Modeling Product Form Preference Using Gestalt Principles Semantic Space and Kansei

Proceedings of the ASME 2012 International Design Engineering Technical Conferences &Computers and Information in Engineering Conference

IDETC/CIE 2012August 12-15, 2012, Chicago, Illinois, United States

DETC2012/DTM-70434

MODELING PRODUCT FORM PREFERENCE USING GESTALT PRINCIPLES,SEMANTIC SPACE, AND KANSEI

Jose E. Lugo ⇤

Stephen M. BatillDesign Automation Laboratory

Department of Aerospace and Mechanical Engineering

University of Notre Dame

Notre Dame, Indiana 46556

Email: [email protected]

[email protected]

Laura CarlsonSpatial Cognition Laboratory

Department of Psychology

University of Notre Dame

Notre Dame, Indiana 46556

Email: [email protected]

ABSTRACTEngineers describe design concepts using design variables.

Users develop their visual judgment of products by mentallygrouping design variables according to Gestalt principles, ex-tracting meaning using semantic dimensions and attaching at-tributes to the products, as reflected in Kansei methodology. Thegoal of this study was to assess how these different sources of in-formation and representations of product form (design variables,Gestalt variables, Kansei attributes, and semantic dimensions)could combine to best predict product preference for both de-signers and users. Sixteen wheel rim designs were created usingfour design variables that were also combined into higher-orderGestalt variables. Sixty-four participants viewed each rim, andrated it according to semantic dimensions and Kansei attributes,and provided an overall “like” rating. The most reliable pre-diction of product preference were developed using Gestalt vari-ables in combination with the meaning and emotion the usersattached to the product. Finally, implications for designers arediscussed.

1 INTRODUCTIONThe engineering design process involves a sequence of in-

terrelated decisions. These decisions are based upon acquired

⇤Address all correspondence to this author.

knowledge, experience and project-specific information derivedfrom modeling, analysis and experimentation. The decisions aredriven by the roles and responsibilities of groups or individualswho participate in the process. Some of the decisions are ob-jective and supported by quantitative information and some aresubjective and result from emotional assessments [1].

Traditionally, industrial or product designers tend to focuson decisions related to form or visual appearance with the in-tent to exploit the opportunities provided by a new design [2–4],while engineering designers are faced with the pragmatic deci-sions associated with assuring that the design meets the func-tional constraints set forth in design requirements and specifica-tions, as in Zuo et al. [5]. In today’s design challenges these rolesoverlap more and more with the realization of the importance ofuser-centered design and the subsequent recognition that usersalso influence the design process.

For users certain properties can influence how a product isjudged. A property is an attribute, quality, or characteristic ofa product. Furthermore, properties range from materials and di-mensions to geometric features. In the design of a product, a de-signer can specify some of the properties. The set of propertiesthat the designer can control will be referred to as design vari-ables. Design variables are physical features and dimensions ofa product. Examining how design variables influence the subjec-tive judgment and preference of a product is one of the purposes

1 Copyright c� 2012 by ASME

of this study.Both the users and the designers have a role in developing

the requirements on both form and function of a new design.Early in the design process concepts can be expressed in waysthat represent potential form (concept sketches or renderings) orways that represent potential function (target design specifica-tions) but in both cases these are only expressions of what theproduct may become. Prior to the actual product being built,most of the representations of a product are visual [6]. They takethe form of sketches, drawings, CAD and Virtual Reality (VR)models. Decisions made early in the design process based uponthese conceptual expressions of a product are influenced by howboth designers and users react to visual representations of a prod-uct [7, 8].

The human visual system has a bias to group certain prop-erties (i.e a visual representation of a product) into higher ordervariables following Gestalt principles; this study will be referringto these variables as Gestalt variables. Therefore, in additionto the information provided by design variables, designers andusers also perceive the product in terms of these Gestalt variables.The higher order properties included in this study are proximity,closure and continuation. However, there is not a unique set ofGestalt variables for a given visual representation of a product.The use of Gestalt principles to discretize the visual representa-tion of a product, and to model the predicted product preference,are the novel applications of these principles presented in thisstudy.

Although, one would hope that most of the design decisionsare rational and justifiable based upon quantifiable information,that is not always the case, particularly early in the process.There are situations where the decisions are intuitive, often jus-tified by experience, but sometimes they are emotional and areinfluenced by factors that might not normally be associated withengineering design. Nevertheless, they are important as they af-fect the outcome of the process. This study uses Kansei words,from Kansei engineering methodology, to probe the emotions in-ferred from a product, and then predict product preference. TheKansei words used in this study have product-specific meaningbecause they are the terms used by users to explain attributes ofthe product.

Another attribute that is represented by a product’s form isits meaning [9]. In this study, the semantic space is introducedto measure the meaning designers and users assign to a product’sform. The semantic space is a 3-dimensional space that mea-sures meaning with respect to 3 factors: evaluation, activity andpotency. Each of these dimensions are then used to predict prod-uct preference.

Previous work has shown that expertise influences productform assessment [7]. Thus this study used two subject groupsthat varied in expertise. Expertise was defined with respect tothe potential role of the subject in the design process. Expertswere students with mechanical or industrial design majors who

had some level of experience in making design decisions. Non-experts were students with other majors and were potential usersof the product. This distinction was used to investigate whetherthis small variation in expertise altered the influence of these dif-ferent sources of information: design variables, Gestalt variables,Kansei words and semantic space.

The first goal of this study was to demonstrate which Gestaltvariables can be used to predict product preference. The hy-pothesis is that the Gestalt variables (e.g. proximity, etc.) arerepresentative of the mental process when the product form isvisually explored. When a subject looks at a product their pref-erence mental model is formed by discretizing the product shapefollowing Gestalt principles. This implies that representing thedesign space with traditional engineering design variables maynot be as effective when product preference is the desired out-come. The second goal was to explore whether different sourcesof information and representations of product form (design vari-ables, Gestalt variables, Kansei words and semantic space) canbe combined to predict product preference.

This paper employed a study of automobile wheel rim de-signs to test the hypotheses and accomplish the goals. Dif-ferent methods were used to gather subjects visual judgmentof the wheel rims. Kansei words were used to capture sub-jects’emotions towards the wheel rims. Semantic space was usedto gather meanings associated with the wheel rim designs. Themethodology used in this paper is not limited to developing a re-lationship between the wheel rim form (discretize by design vari-ables) and the subjects judgments as in previous research studieswith other products [4, 10–12]. Rather, explain and justify theform-judgment relationship, a novel procedure to discretize thewheel rim form in terms of Gestalt variables is presented. Thisnew representation of the wheel rim form is more consistent withthe manner that humans process visual images.

2 BACKGROUNDThis section introduces concepts and serves as a foundation

to build the study that is presented in this paper.

2.1 Gestalt PrinciplesWhen our visual system gathers information through the

eyes, the brain processes the information and interprets it. Thisinterpretation, visual perception, is a psychological manifesta-tion of visual information. Visual perception is formally definedas “the process of acquiring knowledge about environmental ob-jects and events by extracting [information] from their emitted orreflected light” [13]. The information gathered through our eyescould be interpreted in many ways but we only perceive it oneway at any given time. A simple example is given by Edgar Ru-bin with the optical illusion known as the Rubin vase in whichyou either see a vase or two faces looking at each other. Gestalt


principles help to dictate which interpretation out of many is per-ceived visually at a given time.

Gestalt is a German word that means form. Gestalt princi-ples are the factors that govern how we perceive the whole form.As Kurt Koffka explains, “It has been said: The whole is morethan the sum of its parts. It is more correct to say that the whole issomething else than the sum of its parts, because summing up isa meaningless procedure, whereas the whole-part relationship ismeaningful” [14]. Gestalt principles consider the basic elementsthat compose an image. Then, they consider the relationshipsbetween those basic elements to group them. The relationshipsused in this study are: proximity, closure, and continuation (seeFig. 1). The proximity principle states that elements of an image(e.g. lines or dots) that are closer to each other will be perceivedas being together, as a group. The closure principle states thata set of elements can be perceived as a close figure despite thepresence of gaps such as the triangle in Fig. 1. The continuationprinciple states that when there is a smooth change from one ele-ment to another these elements will be grouped together. Specifi-cally, these three principles will be applied in section 3.2.3 to thecase study product to define Gestalt variables that discretized thewheel rim form.

FIGURE 1: Gestalt Principles examples: a.) and b.) Proximity,c.) Closure, d.) Continuation

When the concept was introduced, Gestalt principles wereonly demonstrated one at a time. Also, the principles work betterwhen everything else is equal, that is there is only one relation-ship between the basic elements of an image. The visual sys-

tem seems to be much more sensitive to certain kinds of differ-ences than others [13]. For example, the same elements can showgrouping regarding orientation at one angle, but might show lessgrouping at another angle. When more than one principle is pre-sented, a process framework could extract the principles that or-ganize the visual perception of the image [15]. The wheel rimdesigns presented in this study can show more than one Gestaltprinciple in a single image.

2.2 Semantic SpaceCharles E. Osgood, a social political psychologist, devel-

oped the semantic differential method in the 1950s. This methodmeasures the connotative meaning of concepts [16]. Some ofthe concepts studied by Osgood were general terms like Rus-sians, patriots, and America. In a study to sample the semanticspace, Osgood presented 20 different concepts to 100 subjects;each subject rated each concept on 50 different scales [17]. Therating scales used the Likert scale with pair of adjectives that areantonyms on each end point. A factor analysis summarized themeaning of concepts into three semantic dimensions: evaluation,potency and activity.

The three semantic dimensions are orthogonal and define thesemantic space. The evaluation dimension contains word pairslike beautiful-ugly, good-bad, clean-dirty; the potency dimensioncontains word pairs like strong-weak, large-small, heavy-light,and the activity dimension contains word pairs like fast-slow,active-passive and hot-cold. The semantic space is a three di-mensional space where the meaning of a concept can be locatedin space. This study replaces concepts with different product de-signs to capture the connotative meaning of designs.

The semantic differential and the semantic space were de-veloped as tools to measure semantic meaning. When first de-veloped, the tool was criticized as a measurement of meaningfrom a linguistic perspective but psychologists consider it to bea useful tool [18]. The semantic differential and semantic spaceare used now in studies from other disciplines [7,19]. In the cur-rent study, the semantic space is used to measure the extractedmeaning from the form of the wheel rim and to represent it in thethree semantic dimensions.

2.3 Kansei EngineeringThe following example illustrates Kansei Engineering: You

are driving a convertible sports car down a curvy road: the windon your face, the short shift leather knob in your hand shifting asyou are going into the turn; the grip of the tires, as the car hitsthe apex, the acceleration that gives you tunnel vision, the soundof the tuned exhausts and the vibrations as the engine revs upaccelerating out of the turn all take the driver and passenger into astate of excitement. These emotions are the Kansei of most sportscars. When designers and users see, hear, touch, taste and smellan artifact, they go through an assessment process to build their


judgment of that artifact. Parts of the judgment of the artifactare feelings and feelings build up to emotions. These subjectiveemotional responses to an artifact are what Kansei Engineeringconnects with engineering features.

The word Kansei is a Japanese term that is translated byNagamachi as“the subjective impressions of an artifact that aregathered through the senses” [20]. There are other definitions inliterature, all trying to explain this same concept but there is nodirect translation from Japanese to English [21, 22].

Kansei Engineering is commonly translated to English asemotional or affective engineering. Kansei engineering is amethod that translates the users’feelings into design specifica-tions [19]. K. Yamamoto first used the term Kansei Engineeringin 1986 [23]. Mitsuo Nagamachi developed the Kansei Engi-neering method in the 1970s at the Hiroshima International Uni-versity. This method is preceded by other methods that share theidea of gathering the user needs or emotional impact of an arti-fact. One of these is the Semantic Differential method introducedin the previous section. Another method that followed was theQuality Function Development method created by Misuno andAkao in the 1960s. After Kansei Engineering was introduced,the Kano model was developed in the 1980s. One of the key fea-tures of Kansei Engineering that differs from these other methodsis its goal of translating the emotional feedback that subjects re-port into changes in the design variables of the artifacts. This wasused in this study as a starting point to identify the relationshipbetween design variables and product judgment.

The Kansei Engineering methodology can be summarized insix steps. The method starts with a collection of what is calledKansei words, which are words that customers and sellers use todescribe the emotions perceived by the product. This collectionof words should represent the description of the product, but inorder to innovate new words can be added. To reduce the list, thewords can be filtered by different methods like pre-surveys andaffinity diagrams. Once the list is reduced to a quantity subjectscan evaluate, the semantic differential method is used to buildthe scales. Schutte has noted that the number of Kansei wordssubjects can evaluate changes with culture [21]. Once the wordsare selected, a sample of products from the market is collected.These products are categorized according to the features that thedesigner deems important, and the products are chosen such thatthere is a variance in the features of interest. In the current studyinstead of using a market sample CAD models were generatedto systematically represent different design variations. Then, anevaluation experiment is conducted where each product is ratedagainst each scale. The experiment is administered to a desirednumber of subjects. The results are interpreted performing a mul-tivariate statistical analysis and using other statistical tools. Theprocess ends with models that explain the Kansei of a product todesigners. The importance of this method in the current researchis that it determines how design variables influence the subjectivejudgment and preference for a product.

3 METHODOLOGYThe concepts presented in the background section are ap-

plied to a case study. This section describes the case study par-ticipants, the design of the study, including the procedures andtasks carried out by the participants.

3.1 ParticipantsA total of 64 subjects from the University of Notre Dame

participated in the study (35 males; 29 females). All subjects hadnormal or corrected-to-normal vision. They completed a writtenconsent agreement and were briefed on the experimental proto-col before starting. They were compensated with either $10.00or course credit. The subjects were classified in two groups (ac-cording to their design expertise) as either students with designexperiences or students with other majors. Senior students fromthe mechanical engineering and industrial design programs wereconsidered students with design experience. Also, graduate stu-dents holding a degree in those disciplines were so classified.The remaining subjects were students of other majors with di-verse academic backgrounds. The design students were recruitedfrom a senior level design methods course through email. Thestudents of other majors were recruited from the Department ofPsychology subject pool . The subject groups were composed of32 design students (25 males; 6 females) and 32 students fromother majors (10 males; 22 females). The distinction betweensubjects was made because of previous research showing that thejudgment towards the same product varies with expertise [7].

3.2 Design of Case StudyThis section describes the development of the case study

and includes a description of the different wheel rim designs thatwere generated and how the judgment dimensions were chosen.Some of the judgment dimensions are taken from Osgood’s se-mantic space and the remainder were Kansei words. The generaldimension, “like”, was included as a measure of product prefer-ence.

3.2.1 Product Selection An automobile wheel rimwas chosen as the case study product because it is geometricallysimple, easy to represent visually with few design variables andhas emergent Gestalt variables; at the same time it plays an im-portant role in the appearance and function of an automobile.The wheel rims are associated with an emotional reaction, as re-flected in comments on blogs and web pages. In addition, theycan be rated with respect to the three principal meaning dimen-sions for semantic space. Another reason to use wheel rims inthe case study is because it is a very common way to change theappearance of a vehicle.


3.2.2 Design Variables The product under study wascharacterized using engineering design variables. These vari-ables correspond to features manipulated in the product design.A parametric CAD model was used to render the images ofwheels rims with a normal tire and a white background. The im-ages were a front view of the wheel rim, thus the design variablesselected were the ones that changed the visual appearance of therim from this view. The design variables selected to parameter-ize the CAD model were: number of spokes (Spokes), number ofbolts (Bolts), spoke width angle (Width), and spoke blend radius(Radius), that is the radius where the spoke meets the outer andinner rim (see Fig. 2). The blend radius to the outer rim is dou-ble the blend radius of the inner rim. Each design variable wasselected at two levels: low and high. See Tab. 1 for each vari-able level values. In practice a designer would select values foreach of the design variables in order to define a specific wheelrim design.

FIGURE 2: Design Variables: a.) spoke width angle, b.) spokeblend radius, c.) number of Bolts, d.) number of Spokes)

TABLE 1. WHEEL RIM DESIGN VARIABLESDesign Variable Low Value High Value

Bolts 4 8

Radius 0.7 in. (17.8 mm) 1.5 in. (38.1 mm)

Spokes 4 10

Width 10� 16�

With these four design variables, each at two levels, a full

factorial DOE generates a total of 16 different wheel rims de-signs, shown in Tab. 2. (The reader is encouraged to look at the16 designs and identify the one that he or she likes most to latercompare with the results of the study.) In order to show eachsubject a complete set while limiting subject fatigue, the studyonly used these four design variables at two levels. The CADmodel was used to generate virtual renderings of the 16 wheelrims designs with a normal tire and a white background.

3.2.3 Definition of Gestalt Variables Gestalt vari-ables were defined to describe the wheel rims designs in terms ofGestalt Principles. The first Gestalt Principle to be applied wasproximity. A general definition of the proximity principle thatcan be applied to most objects is that elements of the objects thatare close to each other may be perceived as being grouped to-gether. This general definition can be applied to the wheel rim todevelop various quantitative measurements of proximity, accord-ingly three Gestalt variables are defined based on this principle.Two of them refer to the angular proximity between spokes andbolts, the other to the angular proximity between a spoke and theadjacent spokes.

The coding defined for these variables is as follows:

Proximity Bolts to Spokes = (number of bolts closely alignedwith spokes) / (number of bolts)Proximity Spokes to Bolts = (number of spokes closelyaligned with bolts) / (number of spokes)Proximity Spokes to Spokes =1 / (number of spokes)

The Gestalt Principle of closure was used to develop an ad-ditional Gestalt variable. There are two wheel rim designs wherethe spoke radii blends are tangent with the adjacent spoke radiiblends forming a closed figure. The contrast between a wheeldesign with and without closure can be seen in Fig. 3 where thedesign on the right shows closure and the one on the left does notshow the effect. Without closure the radius blends are not tangentand it is hypothesized that the subjects fixate more on the spokesthan the rim. The designs for this variable, closure, were coded1 for the designs 8 and 16, and coded 0 for all other designs.

The last Gestalt variable was developed from the continu-ation principle. An initial evaluation of the survey data helpedidentify this variable. It was noted that designs with the Widthset at the low level, and the Radius and Spokes set at the highlevel, behaved effectively as the designs with the Width set atthe high level. One Gestalt principle that can explain this phe-nomena is the continuation principle. Specifically the larger ra-dius blends and the number of spokes increase the continuation(smoother changes between sections) in the front surface of thewheel. Considering these 2 factors and adding the actual widthof the spokes, the continuation Gestalt variable was defined as:

Perceived Width of Spokes = Width ⇥ Spokes ⇥ Radius


Closure = 0 Closure = 1

FIGURE 3: CLOSURE PRINCIPLE EXAMPLE

3.2.4 Definition of the Semantic Space To be ableto locate each design in semantic space, one word was selectedfrom each semantic space dimension; see section 2.2 for exam-ples. From the evaluation component “Beautiful” was selected,for the activity component “Fast” was selected, and for the po-tency component “Strong” was selected. It was a subjective de-cision that these three terms were the ones that applied most di-rectly to wheel rims. In an industry setting the designer wouldhave to choose the most applicable word for each dimension.

3.2.5 Selection of Kansei Words In order to capturethe attributes that subjects attach to the wheel rims designs, Kan-sei methodology is used, specifically the use of Kansei words. Tofind the Kansei words for a rim, descriptions of the product werecollected from web pages and blogs where customers and usersdiscussed this product. Also, point of sale verbatim from webpages that indicated how the seller approached the consumer andsold the product were collected. In absence of this informationother sources of information could be used such as customer andexpert interviews, store intercepts and observation. All the words(2,201) were analyzed for frequency of occurrence. The wordsthat had higher frequency and were used to describe the wheelrims were selected. Eight words were selected to be included inthe study to avoid subject fatigue. The Kansei words included inthe judgment dimensions were: Aggressive, Performance, Qual-ity, Racing, New, Clean, American, and European.

3.2.6 Product Preference Scale Product preferencewas established by asking: “Do you like this wheel?” This ques-tion was answered on a 7-point Likert scale, where 1 was “not atall” and 7 was “very”. The other judgment dimensions discussedabove (Kansei words and semantic space) where also rated onthe 7-point Likert scale. To maintain consistency among Kanseiwords and semantic space only the positive word of the semanticdifferential pair was used.

3.3 Case Study ProcedureThe study began after the subjects completed a consent

agreement, and were briefed on the experimental protocol. Thesubjects started the experiment by entering their major field ofstudy and a subject number. Then, they were presented with on-screen instructions followed by one example of the task to becompleted. When they completed the task (responded to all thequestions) for the same wheel rim design they moved on to com-plete the same task for the remaining wheel rim designs. The or-der in which the wheel rim designs were presented to the subjectwas randomly selected within subjects. Each subject evaluatedall 16 wheel rim designs.

The study equipment used was a PC computer, a Qwertykeyboard, mouse and a monitor (18 inches (45.7 cm) measureddiagonally and set to a resolution of 1280 x 960 pixels). Thestudy procedure was administered using E-Prime 2.0 software(Psychology Software Tools, Pittsburgh, PA).

In the study task subjects were shown an image of one of thewheel rim designs with a tire. Below the image was a sentencedescribing a specific judgment dimension (e.g. On the scalebelow rate your perception of how STRONG is the wheel). Atthe bottom of the screen was the 7-point Likert scale, as shownin Fig. 4. The judgment dimension were composed of Kanseiwords, semantic space terms, and the general judgment dimen-sion that asked how much the subject “like” the design. The judg-ment dimensions were displayed in random order within sub-jects, with the exception of the “like” dimension that was alwaysasked last. The subjects provided their responses using the key-board, and these responses were recorded in the computer. Thesoftware package SPSS 19 was used to build the regression mod-els presented in the Results section.

FIGURE 4: EXAMPLE OF STUDY TASK.


4 RESULTS AND DISCUSSIONSThe key results of the study are presented and discussed in

this section. First, the results indicate that changes in the formor shape of the wheel rims change the subjects design preferenceand judgment. Various forms for the parametric models used tocharacterize the subjects judgment for the wheel rims are pre-sented. Initially, these models were based upon design variablesalone but when the model form was based upon Gestalt vari-ables, the models produced better predictions of product prefer-ence. Additional improvements to the prediction model were re-alized when Kansei words and semantic space were added to theGestalt variables. Lastly, information from subject expertise wasadded to the model; however, this knowledge did not improve thepreference prediction model.

4.1 Wheel Rim Form Variation and JudgmentA linear regression using the design variables to predict the

“like” scale resulted in a good fit and statistically significantmodel (R2 = 0.931,P < 0.05). A linear regression was per-formed for the rest of the judgment dimensions using the designvariables as predictors. An example of the functional form of theequation used in the regression is shown below, b are the coeffi-cients that the regression specifies. This information is summa-rized in Tab. 3. This table illustrates and confirms that variationsin the form of the wheel rim caused by design variables influ-enced the product preference and all other judgment dimensions.

JudgementDimension = b1 + b2(Spokes) + b3(Bolts)

+ b4(Width) + b5(Radius)

Table 3 shows that for the judgment dimension Like all de-sign variables play a significant role except Bolts. However forother judgment dimension like Clean then Bolts is the only sig-nificant design variable. Also the Table shows that more complexjudgment dimensions such as European can not be effectivelymodel by design variables. For most of the judgment dimensionsSpokes is significant. In ten out of twelve judgment dimensionsit is the design variable that had the most effect in all judgmentdimensions. In each of these dimensions more Spokes improvedjudgment of the subjects, the only exception was the Clean di-mension where less Spokes improved judgment of the subjects.

Overall the results indicate that design variables do have aninfluence on the product judgment and preference. This informa-tion is important for all disciplines involved in the design of theproduct, but in particular for engineering designers because theymight select values for some of the product design variables tomeet other objectives (e.g. function or weight) and be unaware oftheir consequences regarding product judgment and preference.

TABLE 3. JUDGEMENT DIMENSIONS PREDICTED BY DE-SIGN VARIABLES

Judgment Standarized CoefficientsR2

Dimension Bolts Radius Spokes Width

Like 0.058 0.147* 0.944* 0.123* 0.931

Fast 0.088 0.079 0.780* -0.191 0.660

Strong 0.074* 0.124* 0.937* 0.288* 0.983

Beautiful 0.066 0.184* 0.929* 0.029 0.903

Aggressive 0.201* 0.126 0.820* 0.189 0.765

Performance 0.098 0.125 0.914* 0.163* 0.888

Quality 0.114* 0.146* 0.933* 0.194* 0.943

Racing 0.087 0.089 0.807* 0.052 0.669

New 0.004 0.229 0.717* 0.021 0.567

Clean -0.537* -0.097 -0.076 0.220 0.352

American 0.055 0.043 0.885* 0.283* 0.869

European 0.062 0.317 0.156 -0.301 0.219

Significant Coefficients (P < 0.05)*

4.2 Wheel Rim Design PreferenceNow that it is known that the form of the wheel rim design

influences how it is judged, how these forms are preferred willbe discussed. The wheel rim design that subjects liked mostwas design 15, and the least liked was design 1. In Fig. 5 thedesigns are ordered by increasing average “like” ratings. ThisFigure also includes the results from three models that were de-veloped to predict “like” and are discussed in the next sections.Inspecting how the wheel rim designs increase in the “like rating,a discontinuity is apparent in the plot that separates the wheel rimdesigns in two groups. One group is composed of designs 1, 2,5, 6, 9, 10, 13, and 14; they have a lower average “like” ratingand all designs share the lower bound in the Spokes variable. Theother group is composed of designs 3, 4, 7, 8, 11, 12, 15, and 16;they have a higher average “like” rating and all designs share thehigher bound in the Spokes variable. This shows that the Spokesvariables has a strong effect in the “like” rating.

4.3 Linear Regression Models to Predict ProductPreference

The first model presented in Fig. 5 to predict “like” employsdesign variables alone and the second Gestalt variables alone.


1 9 5 13 2 10 14 6 3 4 11 12 16 8 7 152.5

3

3.5

4

4.5

5

Wheel Rim Designs

Like

Rat

ing

ReportedDesign Variables Model (Predicted)Gestalt Variables Model (Predicted)Gestalt + Semantic + Kansei Model (Predicted)

FIGURE 5: WHEEL RIM DESIGNS VS LIKE RATING.

Both models provided statistically significant results and goodfits to the reported data, with the Gestalt variables model moreclosely approximating the average subjects “like” data in nineout of sixteen wheel rim designs. Table. 4 presents the contrastbetween models; design variables alone do not perform as wellas Gestalt variables, therefore Gestalt variables were chosen todevelop an alternative prediction model. All models in Tab. 4are linear models where the variables specified are multiplied byregressed b coefficients.

The next step was to explore the effect of adding mean-ing and emotional information to the model. This was done byincluding the semantic space and Kansei words to the Gestaltvariables model. The semantic space adds meaning informationto the models and Kansei words add emotional information tothe models. First, a regression model was built in two steps:

the first step consisted in performing the linear regression modelwith Gestalt variables to predict product preference, the secondstep was a stepwise regression to add the semantic space. Thisstepwise regression chose the predictive variables to be includedin the model using a F-test with criterion of P 0.05 to enterand P � 0.10 to exit. This model included all Gestalt variables.The dimensions chosen by the stepwise regression from seman-tic space were Beautiful and Strong. The same procedure wasimplemented to build a model with Gestalt variables and Kan-sei words. This next model included all Gestalt variables. TheKansei words selected by the stepwise regression were Quality,European, Aggressive, and Performance. Table 4 also shows thatwhen semantic space was added to Gestalt variables the modelwas improved as indicated by increased correlation coefficients,the same trend is shown when adding Kansei words.


TABLE 4. PRODUCT PREFERENCE MODELS SUMMARY

Model (Like =) R2 F Sig. Std. Rˆ2 Sig.

Error D D

f (DV ) 0.931 91.1 0.000* 0.213 - -

f (GV ) 0.949 96.7 0.000* 0.187 - -

f (GV,SS+) 0.978 153.0 0.000* 0.127 0.029 0.013

f (GV,KW+) 0.987 182.8 0.000* 0.103 0.038 0.012

f (GV,KW+,SS+) 0.990 210.4 0.000* 0.091 0.041 0.015

Significant Models (P < 0.0005)*

Variables filtered by Stepwise regression+

D reference model is f (GV )

The last model constructed used Gestalt variables, seman-tic space and Kansei words. This model was built in three steps:the first step consisted of determining the linear regression modelwith Gestalt variables to predict product preference. The secondstep was a stepwise regression that used the same criterion usedpreviously to choose Kansei words; the third step also consistedof a stepwise regression with the same criterion previously usedbut with the semantic space. In addition to Gestalt variables thislast model included Kansei words Quality, European, Aggres-sive, and Performance, and from semantic space only Beautiful.This is the best model achieved in this study with a R2 of 0.990(P < 0.05,F = 210.401,n = 64). This implies that when tryingto understand how the form of a product influences the prod-uct preference, a designer can gather information from Gestaltvariables, and attach meaning and emotions that the shape mightinvoke for the users. In practice a designer will be most inter-ested in a model that predicts the most preferred design. Thislast model is the one that when compared to the rest of the mod-els presented better predicts the most preferred design (wheel rim15).

4.4 Expertise Effect on Product PreferenceThe difference in subject expertise was explored as another

source of information to help predict product preference. Anadditional variable of expertise was added to all the previouslypresented models. However, this expertise variable was not sig-nificant in any of the models. This might be because the studentsidentified as designers have little real experience in product de-sign particularly with regard to the wheel rim product. Subjectswith more experience and specific knowledge of the product un-der study could have demonstrated a difference between design-ers and users. Another factor that might attenuate a distinctionbetween the two groups is that users are more involved in the

design of today’s products with the ease of customization andpersonalization of products.

4.5 Implications For DesignersThis study focused on modeling product form preference us-

ing Gestalt principles, semantic space and Kansei words for aspecific product. The results indicate that designers could followthe methodology presented to model form preference of otherproducts. However the intent of this work was to establish a basefor future formal methods that incorporate Gestalt principles, se-mantic space and Kansei words to aid engineering designers todesign the form of a product taking into account these dimen-sions in conjunction with the functionality of the product. Thispaper establish two important points for the application of sucha method, first product form does influence the judgment of theproduct, and second, the use of appropriately selected Gestaltvariables could better predict the judgment of the form of theproduct.

In order to apply the methodology used in this study a de-signer would have to follow these steps. For the Gestalt variablesthe designer should apply the Gestalt Principles to the productform. A systematic method should be developed to apply GestaltPrinciples to a product form. In the Semantic Space the designercan choose the words that apply best to the product from eachsemantic space dimension. Words from the seller of the prod-uct and from the users that describe the product can be collectedfrom the sources mentioned, then filtered by frequency and thenby the higher frequency words used as the Kansei words. Finallya parametric CAD model is built to generate alternative designs.With these items a study can be built to gather the data to use tomodel the product form judgment. Then the model can informother designers involved in the design of the product. Note thatthe complexity of the models and number of subjects would mostlikely have to increase in order to apply the methodology in anindustry setting.

5 CONCLUSIONSThe results of this study showed that when a designer needs

to gauge product preference from the form or shape of a product,information from design variables, visual perception, meaningand Kansei are useful to predict product preference. Further-more, both design variables and Gestalt variables were able toindividually predict product preference. However, when compar-ing the models developed using either design variables or Gestaltvariables, the later one showed better fit. This indicates a poten-tial advantage of discretizing the product form using a methodsimilar to the way subjects mentally group the design variablesof the product form. Once these models are established for aspecific product they can be used in the early design stages of theproduct at the studio, but more important these models can be


used in the product development process by engineer designersthat are solving engineering problems that affect the final formof the product.

ACKNOWLEDGMENTAuthors would like to acknowledge the support of the Grad-

uate School of the University of Notre Dame Fernandez Fel-lowship, and the Department of Psychology at the University ofNotre Dame for access to their subject pool. Also the authorswould like to thank the anonymous reviewers for their insightsand comments.

REFERENCES[1] Sylcott, B., Tabibnia, G., and Cagan, J., 2011. “Under-

standing of emotions and reasoning during consumer trade-off between function and aesthetics in product design”. InProceedings of the ASME 2011 International Design Engi-neering Technical Conference & Computers and Informa-tion in Engineering Conference.

[2] Pugliese, M. J., and Cagan, J., 2002. “Capturing a rebel:modeling the Harley-Davidson brand through a motorcy-cle shape grammar”. Research in Engineering Design,13(April), pp. 139–156.

[3] Orsborn, S., Boatwright, P., and Cagan, J., 2007. “Identify-ing product shape relationships using principal componentanalysis”. Research in Engineering Design, 18(4), Oct.,pp. 163–180.

[4] Achiche, S., and Ahmed, S., 2008. “Mapping shape geom-etry and emotions using fuzzy logic”. ASME ConferenceProceedings, 2008(43284), Jan., pp. 387–395.

[5] Zuo, Z. H., Xie, Y. M., and Huang, X., 2011. “Reinventingthe wheel”. Journal of Mechanical Design, 133(2), Feb.,pp. 024502–4.

[6] Westmoreland, S., Ruocco, A., and Schmidt, L., 2011.“Analysis of capstone design reports: Visual representa-tions”. Journal of Mechanical Design, 133(5), May,pp. 051010–7.

[7] Hsu, S. H., Chuang, M. C., and Chang, C. C., 2000. “Asemantic differential study of designers’ and users’ prod-uct form perception”. International Journal of IndustrialErgonomics, 25(4), pp. 375 – 391.

[8] Linsey, J. S., Clauss, E. F., Kurtoglu, T., Murphy, J. T.,Wood, K. L., and Markman, A. B., 2011. “An experimen-tal study of group idea generation techniques: Understand-ing the roles of idea representation and viewing methods”.Journal of Mechanical Design, 133(3), Mar., pp. 031008–15.

[9] Hsiao, S., and Wang, H., 1998. “Applying the semantictransformation method to product form design”. DesignStudies, 19(3), July, pp. 309–330.

[10] Orsborn, S., Cagan, J., and Boatwright, P., 2009. “Quanti-fying aesthetic form preference in a utility function”. Jour-nal of Mechanical Design, 131(6), June, pp. 061001–10.

[11] Kelly, J. C., Maheut, P., Petiot, J., and Papalambros, P. Y.,2010. “Incorporating user shape preference in engineer-ing design optimisation”. Journal of Engineering Design,22(9), pp. 627–650.

[12] Reid, T. N., Gonzalez, R. D., and Papalambros, P. Y., 2010.“Quantification of perceived environmental friendliness forvehicle silhouette design”. Journal of Mechanical Design,132(10), Oct., pp. 101010–12.

[13] Palmer, S. E., 1999. Vision Science: Photons to Phe-nomenology. MIT Press.

[14] Koffka, K., 1935. Principles of Gestalt Psychology,. Har-court Brace and Co.

[15] Marr, D., 2010. Vision: A Computational Investigation intothe Human Representation and Processing of Visual Infor-mation. The MIT Press.

[16] Osgood, C. E., Suci, G. J., and Tannenbaum, P. H., 1957.The Measurement of Meaning. University of Illinois Press.

[17] Osgood, C. E., and Suci, G. J., 1955. “Factor analysis ofmeaning”. Journal of Experimental Psychology, 50(5),pp. 325–338.

[18] Carroll, J. B., 1969. “Review of The Measurement ofMeaning”. In Semantic Differential Technique: A Source-book, J. G. Snider and C. E. Osgood, eds. Aldine, Chicago,Illinois, pp. 96–115.

[19] Nagamachi, M., 2010. Kansei/Affective Engineering. CRCPress.

[20] Nagamachi, M., 2001. “Workshop 2 on Kansei Engineer-ing”. In Proceedings of International Conference on Affec-tive Human Factors Design.

[21] Schutte, S., 2005. “Engineering Emotional Values inProduct Design”. PhD Thesis, Linkopings Universitet,Linkoping, Sweden.

[22] Levy, P., 2005. “Interdisciplinary design for the cyberspaceby an approach in kansei information - methodology andworkgroup communication tool design”. PhD thesis, Uni-versity of Tsukuba, Japan.

[23] Yamamoto, K., 1992. “Japans Automotive Industry: It’sStrength”. In Special Lectures by Top Management, Facultyof Business Administration.


TABLE 2. WHEEL RIM DESIGNSSpokes L Spokes H

Width L Width H Width L Width H

Bol

tsL

Rad

ius

L

Design 1, Like avg: 2.72 Design 2, Like avg: 3.11 Design 3, Like avg: 4.08 Design 4, Like avg: 4.38

Rad

ius

H


Bol

tsH

Rad

ius

L


Rad

ius

H



Modeling Product Form Preference Using Gestalt Principles Semantic Space and Kansei

Documents

Transcript of Modeling Product Form Preference Using Gestalt Principles Semantic Space and Kansei