimax orthogonal rotation, the loadings of the three element factors were obtained, as shown in Table 10. The variances in relation to the esthetic, operational strength, and modernity factors were 44.62%, 19.52%, and 11.90%; the accumulated total variance was 76.043%. According to the meanings of the adjectives, the factors can be categorized into esthetic factors: six pairs of adjectives with qualities relevant to comparing external esthetics, namely, embellished/plain, futuristic/conventional, classy/ordinary, unique/common, classic/fashionable, and casual/ formal; operational strength factors: two pairs of adjectives with qualities used for comparing form and power, namely, sturdy/ delicate and lightweight/heavy; and modernity factors: two pairs of adjectives with qualities used for assessing the level of modernity and esthetics, namely, simplistic/changeful and artificial/ spontaneous.
0.714 173.729 45 0.000
To contrast the results of the two Kansei engineering models from the form analysis of the element-oriented model and the overall model, multiple linear regression analysis was used,
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Table 7 Total variance explained. Total variance explained Component
1 2 3 4 5 6 7 8 9 10
Initial eigen values
Extraction sums of squared loadings
Rotation
Total
% Of Variance
Cumulative %
Total
% Of Variance
Cumulative %
Total
4.462 1.952 1.190 0.875 0.526 0.309 0.273 0.195 0.123 0.095
44.622 19.517 11.903 8.752 5.259 3.096 2.728 1.945 1.227 0.953
44.622 64.139 76.043 84.795 90.054 93.147 95.875 97.820 99.047 100.000
4.462 1.952 1.190
44.622 19.517 11.903
44.622 64.139 76.043
4.224 2.092 2.108
Extraction Method: Principal Component Analysis. a When components are correlated, sims of squared loadings cannot be added to obtain a total variance.
Table 8 Statistics of reliability. Cronbach's alpha
Number of items
0.711
10
establishing the relations between the car steering wheel form factors and the emotional perception vocabulary. The design factors obtained from the form analysis of the 45 test samples were defined as independent variables, and the averages of the emotional perception evaluations of the 10 adjective pairs were set as the dependent variables. Backward regression was then used to conduct multiple regression analysis of each pair of adjectives, locating the form factors that significantly influenced the emotional perception vocabulary. Simultaneously, the design factors were
listed according to a nominal scale, and dummy variables were used in the multiple regression analysis. Zero and 1 represented nonquantifiable characteristics and attributes. In the elementoriented form analysis model, the design factors were mainly element characteristics, whereas in the overall model, the design factors were combinations of element characteristics and the overall interrelations of the elements. Table 11 show the results of the regression analysis of the two models. In Table 11, the blank spaces (such as B3: No decorative, uneven surface design) signify that the influence of the element did not achieve the significance level of 0.05. A higher standardized coefficient indicates that the participants considered the impression of the second adjective in the pair to be more useful in design development. A negative standardized coefficient means that the participants believed that the first adjective of the pair exerted a stronger influence on impression creation.
Table 9 Overall statistics of the items.
Simplistic e Changeful Artificial e Spontaneous Unique - Common Casual e Formal Embellished e Plain Sturdy e Delicate Classic e Fashionable Lightweight e Heavy Futuristic e Conventional Classy e Ordinary
Scale mean obtained when the item was excluded
Scale variance obtained when the item was excluded
Revised total item correlation
Cronbach's alpha obtained when the item was excluded
28.7556 29.5556 29.0667 28.7333 29.1778 28.8444 28.4222 28.2222 29.0444 28.5778
41.553 44.934 37.882 40.973 38.195 47.225 48.340 39.677 38.316 40.113
0.326 0.377 0.563 0.391 0.630 0.068 -0.036 0.502 0.585 0.446
0.698 0.693 0.653 0.686 0.645 0.734 0.761 0.667 0.651 0.676
Table 10 Post-rotation factor loadings. Adjective
Factor type 1 esthetic factor
Factor type 2 operational Strength factor
Factor type 3 Modernity factor
Embellished e Plain Futuristic e Conventional Classy e Ordinary Unique - Common Classic e Fashionable Casual e Formal Sturdy e Delicate Lightweight e Heavy Simplistic e Changeful Artificial e Spontaneous Eigenvalue Variance Explained Accumulated Variance Explained
0.931 0.894 0.882 0.837 0.697 0.549 0.048 0.245 0.465 0.111 4.224 44.62% 44.62%
0.303 0.213 0.146 0.143 0.300 0.544 0.907 0.832 0.110 0.027 2.092 19.52% 64.139%
0.314 0.330 0.040 0.397 0.522 0.062 0.168 0.094 0.851 0.839 2.108 11.90% 76.043%
Table 11 Regression analysis of the element-oriented and overall models. Attribute
Futuristic e Conventional
Classy e Ordinary
Unique e Common
Classic e Fashionable
Casual e Formal
Sturdy e Delicate
Lightweight e Heavy
Simplistic e Changeful
Artificial e Spontaneous
Item
Category
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
A. Number of Spokes
3 spokes 4 spokes Uneven surface design No decorative uneven surface design 90/180 90/145 90/130 Leather cover Walnut veneer cover Composite cover Unibody Jointed Water drop-shaped Pyramidal Trapezoidal Semi-circular Diamond-shaped Round Gapless joint Gapped joint Approximate visible areas Unequal visible areas Symmetrical on both sides of the hub Surrounding the hub Constant Significance
0.212 0.121 0.322
0.124 0.349 0.322
0.063 0.432 0.212
0.332 0.434
0.235 0.453 0.225
0.454 0.112
0.221 0.323 0.065
0.542 0.431 0.064
0.467 0.543 0.098
0.060 0.654
0.165
0.187
0.132
0.062
0.065
0.061
0.325
0.343
0.232
0.437 0.276 0.254 0.215 0.211 0.331 0.196 0.174 0.082 0.197 0.214 0.061 0.135 0.062 0.072 0.176 0.178 0.021 0.097
0.118 0.211 0.132 0.176 0.187 0.232 0.276 0.182 0.098 0.212 0.223 0.083 0.207 0.121 0.063 0.113 0.111 0.078 0.054
0.212 0.112
0.238 0.320 0.245 0.134 0.232 0.243
0.154 0.268 0.299 0.119 0.063 0.213 0.137 0.012 0.097 0.243 0.112 0.079 0.095 0.154 0.133 0.091 0.065 0.132
0.098 0.332 0.221 0.121 0.146 0.199 0.165 0.153 0.138
0.112 0.142 0.156 0.053 0.115 0.098 0.065
0.267 0.211 0.165 0.255 0.113 0.345 0.324 0.157 0.215 0.210 0.165 0.089 0.321 0.231 0.124 0.132 0.156 0.067 0.124
0.132
0.163 0.217 0.098 0.199 0.215 0.142 0.091 0.139 0.124 0.187 0.087 0.208 0.101 0.202 0.094 0.099 0.087 0.067
0.189 0.171 0.061 0.124 0.216 0.211 0.211 0.143 0.154 0.091 0.095
0.115 4.234 0.012 R ¼ 0.884 R2 ¼ 0.781
0.165 3.884 0.008 R ¼ 0.888 R2 ¼ 0.789
0.102 4.225 0.011 R ¼ 0.781 R2 ¼ 0.610
0.156 4.004 0.030 R ¼ 0.785 R2 ¼ 0.616
0.093 3.575 0.021 R ¼ 0.784 R2 ¼ 0.615
0.111 5.445 0.022 R ¼ 0.770 R2 ¼ 0.593
0.211 3.132 0.013 R ¼ 0.911 R2 ¼ 0.830
B. Touch of Spokes
C. Positioning of Spokes
D. Wheel Rim Material
E. Crossbar F. Hub Shape
G.Spoke-rim joint H.Visible areas of hub, spokes and rim I.Switch positions in relation to the hub
R and R2 values
0.093
0.087 0.112
0.102
0.064
0.121 0.221 0.364 0.264 0.132 0.226 0.176 0.108 0.114 0.143 0.269 0.143 0.061 0.154 0.021 0.097 0.102 0.043 0.127
0.234 4.255 0.002 R ¼ 0.906 R2 ¼ 0.821
0.202 3.755 0.001 R ¼ 0.924 R2 ¼ 0.854
0.202 5.064 0.020 R ¼ 0.775 R2 ¼ 0.601
0.321 0.087 0.443 0.209 0.183 0.099 0.224 0.326 0.102 0.321 0.112 0.133
0.146 0.162 0.112 0.119 0.132 0.232 0.061
0.223
0.249 0.332 0.216 0.435 0.237
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Embellished e Plain
103
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Table 12 Comparison of the R2 values of the element-oriented model and the overall model. Element Attribute
Esthetics
Operational strength
Modernity
Adjective pair
Embellished e Plain
Futuristic e Conventional
Classy e Ordinary
Unique - Common
Classic e Fashionable
Casual e Formal
Sturdy e Delicate
Lightweight - heavy
Simplistic - Changeful
Artificial - Spontaneous
R2 value in featureoriented model R2 value in integrated model R2 gain value Adjusted R2 value in feature-oriented model Adjusted R2 value in integrated model Adjusted R2 gain value
0.774
0.783
0.605
0.817
0.828
0.593
0.581
0.598
0.601
0.819
0.781
0.789
0.610
0.821
0.854
0.601
0.616
0.615
0.593
0.830
0.007 0.552
0.006 0.575
0.005 0.412
0.004 0.675
0.026 0.689
0.008 0.374
0.035 0.368
0.017 0.376
0.008 0.397
0.011 0.679
0.568
0.580
0.425
0.687
0.702
0.397
0.430
0.427
0.374
0.699
0.016
0.005
0.013
0.012
0.013
0.023
0.062
0.051
0.023
0.020
Table 13 Relations between emotional perception vocabulary and design factors. Emotional impression
Individual element features
Overall interrelations
Casual
Number of spokes: 3 Spokes (0.454), Touch of spokes: no uneven surface design (0.065), Positioning of spokes: 90/130 (0.364), Wheel rim material: Leather cover (0.264), Hub surface: Jointed (0.108), Hub shape: round (0.154)
Formal
Number of spokes: 4 Spokes (0.112), Positioning of spokes: 90/180 (0.321), Wheel rim material: Walnut veneer cover (0.132), Hub surface: Unibody (0.176), Hub shape: trapezoidal (0.269)
Spoke-rim joint: Gapped joint (0.097), Visible areas of hub, spokes and rim: Unequal visible areas (0.043), Switch positions in relation to the hub: Surrounding the hub (0.202) Spoke-rim joint: Gapless joint (0.021), Visible areas of hub, spokes and rim: Approximate visible areas (0.102), Switch positions in relation to the hub: Symmetrical on both sides (0.127)
Because of the magnitude of the standardized coefficient, the correlation between the design factors and the emotional perception evaluation of the participants can be deduced to establish references for future designs. For example, regarding the analysis result for the casual/formal adjective pair, the 0.154 score for “H6 round hub shape” compared with the 0.114 score for “H1 water drop hub shape” indicates that the participants perceived the shape to be more “casual.” Similarly, the 0.454 score for “A1: three spokes” demonstrates that “casual” was the prevailing impression, whereas the 0.269 score for “H3: trapezoidal hub shape” indicates that “formal” was the prevailing impression. To compare the differences between the element-oriented model and the overall model, the values of the coefficient of determination (R2) for the 10 pairs of adjectives were arranged as shown in Table 12. The R2 values in the overall model were larger than those in the element-oriented model, indicating that the integrated model was more effective than the element-oriented model was in explaining variance. The R2 values in Table 12 reveal that the numerical value differences between classic/fashionable (esthetic factors) sturdy/delicate (operational strength factors), and simplistic/changeful and artificial/spontaneous (modernity factors) were more apparent, indicating that the overall model was more effective in predicting consumer assessment that was based on emotional perception. The increase in the R2 of the modernity factors was the most significant change; therefore, the overall model performed more efficiently in modernity assessment. To identify the modernity difference between products, the overall interrelations between the external features became critical, as the analysis results suggest. The increase of the R2 values for the esthetic and operational strength factors in the overall model were less significant, probably because consumers relied more on morphological features when evaluating a product.
4. Conclusions This study designed a Kansei engineering model that integrates the interrelations of constituent elements to determine consumer emotional perception and preferences in a case regarding steering wheel design. 1. Through factor analysis extraction, three types of factors affecting consumer emotional perception toward car steering wheels were identified: esthetic factors, which have qualities relevant to external esthetics; operational strength factors, which are used for comparing form and power; and modernity factors, which relate to modernity and esthetics. 2. From the results of multiple regression analysis, the correlations between design (appearance) factors and the emotional perception evaluation of participants can be ascertained to establish design principles based on specific impressions. These principles can guide design in the future. For example, regarding the casual/formal pair, suggestions regarding appearance can be made depending on the intended impression. When the standardized coefficient is positive, the item should be designed to give a more “formal” impression; the larger the value, the stronger the impression should be. However, when the standardized coefficient is negative, a “casual” impression should be emphasized; the smaller the negative value, the stronger the impression should be, as shown in Table 13. 3. To build models for Kansei engineering in the future, a comprehensive product design analysis should be conducted to consider the relationship between a product's form features and feature composition. Chuang and Chen (2004) proposed a design analysis model for considering both form features and feature composition. By using the design of a teapot as an example, Chuang and Chen indicated that a model that
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considers both form features and feature composition can more accurately predict consumers' Kansei evaluations. In the current study, the test samples were only near life-size images of steering wheels, which is a limitation of the study. Using real samples from automobile companies may enable a more comprehensive discussion. Future research may apply this Kansei evaluation model to other types of products to more thoroughly examine the effectiveness of the model. Furthermore, with the model proposed in this study as a basis, a fuzzy analytic hierarchy process can be adopted to determine drivers' steering wheel needs. Thus, drivers' considerations regarding operating steering wheels can be analyzed and the importance of these considerations can be studied. For example, intelligent vehicles are equipped with many functions and features, but drivers do not typically feel less burdened when operating such a vehicle. Therefore, further research can examine integrating four key functions into the steering wheel interface, namely telephone communications, multimedia player, GPS navigation, and gear shift paddles. In this regard, a superior telematics system can be created to provide convenient services for drivers, and the usability of the interface can be improved. Thus, this study lays the groundwork for further research that can serve as a crucial reference point for designing the interior products and designs of vehicles. References Chang, Y.M., Chen, H.Y., Lin, K.H., Hung, T.C., 2005. Exploring the affection of material surface attribution and vibration attribution to KANSEI image of tactile sense. Chinese Institute of Design (CID). J. Des. 10, 73e87. Chien, W.P., 2006. A Study on the Control Performance of the Car Steering Wheel (Master's Thesis). Tatung University, Taipei. Chuang, M.C., Chen, C.C., 2004. Exploring the relationship between the product form features and feature composition and user's Kansei evaluation. ROC J. Des. 9, 43e58. Chuang, M.C., Ma, Y.C., 2001. Expressing the expected product images in product design of micro-electronic products. Int. J. Ind. Ergon. 27, 233e245. Cross, N., 2000. Engineering Design Methods: Strategies for Product Design, third ed. Wiley, Chichester, UK.
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DeVellis, R.F., 1991. Scale Development: Theory and Applications. Sage, Newbury Park, CA. Fukushima, K., Kawata, H., Fujiwara, Y., Genno, H., 1995. Human sensory perception oriented image-processing in a color copy system. Int. J. Ind. Ergon. 15, 63e74. http://dx.doi.org/10.1016/0169-8141(94)00057-A. Goonetilleke, R.S., 2000. The comfort-discomfort phase change. In: Karwowski, W. (Ed.), International Encyclopedia of Ergonomics and Human Factors. Taylor and Francis, London, pp. 399e402. Goonetilleke, R.S., Shih, H.M., Fristch, J., 2001. Effects of training and representational characteristics in icon design. International Int. J. Hum. Comput. Stud. 55 (5), 741e760. Helander, M.G., Khalid, H.M., Lim, T.Y., Peng, H., Yang, X., 2013. Emotional needs of car buyers and emotional intent of car designers. Theor. Issues Ergonomics Sci. 14, 455e474. Hsu, S.H., Chuang, M.C., Chang, C.C., 2000. A semantic differential study of designers' and users' product form perception. Int. J. Ind. Ergon. 25, 375e391. Jiao, J.R., Zhang, Y., Helander, M., 2006. A Kansei mining system for affective design. Expert Syst. Appl. 30, 658e673. Jindo, T., Hirasago, K., 1997. Application studies to car interior of Kansei engineering. Int. J. Ind. Ergon. 19, 105e114. http://dx.doi.org/10.1016/S0169-8141(96)000078. Jindo, T., Hirasago, K., Nagamachi, M., 1995. Development of a design support system for office chairs using 3-D graphics. Int. J. Ind. Ergon. 15, 49e62. http:// dx.doi.org/10.1016/0169-8141(94)00056-9. Jindo, T., Hirasago, K., Nagamachi, M., Matsubara, Y., Kagamihara, Y., 1994. In: A Study of Kansei Engineering on Steering Wheel of Passenger Cars (Pp. 545e548). Japan-USA Symposium on Flexible Automation - II, Kobe, Japan. Khalid, H.M., 2001. Towards affective collaborative design. In: Smith, M.J., Salvendy, G., Harris, D., Koubek, R.J. (Eds.), Usability Evaluation and Interface Design. Proceedings of HCI International 2001 (Vol. 1). Lawrence Erlbaum, Mahwah, NJ. Nagamachi, M., 1995a. An Overview of Kansei Engineering (Japanese). Japanese Standards Association, Tokyo, Japan. Nagamachi, M., 1995b. Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15, 3e11. http:// dx.doi.org/10.1016/0169-8141(94)00052-5. Nagamachi, M., 2002. Kansei engineering as a powerful consumer-oriented technology for product development. Appl. Ergon. 33, 289e294. Tanoue, C., Ishizaka, K., Nagamachi, M., 1997. Kansei engineering: a study on perception of vehicle interior image. Int. J. Ind. Ergon. 19, 115e128. http:// dx.doi.org/10.1016/S0169-8141(96)00008-X. Yang, J., 2003. The icon recognition affected selection intention and behavior pattern of college students in playing lucky-hole poking game. J. Des. 8 (1), 51e63. Zheng, Z., 1993. Cognitive psychology e theory and practice, laureate publishing a study on car seat preferences. Ind. Des. 29 (2).
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International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon
Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design* Yu-Ming Chang a, Chun-Wei Chen b, * a b
Department of Creative Product Design, Southern Taiwan University, No. 1, Nantai St., Yongkang City, Tainan County 710, Taiwan, ROC Department of Industrial Design, National Cheng Kung University, No. 1, Dasyue Rd., East District, Tainan City 701, Taiwan, ROC
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 January 2015 Received in revised form 18 July 2016 Accepted 24 September 2016
In most studies on Kansei, the product form analysis model is based on the external features or elements of the product components. However, such an approach cannot completely convert consumer emotional perceptions into design elements. Therefore, this study combined the ergonomic technology used in Kansei engineering with the unique cognitive ability of humans to identify patterns and establish an emotional perception model that can integrate the overall interrelations of the constituent elements. Steering wheel design was used as the object of examination. Three types of adjectives were applied to describe the constituent elements. The first type was esthetic factors and involve external esthetics. The second type comprised two pairs of adjectives, sturdy/delicate and lightweight/heavy, called operational strength factors because they relate to form and strength. The third type comprised simplistic/changeful and artificial/spontaneous, called modernity factors because they pertain to the modern sense of beauty of the parts. Multiple linear regression analysis was used to construct a Kansei engineering model and compare the performance of individual elements and the product as a whole. The results show that the R2 values in the overall model were greater than those in the element-oriented model, indicating that the integrated model outperformed the element-oriented model in variance explanation. The differences between the numerical values of the adjective pairs classic/fashionable (esthetic factors), sturdy/delicate (operational strength factors), and simplistic/changeful and artificial/spontaneous (modernity factors) were significant, demonstrating that the overall model is useful in predicting how consumers make assessments according to emotional perceptions. The R2 increase of the modernity factors was the most obvious, indicating that the overall model assesses modernity more accurately. Comparing results and verifying test samples demonstrated that the overall model is more useful in predicting consumer appraisals that are based on emotional perception. Relevance to industry: This study determined that esthetics, operational strength, and modernity are the three most crucial factors in the emotional perceptions and preferences of consumer regarding steering wheel design. The results demonstrate that a model that integrates constituent elements can evaluate consumer behavior and assist product designers in understanding consumers. © 2016 Elsevier B.V. All rights reserved.
Keywords: Constituent factor Overall interrelation Kansei engineering Design assessment Car steering wheel
1. Introduction 1.1. Background and study motivation The car part that a driver interacts with most directly and
* This study combined the ergonomic technology used in Kansei engineering with the unique cognitive ability of humans to identify patterns and establish an emotional perception model that can integrate the overall interrelations of the constituent elements. Steering wheel design was used as the object of examination. * Corresponding author. E-mail address: [email protected] (C.-W. Chen).
http://dx.doi.org/10.1016/j.ergon.2016.09.010 0169-8141/© 2016 Elsevier B.V. All rights reserved.
frequently is neither the appearance nor power system of the car, but rather its interior design, of which the steering wheel is the most fundamental component. In addition to the shape of the steering wheel, the gripping experience and the convenience during operation are also crucial. In an era of emotional consumption, the sense of satisfaction with a product that a consumer derives and that leads to a purchase decision is no longer merely a consideration regarding function or practical use. Sense perception is applied to comprehensively and perceptually assess before a purchase decision is made (Chang et al., 2005). When a consumer wants to buy something, he/she will have a kind of feeling and image (Kansei in Japanese) in his/her mind. If the consumer's
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feeling could be implemented in the new product, he/she would be more satisfied with the product (Nagamachi, 2002). Affective design has received much attention from both academia and industries. It aims at incorporating customers' affective needs into design elements that deliver customers' affective satisfaction. The main challenge for affective design originates from difficulties in mapping customers' subjective impressions, namely Kansei, to perceptual design elements (Jiao et al., 2006). In today's competitive environment, satisfying customer needs has become a great concern of almost every company (Cross, 2000). While there are various customer needs, the functional and affective needs have been recognized to be of primary importance for customer satisfaction (Khalid, 2001). Following the progress achieved in the auto industry in recent years, consumer demand for quality in various aspects of automobiles has increased. The control panel in new cars often features an LCD screen that can integrate DVDs, stereos, GPS devices, digital TVs, and smartphones. In some high-end models, the steering wheel is equipped with a dial control featuring consolidated functions. Further examples such as LCD screens in headrests and constant air conditioning control suggest that the trend of installing entertainment devices in cars is irreversible. However, car design is complex and changing, involving considerations of interactions among the driver, the vehicle, and the environment. Within Taiwan and abroad, the comfortability of seats and driving is of particular interest. However, one other crucial factor has often been neglected: the geometric elements of the driver (human) and the design of the steering wheel (object). The turn and angle design for the geometric structure of the steering wheel affects driver comfort. Drivers hold the steering wheel until the journey ends; An understanding of the pressure patterns that are appropriate for the human body (that is, the patterns that reduce discomfort or improve what is called comfort) can make product design and usage very satisfying and fulfilling (Goonetilleke, 2000). Car owners look beyond functionality to consider emotional design features, car designers may need to learn how to include emotional design features as a design procedure (Helander et al., 2013). Therefore, control devices built into the steering wheel have drawn the attention of drivers. Most drivers, particularly those who drive for work, such as taxi drivers, often experience neck, shoulder, lower back, or waist pain after driving a long distance or for a long period (Chien, 2006). Hence, in addition to appropriate operating environments and comfortable seats, drivers require a well-designed steering wheel. Worldwide vehicle sales surpassed 80 million in 2013, and the market continued to grow in 2014, which led to a positive outlook for profitability in the global automotive industry. The two major automotive markets of China and the United States are expected to continue growing. Furthermore, as automobile companies continue to innovate, they will attract more drivers to purchase their products. Thus, more attention will be paid to how these automobile companies innovate and make safer vehicles catering to the needs and interests of drivers. Quantitative data on the relationship between design elements and user evaluations is useful to product designers and managers in formulating design strategies (Hsu et al., 2000). Therefore, this study identified the effects of steering wheel devices on driver perception to design a comfortable entertainment system with quality steering wheel interfaces.
Yamamoto, emphasized the “contribution of automobiles to cultural creativity” and explained his theory of car-driving culture. He proposed applying Kansei engineering in designing automobile interiors to satisfy the needs and emotional demands of car users and to increase the comfort of automobiles. Through Kansei engineering, designers can access objective data and more comprehensively understand consumer preferences and perception (Chuang and Chen, 2004). During exploration of product appearance and consumer impressions, Kansei engineering is a crucial cut-in point. Developing product design and theory according to consumer feelings and perceptions (psychological impressions) is now common (Chang et al., 2005). Through engineering approaches, human emotional perceptions are quantified to establish a functional relationship between the quantity of the emotional perception and the physical quantity (form) of the stimulus that triggers the emotional perception; this relationship is the foundation of design development (Zheng, 1993). In other words, Kansei engineering is being used to convert customer demands and feelings into product design elements and realize the ideas in the minds of consumers (Nagamachi, 1995a) (Nagamachi, 1995b) (Jindo et al., 1994). In most studies on Kansei, the external design and features of products have formed the core of analysis. Therefore, Kansei engineering can be defined as a technology that quantifies and presents the feelings that people have toward objects (products) and that explores design elements that match certain emotions that people have. The word “Kansei” can be interpreted as people's feelings toward or impressions of an object, representing their psychological expectations and perceptions. Businesses in Japan have widely applied Kansei engineering in various fields, such as in the design of cars, office chairs (Jindo et al., 1995), interior design of cars (Jindo and Hirasago, 1997), vehicle interior image (Tanoue et al., 1997), and color planning for product appearance (Fukushima et al., 1995). However, from the perspective of cognitive psychology, further analysis suggests that ambiguity, uniqueness and dominance are three important aspects to consider when designing and developing icons.(Goonetilleke et al., 2001). Examining consumer recognition and perception of a product by identifying only the features of the object cannot explain human cognitive behavior completely. The overall interrelations between components are also critical. Thus, this study developed a Kansei engineering model to analyze and determine the interrelations of constituent elements. The three main objectives were to (1) investigate consumer design preferences, (2) identify the types of emotional perceptions toward product form, and (3) understand the viability and capacity of the two emotional perception analysis systems regarding the constituent elements and their overall interrelations.
1.2. Study objectives
In the development of products, methods enabling the designer to create an appropriate image for a product so that it may communicate with the user are always critical issues (Chuang and Ma, 2001). Pictures of steering wheels of different designs were obtained from books, magazines, and almanacs for use as the stimulation test samples. Because the chief objective of this study was to examine the correlations among the constituent elements,
The software and hardware installed in automobiles, such as GPS and mobile phone hands-free devices, are increasingly diverse, increasingly complicating driving. In a speech at the World Automotive Technology Conference and the U.S. Automaker Seminar in 1986, former Mazda Motor Cooperation president, Kenichi
2. Methods of study and implementation In this study, two Kansei engineering systems were developed to analyze the design of car steering wheels according to the overall interrelations of constituent elements. The results were interpreted and predictive capacities were compared. The steps of the development are as follows: 2.1. Stimulation test sample selection
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participants, the researcher first removed all brand logos from the samples and converted the images into 30 cm 30 cm black-andwhite prints. A focus group research method was then used to select representative samples. The focus team (product design specialists) comprised six graduate students from the Graduate Institute of Industrial Design at National Cheng Kung University. Four students were male, two were female, all were industrial design majors in undergraduate and graduate programs, and three had more than two years of practical design experience. To avoid discrepancy regarding form vs content, the focus team, through direct visual perception, classified the test samples according to the visual characteristics of the images. To classify the 114 test samples by form contrast, the KJ method was adopted. This method is used for qualitative analysis based on intuition and for innovating through teamwork. Because this method is used for analyzing how participants perceive the images, it enables the characteristics of images to be more clearly understood (Yang, 2003) The team members classified the samples with similar appearances into groups and to select 45 representative samples for the emotional perception survey. The team members discussed and divided the samples into 12 groups. The representative samples were then chosen (Fig. 2). Despite the logo removal, interference from preconceptional impressions remained in the overall assessment and interpretation. Consequently, the representative samples were traced to produce linear sketches (Fig. 3) that were used as the final representative test samples.
Fig. 1. 114 Test samples initially collected.
Fig. 2. Final representative test samples.
2.2. Vocabulary selection
Fig. 3. Linear sketches of final representative test samples.
structural relations, and quantity of emotional perception toward the product, color was considered. As shown in Fig. 1, 114 samples were collected. To prevent brand names from influencing
Eighty contrasting pairs of adjectives suitable for describing product impressions were selected from related professional publications, magazines, and research papers for the emotional perception vocabulary (Table 1). The focus team discussed the qualities of the steering wheel samples to be tested, and applied the Delphi method to the adjective pairs that had received more than half of the votes. The Delphi method is a structured technique for decision-making. During the process of collecting messages, this method is used to obtain relatively objective messages, opinions, or insights from specialists who make independent, repeated, and subjective judgements. Eliminated unsuitable pairs of adjectives, and retained the 20 most appropriate pairs (Table 2). Ten participants (five men and five women; all graduate students at the Graduate Institute of Industrial Design at National Cheng Kung
Table 1 Eighty adjectives. Smooth Simple Quiet Warm Classy Sturdy Sensible Attractive Unique Geometric
Sophisticated Harmonious Ordinary Male Formal Intricate Generous Safe Changeful Organic
Natural Heavy High-class Fashionable Avant-garde Magnificent Elegant Classic Expensive Lively
Conventional Modern Comfortable Young Trendy Bright Lightweight Soft Sexy Dull
Noble Innovative Round Casual Plain Thin Introverted Rational Monotonous Complex
Technological Simplistic Neutral Exclusive Dynamic Passionate Rich Steady Embellished Minimal
Delicate Artificial Futuristic Professional Tasteful Dazzling Valuable Intimate Common Cold
Low-key Contemporary Novel Pleasurable Luxurious Tough Loud Friendly Reserved Rugged
Table 2 Twenty contrasting pairs of adjectives. SophisticatedeSmooth ExclusiveeCommon ComplexeMinimal ClassiceFashionable FuturisticeConventional
IntrovertedeMagnificent CasualeFormal SofteTough IntricateeRugged QuieteLoud
SimplisticeChangeful EmbellishedePlain LivelyeDull ColdeWarm High-classeOrdinary
ArtificialeNatural SteadyeLightweight ReservedeAvant-garde ThineHeavy GeometriceOrganic
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Table 3 Cluster analysis of the 20 contrasting pairs of adjectives. Ward's linkage method Stage
Cluster combined Cluster 1
Cluster 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
15 11 12 7 7 1 7 2 12 4 1 10 2 1 6 4 1 1 1
20 17 13 19 14 5 11 3 18 15 9 16 12 7 10 8 6 4 2
Agglomeration schedule Coefficients
25.000 50.000 76.000 102.500 136.000 174.000 215.400 258.900 304.233 349.900 404.567 468.567 540.533 613.717 691.717 778.300 972.232 1188.933 1748.000
Stage cluster first appears Cluster 1
Cluster 2
0 0 0 0 4 0 5 0 3 0 6 0 8 11 0 10 14 17 18
0 0 0 0 0 0 2 0 0 1 0 0 9 7 12 0 15 16 13
Next stage
10 7 9 5 7 11 14 13 13 16 14 15 19 17 17 18 18 19 0
University) applied the 20 pairs of adjectives to the 45 steering wheel samples, and a preliminary SD test was conducted. Ward's hierarchical clustering method was used to analyze the results (Table 3). From the cluster dendrogram (Fig. 4), 10 final representative pairs of adjectives were chosen (Table 4) to alleviate the burden of the participants in the official SD test. 2.3. Product form deconstruction and description The form of a product is the aggregate result of the components,
such as the texture, position, hub, and number of spokes of a steering wheel. In emotional perception research and application, product form deconstruction and analysis are often based on such a morphological approach. Through a systematic analysis, this approach analyzes all possible product form. To solve problems in product design, relevant independent factors (items) are listed according to the functions or features of the product. Thus, on the basis of each independent factor, all possible solutions (category) are listed. This study, however, also considers the overall interrelations between the components. The focus team discussed the two aspects of the constituent elements (the form and style of each component) and their overall interrelations (the corresponding position, connection, and ratio of the components) and to deconstruct the features of the 45 test samples to form a new design. After ample discussion and analysis, the focus team identified the steering wheel morphological features and constituent relations that influenced emotional perception assessment (Table 5).
2.4. Emotional perception assessment of appearance data To confirm the relations between the appearance of steering wheels and the emotional perception vocabulary, an SD survey was administered to 30 junior year students from the Department of Multimedia and Entertainment Science at Southern Taiwan University of Technology. The 30 participants all had a driving license, were students enrolled in continuing education programs, and were aged 22e28 years. The 10 pairs of adjectives listed in Table 2 were used as a measure and a 9-point Likert scale (1e9 points from left to right) was used to evaluate the 45 steering wheel test samples.
Fig. 4. Dendrogram for cluster analysis of the 20 contrasting pairs of adjectives.
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Table 4 Adjective pairs used in SD test. Simplistic -Changeful Sturdy - Delicate
Artificial - Spontaneous Classic - Fashionable
Unique - Common Lightweight - Heavy
Casual - formal Futuristic - Conventional
Embellished - plain Classy - Ordinary
Table 5 Morphological analysis.
Morphological Feature
Overall Interrelation
Item
Category
Number of Spokes
A1. 3 spokes
A2. 4 spokes
Touch of spokes
B1. Uneven surface design
B2. No decorative uneven surface design
Positioning of spokes
C1.90/180
C2.90/145
C3.90/130
Wheel rim material
D1. Leather cover
D2. Walnut veneer cover
D3. Composite cover
Hub surface
E1. Unibody
E2. Jointed
Hub shape
F1. Water drop- shaped
F2. Pyramidal
F3. Trapezoidal
F4. Semi- circular
F5. Diamond shaped
F6. Round
Spoke-rim joint
G1. Gapless joint
G2. Gapped joint
Visible areas of center, spokes and rim
H1. Approximate visible areas
H2. Unequal visible areas
Switch positions in relation to the hub
I1. Symmetrical on both sides of the hub
I2. Surrounding the center
3. Results and discussion 3.1. Emotional perception assessment results Initially, the average numbers of times the 30 participants applied the adjectives were established and factor analysis was conducted. Table 6 shows that the correlation coefficient from the KMO and Bartlett's test achieved a significance level of 0.714 (sig ¼ 0.000). According to principal component analysis, three elements with an eigenvalue exceeding 1 were extracted. Table 7 shows that the three types of factors together explained 76.043% of the total variance. To ensure that the statistical data acquired using the questionnaire in this study exhibited satisfactory reliability, a reliability analysis of the data was conducted to determine the Cronbach's a value to be 0.711 (Table 8), and the overall statistics of the items were examined (Table 9). According to DeVellis' (1991) suggestion that a
Table 6 KMO and Bartlett's test.
3.2. Emotional perception assessment mode of element-oriented and overall models: construction and comparison
KMO and Bartlett's test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. Chi-Square df Sig
Cronbach's a of between 0.65 and 0.70 indicates moderate reliability, and that a Cronbach's a of between 0.70 and 0.80 entails high reliability, the questionnaire results of the present study exhibited high reliability. Through varimax orthogonal rotation, the loadings of the three element factors were obtained, as shown in Table 10. The variances in relation to the esthetic, operational strength, and modernity factors were 44.62%, 19.52%, and 11.90%; the accumulated total variance was 76.043%. According to the meanings of the adjectives, the factors can be categorized into esthetic factors: six pairs of adjectives with qualities relevant to comparing external esthetics, namely, embellished/plain, futuristic/conventional, classy/ordinary, unique/common, classic/fashionable, and casual/ formal; operational strength factors: two pairs of adjectives with qualities used for comparing form and power, namely, sturdy/ delicate and lightweight/heavy; and modernity factors: two pairs of adjectives with qualities used for assessing the level of modernity and esthetics, namely, simplistic/changeful and artificial/ spontaneous.
0.714 173.729 45 0.000
To contrast the results of the two Kansei engineering models from the form analysis of the element-oriented model and the overall model, multiple linear regression analysis was used,
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Table 7 Total variance explained. Total variance explained Component
1 2 3 4 5 6 7 8 9 10
Initial eigen values
Extraction sums of squared loadings
Rotation
Total
% Of Variance
Cumulative %
Total
% Of Variance
Cumulative %
Total
4.462 1.952 1.190 0.875 0.526 0.309 0.273 0.195 0.123 0.095
44.622 19.517 11.903 8.752 5.259 3.096 2.728 1.945 1.227 0.953
44.622 64.139 76.043 84.795 90.054 93.147 95.875 97.820 99.047 100.000
4.462 1.952 1.190
44.622 19.517 11.903
44.622 64.139 76.043
4.224 2.092 2.108
Extraction Method: Principal Component Analysis. a When components are correlated, sims of squared loadings cannot be added to obtain a total variance.
Table 8 Statistics of reliability. Cronbach's alpha
Number of items
0.711
10
establishing the relations between the car steering wheel form factors and the emotional perception vocabulary. The design factors obtained from the form analysis of the 45 test samples were defined as independent variables, and the averages of the emotional perception evaluations of the 10 adjective pairs were set as the dependent variables. Backward regression was then used to conduct multiple regression analysis of each pair of adjectives, locating the form factors that significantly influenced the emotional perception vocabulary. Simultaneously, the design factors were
listed according to a nominal scale, and dummy variables were used in the multiple regression analysis. Zero and 1 represented nonquantifiable characteristics and attributes. In the elementoriented form analysis model, the design factors were mainly element characteristics, whereas in the overall model, the design factors were combinations of element characteristics and the overall interrelations of the elements. Table 11 show the results of the regression analysis of the two models. In Table 11, the blank spaces (such as B3: No decorative, uneven surface design) signify that the influence of the element did not achieve the significance level of 0.05. A higher standardized coefficient indicates that the participants considered the impression of the second adjective in the pair to be more useful in design development. A negative standardized coefficient means that the participants believed that the first adjective of the pair exerted a stronger influence on impression creation.
Table 9 Overall statistics of the items.
Simplistic e Changeful Artificial e Spontaneous Unique - Common Casual e Formal Embellished e Plain Sturdy e Delicate Classic e Fashionable Lightweight e Heavy Futuristic e Conventional Classy e Ordinary
Scale mean obtained when the item was excluded
Scale variance obtained when the item was excluded
Revised total item correlation
Cronbach's alpha obtained when the item was excluded
28.7556 29.5556 29.0667 28.7333 29.1778 28.8444 28.4222 28.2222 29.0444 28.5778
41.553 44.934 37.882 40.973 38.195 47.225 48.340 39.677 38.316 40.113
0.326 0.377 0.563 0.391 0.630 0.068 -0.036 0.502 0.585 0.446
0.698 0.693 0.653 0.686 0.645 0.734 0.761 0.667 0.651 0.676
Table 10 Post-rotation factor loadings. Adjective
Factor type 1 esthetic factor
Factor type 2 operational Strength factor
Factor type 3 Modernity factor
Embellished e Plain Futuristic e Conventional Classy e Ordinary Unique - Common Classic e Fashionable Casual e Formal Sturdy e Delicate Lightweight e Heavy Simplistic e Changeful Artificial e Spontaneous Eigenvalue Variance Explained Accumulated Variance Explained
0.931 0.894 0.882 0.837 0.697 0.549 0.048 0.245 0.465 0.111 4.224 44.62% 44.62%
0.303 0.213 0.146 0.143 0.300 0.544 0.907 0.832 0.110 0.027 2.092 19.52% 64.139%
0.314 0.330 0.040 0.397 0.522 0.062 0.168 0.094 0.851 0.839 2.108 11.90% 76.043%
Table 11 Regression analysis of the element-oriented and overall models. Attribute
Futuristic e Conventional
Classy e Ordinary
Unique e Common
Classic e Fashionable
Casual e Formal
Sturdy e Delicate
Lightweight e Heavy
Simplistic e Changeful
Artificial e Spontaneous
Item
Category
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
Standardized Coefficient
A. Number of Spokes
3 spokes 4 spokes Uneven surface design No decorative uneven surface design 90/180 90/145 90/130 Leather cover Walnut veneer cover Composite cover Unibody Jointed Water drop-shaped Pyramidal Trapezoidal Semi-circular Diamond-shaped Round Gapless joint Gapped joint Approximate visible areas Unequal visible areas Symmetrical on both sides of the hub Surrounding the hub Constant Significance
0.212 0.121 0.322
0.124 0.349 0.322
0.063 0.432 0.212
0.332 0.434
0.235 0.453 0.225
0.454 0.112
0.221 0.323 0.065
0.542 0.431 0.064
0.467 0.543 0.098
0.060 0.654
0.165
0.187
0.132
0.062
0.065
0.061
0.325
0.343
0.232
0.437 0.276 0.254 0.215 0.211 0.331 0.196 0.174 0.082 0.197 0.214 0.061 0.135 0.062 0.072 0.176 0.178 0.021 0.097
0.118 0.211 0.132 0.176 0.187 0.232 0.276 0.182 0.098 0.212 0.223 0.083 0.207 0.121 0.063 0.113 0.111 0.078 0.054
0.212 0.112
0.238 0.320 0.245 0.134 0.232 0.243
0.154 0.268 0.299 0.119 0.063 0.213 0.137 0.012 0.097 0.243 0.112 0.079 0.095 0.154 0.133 0.091 0.065 0.132
0.098 0.332 0.221 0.121 0.146 0.199 0.165 0.153 0.138
0.112 0.142 0.156 0.053 0.115 0.098 0.065
0.267 0.211 0.165 0.255 0.113 0.345 0.324 0.157 0.215 0.210 0.165 0.089 0.321 0.231 0.124 0.132 0.156 0.067 0.124
0.132
0.163 0.217 0.098 0.199 0.215 0.142 0.091 0.139 0.124 0.187 0.087 0.208 0.101 0.202 0.094 0.099 0.087 0.067
0.189 0.171 0.061 0.124 0.216 0.211 0.211 0.143 0.154 0.091 0.095
0.115 4.234 0.012 R ¼ 0.884 R2 ¼ 0.781
0.165 3.884 0.008 R ¼ 0.888 R2 ¼ 0.789
0.102 4.225 0.011 R ¼ 0.781 R2 ¼ 0.610
0.156 4.004 0.030 R ¼ 0.785 R2 ¼ 0.616
0.093 3.575 0.021 R ¼ 0.784 R2 ¼ 0.615
0.111 5.445 0.022 R ¼ 0.770 R2 ¼ 0.593
0.211 3.132 0.013 R ¼ 0.911 R2 ¼ 0.830
B. Touch of Spokes
C. Positioning of Spokes
D. Wheel Rim Material
E. Crossbar F. Hub Shape
G.Spoke-rim joint H.Visible areas of hub, spokes and rim I.Switch positions in relation to the hub
R and R2 values
0.093
0.087 0.112
0.102
0.064
0.121 0.221 0.364 0.264 0.132 0.226 0.176 0.108 0.114 0.143 0.269 0.143 0.061 0.154 0.021 0.097 0.102 0.043 0.127
0.234 4.255 0.002 R ¼ 0.906 R2 ¼ 0.821
0.202 3.755 0.001 R ¼ 0.924 R2 ¼ 0.854
0.202 5.064 0.020 R ¼ 0.775 R2 ¼ 0.601
0.321 0.087 0.443 0.209 0.183 0.099 0.224 0.326 0.102 0.321 0.112 0.133
0.146 0.162 0.112 0.119 0.132 0.232 0.061
0.223
0.249 0.332 0.216 0.435 0.237
Y.-M. Chang, C.-W. Chen / International Journal of Industrial Ergonomics 56 (2016) 97e105
Embellished e Plain
103
104
Y.-M. Chang, C.-W. Chen / International Journal of Industrial Ergonomics 56 (2016) 97e105
Table 12 Comparison of the R2 values of the element-oriented model and the overall model. Element Attribute
Esthetics
Operational strength
Modernity
Adjective pair
Embellished e Plain
Futuristic e Conventional
Classy e Ordinary
Unique - Common
Classic e Fashionable
Casual e Formal
Sturdy e Delicate
Lightweight - heavy
Simplistic - Changeful
Artificial - Spontaneous
R2 value in featureoriented model R2 value in integrated model R2 gain value Adjusted R2 value in feature-oriented model Adjusted R2 value in integrated model Adjusted R2 gain value
0.774
0.783
0.605
0.817
0.828
0.593
0.581
0.598
0.601
0.819
0.781
0.789
0.610
0.821
0.854
0.601
0.616
0.615
0.593
0.830
0.007 0.552
0.006 0.575
0.005 0.412
0.004 0.675
0.026 0.689
0.008 0.374
0.035 0.368
0.017 0.376
0.008 0.397
0.011 0.679
0.568
0.580
0.425
0.687
0.702
0.397
0.430
0.427
0.374
0.699
0.016
0.005
0.013
0.012
0.013
0.023
0.062
0.051
0.023
0.020
Table 13 Relations between emotional perception vocabulary and design factors. Emotional impression
Individual element features
Overall interrelations
Casual
Number of spokes: 3 Spokes (0.454), Touch of spokes: no uneven surface design (0.065), Positioning of spokes: 90/130 (0.364), Wheel rim material: Leather cover (0.264), Hub surface: Jointed (0.108), Hub shape: round (0.154)
Formal
Number of spokes: 4 Spokes (0.112), Positioning of spokes: 90/180 (0.321), Wheel rim material: Walnut veneer cover (0.132), Hub surface: Unibody (0.176), Hub shape: trapezoidal (0.269)
Spoke-rim joint: Gapped joint (0.097), Visible areas of hub, spokes and rim: Unequal visible areas (0.043), Switch positions in relation to the hub: Surrounding the hub (0.202) Spoke-rim joint: Gapless joint (0.021), Visible areas of hub, spokes and rim: Approximate visible areas (0.102), Switch positions in relation to the hub: Symmetrical on both sides (0.127)
Because of the magnitude of the standardized coefficient, the correlation between the design factors and the emotional perception evaluation of the participants can be deduced to establish references for future designs. For example, regarding the analysis result for the casual/formal adjective pair, the 0.154 score for “H6 round hub shape” compared with the 0.114 score for “H1 water drop hub shape” indicates that the participants perceived the shape to be more “casual.” Similarly, the 0.454 score for “A1: three spokes” demonstrates that “casual” was the prevailing impression, whereas the 0.269 score for “H3: trapezoidal hub shape” indicates that “formal” was the prevailing impression. To compare the differences between the element-oriented model and the overall model, the values of the coefficient of determination (R2) for the 10 pairs of adjectives were arranged as shown in Table 12. The R2 values in the overall model were larger than those in the element-oriented model, indicating that the integrated model was more effective than the element-oriented model was in explaining variance. The R2 values in Table 12 reveal that the numerical value differences between classic/fashionable (esthetic factors) sturdy/delicate (operational strength factors), and simplistic/changeful and artificial/spontaneous (modernity factors) were more apparent, indicating that the overall model was more effective in predicting consumer assessment that was based on emotional perception. The increase in the R2 of the modernity factors was the most significant change; therefore, the overall model performed more efficiently in modernity assessment. To identify the modernity difference between products, the overall interrelations between the external features became critical, as the analysis results suggest. The increase of the R2 values for the esthetic and operational strength factors in the overall model were less significant, probably because consumers relied more on morphological features when evaluating a product.
4. Conclusions This study designed a Kansei engineering model that integrates the interrelations of constituent elements to determine consumer emotional perception and preferences in a case regarding steering wheel design. 1. Through factor analysis extraction, three types of factors affecting consumer emotional perception toward car steering wheels were identified: esthetic factors, which have qualities relevant to external esthetics; operational strength factors, which are used for comparing form and power; and modernity factors, which relate to modernity and esthetics. 2. From the results of multiple regression analysis, the correlations between design (appearance) factors and the emotional perception evaluation of participants can be ascertained to establish design principles based on specific impressions. These principles can guide design in the future. For example, regarding the casual/formal pair, suggestions regarding appearance can be made depending on the intended impression. When the standardized coefficient is positive, the item should be designed to give a more “formal” impression; the larger the value, the stronger the impression should be. However, when the standardized coefficient is negative, a “casual” impression should be emphasized; the smaller the negative value, the stronger the impression should be, as shown in Table 13. 3. To build models for Kansei engineering in the future, a comprehensive product design analysis should be conducted to consider the relationship between a product's form features and feature composition. Chuang and Chen (2004) proposed a design analysis model for considering both form features and feature composition. By using the design of a teapot as an example, Chuang and Chen indicated that a model that
Y.-M. Chang, C.-W. Chen / International Journal of Industrial Ergonomics 56 (2016) 97e105
considers both form features and feature composition can more accurately predict consumers' Kansei evaluations. In the current study, the test samples were only near life-size images of steering wheels, which is a limitation of the study. Using real samples from automobile companies may enable a more comprehensive discussion. Future research may apply this Kansei evaluation model to other types of products to more thoroughly examine the effectiveness of the model. Furthermore, with the model proposed in this study as a basis, a fuzzy analytic hierarchy process can be adopted to determine drivers' steering wheel needs. Thus, drivers' considerations regarding operating steering wheels can be analyzed and the importance of these considerations can be studied. For example, intelligent vehicles are equipped with many functions and features, but drivers do not typically feel less burdened when operating such a vehicle. Therefore, further research can examine integrating four key functions into the steering wheel interface, namely telephone communications, multimedia player, GPS navigation, and gear shift paddles. In this regard, a superior telematics system can be created to provide convenient services for drivers, and the usability of the interface can be improved. Thus, this study lays the groundwork for further research that can serve as a crucial reference point for designing the interior products and designs of vehicles. References Chang, Y.M., Chen, H.Y., Lin, K.H., Hung, T.C., 2005. Exploring the affection of material surface attribution and vibration attribution to KANSEI image of tactile sense. Chinese Institute of Design (CID). J. Des. 10, 73e87. Chien, W.P., 2006. A Study on the Control Performance of the Car Steering Wheel (Master's Thesis). Tatung University, Taipei. Chuang, M.C., Chen, C.C., 2004. Exploring the relationship between the product form features and feature composition and user's Kansei evaluation. ROC J. Des. 9, 43e58. Chuang, M.C., Ma, Y.C., 2001. Expressing the expected product images in product design of micro-electronic products. Int. J. Ind. Ergon. 27, 233e245. Cross, N., 2000. Engineering Design Methods: Strategies for Product Design, third ed. Wiley, Chichester, UK.
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