J Intell ManufDOI 10.1007/s10845-012-0699-5

Perceived feature utility-based product family design: a mobilephone case study

Gül E. Okudan · Ming-Chuan Chiu · Tae-Hyun Kim

Received: 31 October 2011 / Accepted: 13 September 2012© Springer Science+Business Media New York 2012

Abstract To assure profit maximization through masscustomization and personalization, effectively eliciting con-sumer needs across different market segments is critical.Although functional performance specifications and ade-quacy of various design forms can be measured directlyand objectively, many designers and engineers struggle withclearly evaluating product criteria requiring subjective con-sumer input; the fact that these inputs change over timefurther complicates the process. To appropriately evaluateproduct criteria, an effective design decision-making anal-ysis is required. In this study, we propose a methodologyto assure effective elicitation of needs and their inclusionin design decision making and illustrate it using a mobilephone product family design scenario. First, consumerperceived utility of design features is gathered using a ques-tionnaire (500+ responses) and then modeled using multi-attribute utility theory to facilitate the evaluation of aproduct family while responding to needs across customerclusters shaped by demographics. The methodology goal isto determine the relative goodness of a product family in com-parison to its competition. We also compare and evaluate theapplication of the proposed method to conjoint analysis.

G. E. OkudanSchool of Engineering Design, The Pennsylvania State University,University Park,PA 16802, USA

G. E. Okudan · T. H. KimDepartment of Industrial and Manufacturing Engineering,The Pennsylvania State University, University Park,PA 16802, USAe-mail: gek3@engr.psu.edu

M. C. Chiu (B)Department of Industrial Engineering and Engineering Management,National Tsing Hua University, HsinChu 30001, Taiwan, ROCe-mail: mcchiu@ie.nthu.edu.tw

Keywords Mass customization and personalization ·Design decision making · Multi-attribute utility theory ·Mobile phones · Conjoint analysis (CA)

Introduction

Consumer satisfaction increases when the perceived good-ness or utility of a product improves as the result of deci-sions pertaining to functional concepts. Thus, manufacturerstypically make investments to upgrade their products. Forthe most part, however, these development practices—espe-cially in the production of mobile phones—remain heavilyfocused on increasing the number function options regard-less of actual consumer wants or needs. These design andmanufacturing decisions tend to increase consumer confu-sion and frustration. A recent survey indicated that 61 % ofmobile phone owners believe there are too many unnecessaryfeatures on their phones (PCWorld 2009).

Many people use products for their primary applicationonly. For example, they go no further than using their mobilephones for making phone calls and their mp3 players for lis-tening to music. For them, many built-in functions are rarelyor never used. Studies have highlighted the fact that over-loaded mobile phone functionalities create unnecessary com-plexity for most users (e.g., Ling et al. 2007; Renaud and vanBiljon 2010). To improve customer satisfaction, manufac-turers could effectively investigate consumer interest usingevaluation methods to screen out unimportant features, formsand functions. Simultaneously, consumer diversity could betaken into account to achieve an effective mass customiza-tion.

Consumer product preferences differ by age, race, gender,and cultural background. Some gender studies have foundpreferences for gender-specific colors: blue for males and

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pink for females (Andree et al. 1990). Color assignmentstudies have found preferences differ significantly acrossage groups; for example, the preference for blue decreasessteadily, whereas the popularity of green and red increasesas age advances (Dittmar 2001). Age differences can alsoimpact shape preference; for instance older phone users whohave presbyopia often prefer larger keypads while youngerones often prefer easy-to-carry slim phones. Accordingly,manufacturers must customize products for certain popu-lations—a practice cultivating the development of productfamilies.

This work presents an approach for choosing product fam-ilies intended to satisfy customer needs accurately, basedon both product features and the demographics of potentialconsumers. Section “Literature review” provides a summaryof the related literature. In Section “Proposed methodol-ogy”, we propose a two-stage methodology for capturingconsumer preferences and modeling utility to guide productfamily development. We present our case study and discussresults in section “Case study”. In Section “Comparison ofthe presented method to conjoint analysis”, we compare ourproposed method to conjoint analysis (CA). Section “Dis-cussion and conclusion” concludes the study and identifiesfuture work directions.

Literature review

Research within manufacturing companies must investigateappropriate product design features pertaining to both formand function in order to support their vision and improvetheir market share. In the engineering or product designdomain, conceptual design methods such as Akao’s (1997)quality function deployment (QFD), Wang’s (2002) fuzzysets, Saaty’s (1980) analytic hierarchy process (AHP) andPugh’s (1991) method have been used to screen out unim-portant features or to evaluate priorities to satisfy consumerrequirements and desires.

Using QFD (Akao 1997), designers gather informationabout consumer requirements in terms of importance rankingto create direct associations with the functional requirements(Pullman et al. 2002). QFD analysis gives designers sufficientdirection about which components require improvement toaccommodate consumer demand. However, this methodol-ogy neither provides an aggregate utility score for potentialdesign concepts, nor easily incorporates market segmentsinto a perceived utility analysis.

Analytic hierarchy process (Saaty’s 1980) involves pair-wise analysis, comparing potential concepts in order to reacha weighted order of preference. Using a standard 1–9 scale,a value of 1 indicates equal utility (importance) in terms ofpreference whereas a 9 indicates the extreme utility of a con-cept in comparison to another. When designers are faced

with complex decision making, AHP provides a suitable toolfor systematically evaluating the relative importance of con-sumer requirements and the relative goodness of possibleconcepts. AHP assumes all criteria to be mutually prefer-entially independent, and it aggregates the relative priori-ties across customer requirements in a linear form. However,such an assumption is rather restrictive, only representingrare practical cases.

Isiklar and Buyukozkan (2007) proposed a multi-cri-teria decision making (MCDM) method, which combinesAHP and TOPSIS (Technique for Preference by Simi-larity to the Ideal Solution) (Hwang et al. 1981). Theirmethod uses AHP to identify weights across multiple cri-teria in order to identify features based on their rele-vance to consumers’ preferences. TOPSIS, originally pro-posed by Hwang and Yoon as a way to identify the bestalternative with the shortest distance to the ideal solu-tion, is then used to identify the best option from theAHP findings. Isiklar and Buyukozkan’s study examin-ing the mobile phone industry showed that their MCDMmethod helps select a better potential solution by compar-ing choices across multiple criteria. However, this approachmay not completely take into consideration the relativeimportance of certain criteria if the decision makers donot have the up-to-date consumer preferences about specificproduct functions. Additionally, AHP limitations can affectanalysis.

In an effort to solve the concept selection problem mostappropriately, Wang et al. (2006) proposed a multi-objectiveoptimization algorithm to determine the optimal selectionfor multi-objective and multi-constraint problems. However,complex computation often makes this approach prohibitive,specifically with increasing numbers of variables.

In Pugh’s concept screening method (1991), the perceivedutility of design concepts is evaluated in a pairwise fashionusing a scale (better, 1; equal, 0; inferior, −1), coarser thanthat used in AHP. In general, Pugh’s method is practical andcommonly used. However, if team members opine differentlywith regard to consumer requirements or design criteria, timemay be wasted reaching a general agreement. Furthermore,if a design team member has less than adequate knowledgeabout the design challenge at hand, the probability of select-ing the right concept will decrease (Pugh 1991). However, themethod helps to choose quickly the best conceptual designwhen each designer independently selects criteria for com-parison.

When users are faced with linguistic assessments of util-ity such as “slightly better” and “much better,” the fuzzy setmethod (Wang 2002) can be used to help determine the mostappropriate design concept. Using this approach, ambiguouslinguistic terms are represented by arithmetic operations forevaluating design functions (e.g., Liu et al. 2012). QFD, AHP,and Pugh’s concept screening method can also be modeled

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using linguistic assessments that accommodate fuzzy sets(e.g., fuzzy AHP applications in product design by Lee et al.2001).

In marketing research, conjoint analysis (CA) evolvedduring the 1970s. Cattin and Wittink (1981) reported thatmore than thousand CA applications had been conductedin the first decade after its introduction. CA measures howconsumer preferences and perceptions change and is fre-quently used to analyze the relative importance each productfeature has on purchasing decisions (Barone et al. 2007).It enables a design team to easily and effectively assem-ble the most highly valued combination of features beforelaunching a new product. As described by Green et al.(1981), the CA process first requires data collection to esti-mate each respondent’s parameters for the utility function;then subject background variables are related to utility func-tions to identify potential market segments; finally, a set ofdesign configurations is evaluated using choice simulators todetermine market share information. Although CA may beused to classify consumer needs for market research (Pull-man et al. 2002) or to analyze consumer preference factorsin the product design decision-making process (Du et al.2006), it does not always provide valid results when test-ing new products because respondents tend to overestimatetheir preferences toward less important factors (Barone et al.2007).

Kansei Engineering (KE) focuses on consumers’ emo-tional responses to product forms and functions. It fosters anunderstanding of consumer preferences by evaluating theirpsychological interactions with product features and appear-ances (Nagamachi 1995). Within this framework, analysisof design elements helps understand latent consumer prefer-ences and can potentially predict consumption in unknowncultural environments (Veryzer 1993). KE starts by seman-tically capturing consumer feeling (Kansei) about a productand progresses to associating potential design characteristicsthat correspond to the captured Kansei. The final step focuseson adjusting design configurations to elicit the intendedKansei from the consumers. Recently, KE has been usedin unison with latent semantic analysis (Smith et al. 2012).However, KE focuses primarily on a product’s ergonomicfeatures (Nagamachi 1995); thus, incorporating quanti-tative or technical features increases its analytic chal-lenges.

The existing concept selection and marketing researchmethods may neglect the interdependence among criteria orthe uncertainty in customer perceptions. In many, consumerperception is incorporated while developing new products;yet, consumers have been shown to overestimate or under-estimate their preferences (Barone et al. 2007). To respondto these issues, we propose a method that not only makesuse of market information to elicit the important productcharacteristics using historical data mining but also tackles

interdependence and uncertainty issues through the imple-mentation of the Multiple Attribute Utility Theory (MAUT)while mapping product variants to market segments. Wedemonstrate the method using a mobile phone product familyto analyze the appropriateness of product family members toa particular market segment.

Product family refers to developing a set of products thatshares common components yet exhibits adequate differen-tiation intended to target different market niches (Jiao andTseng 1999; Simpson 2004). The ultimate goal of designinga product family is to provide a variety of products to themarket with an effectiveness close to that of mass production(Jiao et al. 2007).

Proposed methodology

Our proposed method has two complementary stages. Itextends and aggregates our prior work (Kim and Okudan2009a,b; Kim et al. 2010). The overall implementation flowof the method is provided in Fig. 1.

Stage 1: Initial matching of consumer preferencesand product features

Historical data mining

Historical data mining aims to identify the most significantdesign features that affect market shares of leading com-panies (Kim and Okudan 2009a,b). The authors conducteda multiple regression analysis using actual market data todetermine whether form or function features had a greaterimpact on market shares as a way to guide limited resourceallocation. This analysis classified of all mobile phone designfeatures as either form or function according to patent anal-ysis. If a feature had a design patent, it was deemed a formfeature; if it had a utility patent, it was classified as a functionfeature. In this paper, we use multiple regression analysis toidentify the significant design features influencing productpurchase, based on historical market data.

This research analyzed the product features of 1028mobile phones released between 2003 and 2008. Unimpor-tant variables were eliminated using the Variance InflationFactor (VIF) method to quantify the severity of multicollin-earity in an ordinary least squares (OLS) regression anal-ysis. For the remaining variables, regression models wereformed and the best fitting model was determined usingMallow’s Cp method, which is dependent on the numberof variables and the sample size as well as the error terms.Finally, weights of important design variables were deter-mined through partial regression coefficients. Further back-ground on VIF and Mallow’s Cp are provided in Kim andOkudan (2009a,b).

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Fig. 1 Implementation flow ofthe proposed method Determine /Develop

Possible Matching Concepts for

Consumer Clusters

Assess Utility of Concepts for

Consumer Clusters

Historical Data Mining

Logistic Regression

(Filtering toIdentify Features)

Confirm Features Using Surveys

Stage 1

Initial matching of consumers’ preference of product features (form & function)

Stage 2Multi-attribute utility function

based assessment

Confirm Concept Model Fit to Customer Cluster through Stages 1 & 2

Logistic regression filtering to identify salientcharacteristics

For many products, it is difficult to find sales or mar-ket share data across companies that are associated withspecific consumer characteristics. Using logistic regression,analysts can identify which features are preferred by variousconsumer groups to better customize a given product. Kimet al. (2010) applied logistic regression to associate pref-erences with consumer age, origin (i.e., Asian, American)and gender; the method can also be applied to other fac-tors (e.g., geographic locations or income levels). Filteringthrough logistic regression provides odds ratios on how con-sumer preference responses differ from reference variables.The results are then evaluated for statistical significance bycomparing the response probabilities. Using the regressionmodels, estimation is made of the relationship between oneor more predictors, and consumer preferences based on age,origin, and gender are identified.

Stage 2: Utility function based confirmation

In stage 1, where the significant features impacting marketshares and variation across gender, age and origin clustersare identified, the results do not provide an aggregate utilityof a design. Here, utility is defined as a measure of satisfac-tion consumers receive from a product or service (Cleaver2011). In stage 2, we develop MAUT models to rank orderthe consumer preferences for mobile phone models using thepre-selected significant mobile phone features determinedduring stage 1. Using MAUT and accounting for uncertainty,we evaluate the appropriateness of the mobile phone modelsfor all consumer groups in the aggregate (i.e., across all fea-tures in the product). MAUT eliminates the limitations found

in prior applications, such as additive linear modeling (AHP),accounting for all features rather than single ones (KE), andthe needed choice simulation (CA). Although we assess theutility of actual models for our case study, conceptual modelscan also be used.

Case study

To demonstrate the methodology, this study used our previ-ously gathered data for 1028 mobile phone models (Kim et al.2010; Kim and Okudan 2009a,b). Feature evaluation andmultiple regression analysis, using market share as the depen-dent variable, led to the identification of potential regressionmodels (see Table 1). From these, a selection was made usingMallow’s Cp (lowest Cp value).

A survey was developed and administered to determineconsumer preferences associated with mobile phone charac-teristics. Survey questions, designed according to the histor-ical data mining, are provided in Kim (2009). All featuresfound to be significant were included as survey questions.

In total, 527 participants (274 U.S citizens, 253 non-U.Scitizens of Asian origin) took part in the survey (Kim andOkudan 2009b). Their ages ranged from 14 to 39. Resultswere categorized in four age groups: 14–17, 18–22, 23–29,and 30–39. The U.S. citizens included 142 males and 132females. Of the non-U.S citizens, 125 were males and 128were females, primarily Asians living in the U.S. and pre-dominantly Korean. The participants spanned age, gender,education level, and ethnicity categories.

Eleven questions were developed to identify user prefer-ences for mobile phone factors, design characteristics, andfrequency of function use. Based on the results, consumerpreferences were analyzed using logistic regression. Then

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based on stage 1 activities (see Sect. “Historical data mining”and “Logistic regression filtering to identify salient charac-teristics”), we determined the important design features forconsumers. Using this data, we identified salient consumerpreferences in order to match the most appropriate mobilephone models for each of 16 different groups formed in amatrix of four age, two gender, and two origin categories.Analysis showed nine attributes, namely, number of contacts(k1), weight (k2), screen size (k3), camera (k4), battery life(k5), memory (k6), transfer speed (k7), keypad (k8), andform (k9), were found to impact consumer preferences sig-nificantly and in different ways across the groups.

In Sect. “Case study”, we demonstrate how the perceivedfeature utility-based product family design can be used intwo ways: (1) studying market offerings to arrive at potentialmodels for a company to benchmark and further develop as aproduct family, and (2) studying a company’s own productsto better target specific consumer groups.

Utility assessment of models in a market as a foundationfor a product family

After examining the market offerings of summer 2009, weidentified past and present mobile phone models satisfyingthe consumer preferences for each consumer group. Modelsmatching the U.S. sample are provided in Fig. 2, and thosefor the Asian sample are provided in Fig. 3.

Nine mobile phone attributes (determined in stage 1 as theevaluative attribute set for fulfilling customer preferences)were used for utility assessment. In this stage, single attributeutility (SAU) functions to reflect consumer’s priorities usingthe certainty equivalent concept were formulated accordingto Keeney and Raiffa (1976). While the boundaries for attri-bute utility functions are ultimately defined by the designeror decision analyst modeling the preferences, a utility scaleof 0–1 is often used. The most preferred attribute level willreturn the best utility value of 1 (UBest = 1), while the leastpreferred level will yield the worst, 0 (UWorst = 0)

Exponential SAU formulation, shown in Eq. (1), was usedin the current study as it requires the estimation of only oneparameter: risk tolerance (RT). The RT values for attributeswere assessed by determining the best and worst data valuesand applying certainty equivalent (CE) analysis. The CE isthe value of an attribute for which the consumer is indifferentbetween the CE and the probabilistic expected consequence.

The risk attitude of the designer can be modeled in utilityfunctions (Eqs. 1–3). For monotonic increasing functions, therisk prone utility will have an expected consequence smallerthan the CE, while the risk averse utility indicates that theexpected consequence is higher than the CE. Accordingly, therisk prone attitude reflects that the designer is confident ofachieving better perceived goodness (better than the expected

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Fig. 2 Mobile phone candidate models for fit analysis (USA)

Fig. 3 Mobile phone candidate models for fit analysis (Asia)

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consequence), while the risk averse attitude suggests a lackof it.

Ui (xi j ) = Ai − Bi ∗ e−Min(xi j )

RTi (1)

Ai = e−Min(xi j )

RTi⎡⎣e

( −Min(xi j )RTi

)

− e

( −Max(xi j )RTi

)⎤⎦

(2)

Bi = 1⎡⎣e

( −Min(xi j )RTi

)

− e

( −Max(xi j )RTi

)⎤⎦

(3)

where,

xi j : The j th value of attribute iRTi = Risk tolerance for the attribute iMin(xi j ) = Minimum value of the attribute i across allalternativesMax(xi j ) = Maximum value of the attribute i across allalternatives

A consumer’s preference across each attribute range is repre-sented by the mathematical expression of the SAU functions.All attributes in this case study are monotonic. The SAU func-tions, developed using the above explained procedure for thenine attributes, are as follows:

U1(x1 j ) = 1.007 + 12.265 ∗ e−(x1 j400 ) (4)

U2(x2 j ) = −0.685 − 354.900 ∗ e−(x2 j20 ) (5)

U3(x3 j ) = 1.157 + 651.154 ∗ e−(x3 j0.3 ) (6)

U4(x4 j ) = 1.093 + 2.600 ∗ e−(x4 j1.5 ) (7)

U5(x5 j ) = 1.089 + 161.685 ∗ e−(x5 j60 ) (8)

U6(x6 j ) = 1.163 + 1.374 ∗ e−(x6 j120 ) (9)

U7(x7 j ) = 1.134 + 3.766 ∗ e−(x7 j

3 ) (10)

U8(x8 j ) = 1.019 + 2.769 ∗ e−(x8 j

1 ) (11)

U9(x9 j ) = 1.157 + 466.573 ∗ e−(x9 j0.5 ) (12)

The SAU function of the attribute “weight” (k2) implies amonotonically decreasing exponential function. For increas-ing values of weight (k2), its utility will decrease. This mono-tonically decreasing condition indicates that if value of theattribute (k2) on the consequence axis is reduced, the util-ity improves. In this case, the designer is risk prone for theweight attribute (k2). The certainty equivalent (CE2) is 116g,and the corresponding (RT2) value is 20.

The balance of the attributes can be modeled usingmonotonically increasing functions. When their consequence

values increase, their utility values also increase. Thedesigner is risk averse for screen size (k3), camera (k4), bat-tery life (k5), memory (k6), and transfer speed (k7). CE3 ofscreen size is 2.2 inch, and RT3 value is 0.3. RT4 of camerais calculated as 1.5, and its CE4 = 3.5 megapixels. WithCE5 = 375 min, battery life is determined as RT5 = 60.The risk tolerance of memory, RT6 , is calculated as 120,and CE6 indicates 140 mb. CE7 of transfer speed is 6.5 mbs,and the corresponding is RT7 = 3.0. In the same manner,CE8 = 2.5, RT8 = 1; and CE9 = 1.5, and RT9 0.3 areassessed.

We modeled the aggregation of the SAU functions toreflect the overall view of the designer while taking intoaccount risk and uncertainty. To assess the fitness of mobilephone model alternatives for each customer group with anaggregated utility approach, the multiplicative form wasused. Applying the following relationship (Eq. 14), the multi-plicative form may be shown to help solve decision problemsby ranking alternatives based on their utility scores.

Uall(x) = 1

k

[n∏

i=1

(K kiUi (xi j ) + 1) − 1

](13)

1 + K =n∏

i=1

(1 + K ki ) (14)

where,

Uall(x): The total utilityxi j : The j th value of attribute iUi (xi j ): The single attribute utility for attribute iki : Attribute-scaling parameter for attribute iK : Normalizing constant

To measure the relative influence of each attribute, attri-bute trade-off parameter (ki ) values were determined. Thesereflect the designer’s desire for trade-offs under uncertainty.With respect to each consumer group, the importance of eachattribute was determined. For example, a risk assessment wasconducted for the group including U.S. females in the 30–39age group. The rank ordering for importance of all attributeswas established as: Keypad (k8) = Form (k9) = Battery life(k5) > Screen Size (k3) > Weight (k2) > Contact (k1) =Transfer speed (k7) > Camera (k4) > Memory (k6). Basedupon consumer preferences as compiled by the survey data,results show that the most significant attributes were rankedas keypad, form, and battery life.

Beyond a rank order of importance, specific ki valueswere determined through standard decision scenarios. First,k8 was calculated, as it had the highest importance. Next, weassumed that the consumer was confronted with the decisionscenario shown in Fig. 4, in which (∗) represents best value ofthe attribute, and (◦) represents the worst value. This scenario

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Fig. 4 Keypad trade-offdecision scenario: aprobabilistic, versus b certainsituation

(a) (b)

is a von Neumann–Morgenstern utility and is built upon amulti-axiomatic formulation where the expected value of theutility function can be presented as shown in Eq. (15). Wecompared the indifference point by asking the consumer atwhat probability value she would feel indifferent between aprobabilistic outcome of getting the best or the worst result,and the certain outcome of getting the best in one attribute butthe worst in all others. Figure 4 shows this decision scenariofor attribute 8, where Uall(X◦

1, . . ., X◦7, X∗

8, X◦9) is equal to

U8(X∗8) since all other utility values are zero. Based on the

utility theory, UBest is equal to 1. Equation (15) demonstratesthis relationship and calculates the value of k8. When p8 valueis 0.5, the consumer finds the probabilistic case and the cer-tain situation (shown on the right in Fig. 4) to be equal. Thisindicates that keypad (k8)’s scaling (trade-off) factor is 0.5.Keypad’s alternative consequences are presented in quanti-tative means as follows: Full QWERTY keyboard & Touchscreen = 1, Full QWERTY Keyboard = 2, Touch screen =3, QWERTY keypad & Touch screen = 4, QWERTY keypad= 5.

E(u) = p∗81 + (1 − p8)

∗(0) = k∗8Uall(X◦

1, . . . , X◦7, X∗

8, X◦9)

= k∗8U8(X∗

8) = k∗81 = k8 (15)

In Eq. 15, pi denotes the probability, and E(u) is theexpected value of the utility function. Simplification of(Eq. 15) yields p8 = k8 = 0.5. For calculating the trade-offparameter for form (k9), keypad k8 and form k9 are com-pared to arrive at an indifference point. Quantification of theform consequences are done by assigning numerical valuesto the alternatives: Flip (folder) = 1, Slider = 2, Block = 3,and Clamshell = 4. As a result of the calculations shown inEqs. (16–18), k9 is found to be equal to k8.

The utility comparison was completed to evaluate therelationship between k5 and k3. An indifference pointwas achieved when the battery life = 420 min and screensize = 2.5 inches. k5 was determined to be 0.5. Using themethodology shown in Eqs. (16–18), k3 was determined tobe 0.471. Using a similar decision process, the remainder ofthe ki ’s were determined as: k2 = 0.344, k1 = 0.1527, k7 =0.1527, k4 = 0.121, and k6 = 0.049. Attribute trade-offparameters reflect designer’s preferences in the decision sce-narios. The multiplicative formulation was scaled between 0

and 1 using the scaling constant, K . Using Eq. 14, K valuewas calculated as −0.97. Each age, origin, and gender groupwas evaluated using the aggregated utility. The overall prefer-ence rankings for mobile phone alternatives shown in Figs. 2and 3 were achieved as shown in Table 1.

U(1000, 125, 1.9, 1.3, 300, 20, 3.6, 5.0, 3.0)

= U(1000, 125, 1.9, 1.3, 300, 20, 3.6, 1.0, 4.0) (16)

k∗1u1(1000) + k∗

2u2(125) + k∗3u3(1.9) + k∗

4u4(1.3)

+k∗5u5(300) + k∗

6u6(20) + k∗7u7(3.6)

+k∗8u8(5.0) + k∗

9u9(3.0)

= k∗1u1(1000) + k∗

2u2(125) + k∗3u3(1.9) + k∗

4u4(1.3)

+k∗5u5(300) + k∗

6u6(20) + k∗7u7(3.6)

+k∗8u8(1.0) + k∗

9u9(4.0) (17)

k∗8(1) + 0 = 0 + k∗

9(1) (18)

According to the consumer preference rankings shownTable 2, U.S. males and females in the 14–22 year cate-gory are most likely to select “Block or Flip” style as formand “Full QWERTY keyboard” as keypad. On the otherhand, U.S. males and females in the 23–29 age group aremore likely to select “Block” as form and “Touch screen”as keypad. For this group, large screen size and long batterylife are also critical factors in purchasing decisions. Asianmales and females between the ages of 14 and 22 prefer“Slider” as form and “Full QWERTY keyboard and touchscreen” as keypad, and colorful designs influence their pref-erences. On the other hand, Asian males and females 23–29 years old regard “QWERTY keyboard and touch screen”as the best keypad option, and “Slider” as the preferredform. High resolution camera and easy user interface arealso among the significant factors with high impact on con-sumers’ response.

Assessing the aggregated utility can help provide thebest trade-offs under uncertainty and help rank order mobilephone alternatives. Our results suggest that for the 14–17 agegroup, optimal models such as rumor II (0.82), VU cu920(0.89), impression SGH A877 (0.64), and chocolate vx8550(0.95) are recommended (aggregated utilities in parenthe-ses). The highest ranking preferred models for the 18–22age groups are behold t919 (0.98) and N9 (0.59). Amongthe 23–29 age group, bl40 new chocolate (0.94), gm750

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Table 2 Aggregated utility ranking for each age and gender group

Models Criteria

Contact Weight Screen Camera Battery Memory Transfer Keypad Form U(x) Rankcapacity U2(x2) size U (x4) life U3(x6) speed U3(x8) U3(x9)

U1(x1) U3(x3) U3(x5) U3(x7)

USA male 14–17

EnvTouch Vx11000 0.519 0 1 1 0.256 1 1 0 1 0.66 2

Rumor 2 0 1 0 0 1 0 0 1 0 0.82 1

Nokia 5730Xpressmusic 0 0.274 0.689 1 0 1 0 1 0 0.59 3

USA female 14–17

Vu-cu920 0 1 1 0 0 0 1 1 1 0.89 1

Incite CT810 1 0 1 1 1 0.108 1 1 1 0.79 2

LG KS360 0.761 0.236 0 0 0.76 1 1 0 0 0.32 3

Asia male 14–17

Impression SGH A877 0.731 0 1 1 0 0.734 1 1 1 0.64 1

Nokia 6760 1 0.263 0 1 1 0 1 0 1 0.62 3

SPYDER II 840 0 1 0.898 0 0.644 1 1 1 1 0.63 2

Asia female 14–17

Chocolate vx8550 1 1 0 0 0.87 1 1 1 1 0.95 1

LG Shine cu720 0 0 1 1 0 0 1 1 1 0.067 3

SGH A76Propel 1 0.167 1 0 1 0 1 0 1 0.72 2

USA male 18–22

eNV3 VX9200 1 1 1 1 0.632 0 1 1 1 0.31 2

Alias 2 U750 1 0.072 1 0 0 1 0 1 1 0.37 1

KT610 1 0 0 0 1 0 0.458 1 0 0.20 3

USA female 18–22

Behold T919 1 1 1 1 0.694 0.121 1 1 1 0.98 1

Instinct 1 0 1 0 1 1 0.325 1 1 0.95 2

Blackberry curve 8330 1 0.91 0 0 0 0 0 0 1 0.72 3

Asia male 18–22

Xneon (GR 500) 0 0.0384 1 0 0 0 0 1 1 0.113 3

Propel Pro (shg i627) 1 0 0 1 0.938 1 0 0.007 1 0.118 1

S3650 0.486 1 1 0 1 0.55 1 0 1 0.117 2

Asia female18–22

SonyEricsson Xperia X2 1 0 0.0455 0.721 1 0.079 1 1 1 0.57 2

N97 1 0.06 1 0 1 1 0 1 1 0.59 1

M8910 PLXON12 0 1 0 1 0 0 1 0 0 0.53 3

USA male 23–29

Apple iPhone 3G 1 1 0 0 0 1 1 1 1 0.69 2

Bl40 New Chocolate 0 0.84 1 1 0 0.835 1 1 1 0.94 1

HTC touch pro 2 1 0 0.287 0.735 1 0 1 0 0 0.29 3

USA female 23–29

Blackberry Storm9530 1 0 1 0.815 0.285 1 0.677 1 1 0.91 2

GM750 1 1 0.837 1 1 0 1 1 1 0.99 1

Moto Q8 0 0.838 0 0 0 0 0 0 1 0.57 3

Asia male 23–29

SGH-A77 1 1 1 0 1 0 0.595 0 1 0.572 1

Venus VX8800 1 0 0 1 0 0.068 1 1 1 0.537 3

SCH-U490 1 0.536 0.506 0 1 1 0 1 1 0.570 2

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Table 2 continued

Models Criteria

Contact Weight Screen Camera Battery Memory Transfer Keypad Form U(x) Rankcapacity U2(x2) size U (x4) life U3(x6) speed U3(x8) U3(x9)

U1(x1) U3(x3) U3(x5) U3(x7)

Asia female 23–29

LG GD900 1 0.122 0.923 0.905 0 0.972 1 1 1 0.88 1

S3500 1 1 0 0 1 0 1 0 1 0.83 3

Motorola RokrZZN50 1 0 1 1 0 1 0 1 1 0.85 2

USA male 30–39

HTC Touch HD 1 0 1 1 0.876 0 1 1 1 0.88 1

S800 Jet 0 1 0 1 0 1 0 1 1 0.49 3HTC Hero 0 0.153 0.241 1 1 0.901 1 1 1 0.61 2

USA female 30–39Nokia 6700 Classic 0 0.444 0.731 1 0 0.83 1 1 0 0.81 2

W510 0 1 0 0 1 0 0.793 1 1 0.95 1

SGH-1907 Epix 1 0 1 0.407 0.942 1 0 0 0 0.80 3

Asia male 30–39I8000 Omnia II 0 0.077 1 0.653 1 1 1 1 1 0.18 3

S8300 Ultratouch 0.807 1 0 1 0 0 1 1 1 0.21 1

iPhone 3GS 1 0 0.903 0 0.464 0.916 0 0 0 0.20 2

Asia female 30–39B7610 Omnia Pro 1 0 1 1 1 0.714 1 0 0 0.85 2

S8003 JET 0 1 0.7 1 0 1 0 1 1 0.87 1

Motrola A3100 1 0.650 0 0 0.594 0 1 1 1 0.80 3

USA

ASIA

Ma leFe m a le

QWERTYFlip/SlideLar ge Scr een

39 29 22 17 14

Easy In ter faceLong bat tery lifeTouch ScreenCamera

14 17 22 29 39

Fig. 5 The allocation of mobile phone features and potential productfamily with 16 members

(0.99), sgha777 (0.57), and LG gd900 (0.88) models are pop-ular preferences. For the 30–39 age group, HTC touch HD(0.88), w510 (0.95) and s8003 jet (0.87) were ranked thehighest.

Figure 5 illustrates the preference of mobile phone fea-tures based on origin, gender, and age. The 16 family mem-bers are clustered to highlight the key phone features persegment.

Fig. 6 The product family with 8 members based on origin and age

For a mobile phone company with limited resources,designing and producing 16 new products might be a daunt-ing task. To reduce the size of the product family, our pro-posed derivation for customer perceived utility data canhelp determine a smaller mobile phone family design. If acompany has the means to develop only eight new prod-ucts, clustering methods can be used to generate appropriateoptions. For example, Fig. 6 depicts the features of eightmobile phones that can satisfy consumer needs classified

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Fig. 7 The product family with 8 members based on gender and age

Fig. 8 The product family with 4 members based on gender and origin

Fig. 9 The product family with 4 members based on age

only by age and origin. Figure 7 shows the same prod-ucts overlaid on a gender axis. These two cluster optionscover all market segments by adding one or two design fea-tures.

In the same manner, distinguishing only four new productsfor a product family is achievable. Two possible solutions arepresented in Figs. 8 and 9. The difference between them is theclustering criteria. Figure 8 divides customers by origin andgender. In this arrangement, mobile phones popular among

Asian buyers will not be so among U.S. buyers. The draw-back to this categorization is that every phone will have 5 or6 key features that will raise the price. Alternatively, Fig. 9provides a clustering with 3 key features for the 14–17 yearold group, 4 key features for the 18–22 group, and 6 keyfeatures for the remaining two groups.

Utility assessment of phone models to investigate productfamily member fit to consumer group

In addition to investigating product models for their con-sumer group fit as a basis for developing a product familydesign or simply for use as a benchmark, a company can alsoassess the appropriateness of their existing offerings for spe-cific consumer groups. This study used four mobile phonemodels from Samsung (shown in Fig. 10) to investigate theirfit for U.S. females aged 30–39.

Table 3 presents utility ranking data achieved through theimplementation of MAUT on the nine attributes found tosignificantly impact consumer preferences. Results show thatthe overall utility ranking of the Samsung D880 and Samsungi607 are nearly identical. Thus, one of these alternatives couldbe removed from the market to simplify company operationsand consumer purchasing decisions. It may also be seen thatthe utility values for the remaining two models are inferior incomparison to the D880 and the i607; the implication is thatunless they satisfy other consumer group preferences, theyshould be withdrawn from the market.

Comparison of the presented method to conjointanalysis

Conjoint analysis (CA), a well-established and widely usedmethodology, requires surveyed consumers to discern theirpreferences. In our proposed method, we use consumer sur-veys for analysis; hence, here we show the methodologicaldifferences and implementation results between the two tech-niques. Table 4 presents the methodology steps side-by-side.Those for CA are adopted from Gustafsson et al. (1999).Implementation details are given following the table. Thesame data set was utilized for the CA process as was for ourproposed method. Note that for neither method do we extendour comparison to the validation; however, various methodsexist for validation including virtual reality prototypes (e.g.,Carulli et al. 2012).

Step 1: Identify attributes and their levels

Although guidelines were provided for how to select thisattribute set, no specific method for the selection wasdictated. Gustafsson et al. (1999) recommended selectingattributes that: (1) can impact the purchasing decisions,

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Samsung X820 Samsung E700 Samsung D880 Duos Samsung i607

Fig. 10 Samsung mobile phone models included in the fit evaluation

Table 3 Investigation of fit for company models for a consumer group

Models Attributes

ContactcapacityU1(x1 j )

WeightU2(x2 j )

ScreenSizeU3(x3 j )

CameraU4(x4 j )

BatteryLifeU5(x5 j )

MemoryU6(x6 j )

TransferSpeedU7(x7 j )

KeypadU8(x8 j )

FormU9(x9 j )

Uall (x) Rank

USA female 30–39ki 0.153 0.344 0.471 0.121 0.500 0.049 0.1527 0.500 0.500RT 400 20 0.3 1.5 60 120 3 1 .5CE 2000 116 2.2 3.5 375 140 6.5 3 3.5Samsung X820 0 1 0 0 0 0.457 0 0 0 0.36 4Samsung E700 1 0.377 0.539 0.741 0.616 0 0 0.881 0.731 0.89 3Samsung D880 0 0.008 0.898 0 0.884 0.330 0.552 1 1 0.95 1Samsung i607 1 0.129 0.898 1 1 0.357 0.552 0.935 0.381 0.94 2

Table 4 Methodologicalcomparison

Steps Conjoint analysis Historical data mining and MAUT

1 Identify a limited number ofattributes and their levels

Using historical market share (or sales) information and realproduct features (form and function) and employingmultiple regression identify significant attributes

2 Configure attributes and levelsinto individual concepts

Conduct surveys using the identified attributes from step 1to gauge preferences for specific market segments (e.g.,origin, age, gender)

3 Conduct surveys to gaugepreferences

Using logistic regression test the significance of varyingpreferences across market segments

4 Using regression (or part-worthmodels), attributes and levelswith impact on the preferenceare identified

For each market segment, single attribute utility functionsand the aggregation function are formulated

5 Validate the results bothinternally and externally.

Using actual designs to benchmark or potential concepts toevaluate, utility calculations are used to compare designs,and eventually fit designs to market segments (andeliminate designs with limited utility)

(2) can be altered, and (3) can be used in benchmark-ing. For a comparison with our proposed method, the sameattributes were applied to the CA. Further, the comparisonincludes only a subset of mobile phones shown in Fig. 10.

Although our proposed methodology originated using 16attributes (see Table 1), applying the historical data min-ing of actual market share information reduced them to 9(Table 5).

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Table 5 Attributes and levelsNo. Attribute (Notation) No. of levels Levels

1 Contact capacity (n) x1j 2 1000, 30002 Weight (grams) x2j 4 66, 85, 105, 1163 Screen size (pixels) x3j 3 1.8, 2.0, 2.34 Camera (pixels) x4j 3 1.3, 3.0, 5.05 Battery life (h) x5j 4 300, 350, 400, 4506 Memory (Mb) x6j 4 20, 60, 64, 807 Transfer speed (kbps) x7j 2 3.6, 5.68 Keypad (various types) x8j 3 1 (Full QWERTY keyboard &

Touch screen), 3 (QWERTY keypad& Touch screen), 5 (QWERTY key-pad)

9 Form (Clamshell, Block, Slider) x9j 3 1, 2, 3

Table 6 Concept configurations

Concepts Contact capacity Weight Screen size Camera Battery life Memory Transfer speed Keypad Form

1 −1 −1 −1 −1 1 1 1 1 1

2 −1 −1 −1 1 1 1 −1 1 −1

3 −1 −1 1 −1 1 −1 1 −1 −1

4 −1 −1 1 1 1 −1 −1 −1 1

5 −1 1 −1 −1 −1 1 1 −1 1

6 −1 1 −1 1 −1 1 −1 −1 −1

7 −1 1 1 −1 −1 −1 1 1 −1

8 −1 1 1 1 −1 −1 −1 1 1

9 1 −1 −1 −1 −1 −1 −1 1 1

10 1 −1 −1 1 −1 −1 1 1 −1

11 1 −1 1 −1 −1 1 −1 −1 −1

12 1 −1 1 1 −1 1 1 −1 1

13 1 1 −1 −1 1 −1 −1 −1 1

14 1 1 −1 1 1 −1 1 −1 −1

15 1 1 1 −1 1 1 −1 1 −1

16 1 1 1 1 1 1 1 1 1

Step 2: Configure attributes and levels into individualconcepts

Gustafsson et al. (1999) liken this step to a design of experi-ments applied to preference decisions, requiring the config-uration of concept combinations for all attributes and theirlevels. The case at hand would require configuring 20,736 dif-ferent concepts. In the literature, fractional factorial designsare recommended to reduce the number of configurations.Thus, only 2 levels for each attribute are considered, yield-ing 128 configurations, and a 29−5 fractional design reducingthe configurations to 16 is adopted. Table 6 lists these con-cept combinations, where −1 and 1 represent the lowest andhighest levels of the attributes.

Step 3: Conduct surveys for preference assessment

At this stage, a rank ordering of concepts for each individualparticipant is collected by reaching a sample of the targetpopulation through appropriate means. This study uses pre-viously collected survey data (from Kim and Okudan 2009b).

Step 4: Calculate part worth models

Based on the survey data, models are used to derive thepart worths. In general, linear functions are used wherethe attributes are independent variables (Kohli and Krish-namurti 1989). Equation 19 presents a typical part worthmodel, where y is the preference measure; x1 − xn are

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Table 7 Comparison of the rank ordering of concepts

Models Methods

Conjoint Presented Rankanalysis method agreement(CA) (PM)

ConceptsSamsung X820 0.79 0.36 Same across methods, 4Samsung E700 0.86 0.89 Same across methods, 3Samsung D880 0.89 0.95 2 for CA, 1 PMSamsung i607 0.92 0.94 1 for CA, 2 PM

attributes; and b, c,…, z are part worths. Such models canbe solved using the collected data, taking into account thetype of attributes (categorical vs. continuous) using regres-sion, ANOVA or ANCOVA models; for this analysis, we usedregression. First individual attribute part worth models wereconstructed; next all were aggregated assuming linearity. Asfor the weights of individual attributes during aggregation,normalized weights from the proposed method were used toensure a head-to-head comparison. Table 7 provides a com-parison of the results under both methods.

y = a + bx1 + cx2 + · · · + zxn (19)

As may be observed, there is a rank reversal for the toptwo concepts. It is possible that because only a 29−5 designwas used involving only two levels for each attribute, theremay well be room to improve the predictive power of thepart worth models. However, the original task becomes com-putationally expensive as it requires perusing all possible20,736 concept configurations. This problem is well recog-nized. Scholars have proposed the use of heuristics to effi-ciently search the feasible solution space utilizing individualpart worth models (e.g., Kohli and Krishnamurti 1989; Kohliand Sukumar 1990).

Discussion and conclusion

By evaluating the most salient features of mobile phone formsand functions, companies can determine how to best appro-priate limited resources. In stage 1 of our analysis, historicaldata mining, logistic regression filtering, and survey resultsprovide important features for use in the design decision mak-ing process. In stage 2, the SAU functions aid in evaluatingthe “goodness” of alternatives for specific consumer grouppreferences. As such, consumer preference rankings for thedetermined alternatives of mobile phones can be established.Based on the highest utility rankings, mobile phone modelsmost suited for each age, origin, and gender groups can beidentified.

As illustrated by our proposed methodology and analysis,companies can determine the suitability of design alterna-tives for different customer groups. Product family designsdeveloped this way not only avoid over-design but also arepositioned for higher customer satisfaction. The eliminationof product models that render very close utilities can reducesupply chain complexity and thereby provide higher com-petitiveness for a company. Moreover, the number of prod-uct family members can be adjusted in accordance with acompany’s marketing strategy, allowing flexibility in prod-uct design and better resource allocation.

Our proposed approach eliminates certain limitations ofother concept selection and marketing research methods. Forexample, the use here of MAUT-based utility ranking doesnot require variables to be mutually preferentially indepen-dent whereas AHP requires, and CA assumes mutual andpreferential independence; MAUT’s multiplicative aggrega-tion form allows for trade-offs as shown in this case study.Moreover, our approach eliminates the time-consuming pair-wise judgments that both Pugh’s method and AHP require,because once the SAU functions are constructed, the utilityevaluation of a design concept requires only identifying thedesign features and placing them in the aggregate utility func-tion. Perhaps more importantly, because of the significantmarket share feature identification (stage 1), potential over-estimation or underestimation results derived from survey-based preference estimation are largely eliminated. Finally,because utility functions capture the probabilistic nature ofthe preference decisions, consumer choice simulations nec-essary for CA are not required. In our comparison, we havealso shown that realistic problems with more than 10 attri-butes and varying levels can make implementing CA moreexpensive.

In summary, our methodology demonstrates that the mostappropriate one from a set of mobile phone alternatives canbe recommended by matching consumer preferences andattributes using historical data mining, logistic regression fil-tering, and consumer surveys, and then confirming the aggre-gated utility values of product preferences using MAUT. Thisproposed method provides a reliable way of fitting designdecision-making alternatives to market segments in order tosatisfy consumer requirements and a technique to help pre-dict which future mobile phone designs have a high likeli-hood of success in the marketplace—allowing the goal ofmass customization and personalization to be achieved. Inaddition, our proposed analysis considers clustering marketsegments based on gender, origin, and age, enabling compa-nies to determine the size of a product family according toits marketing strategy and financial situation.

Although the solo applications of MAUT and CA havelimitations in determining feasible trade-offs, our proposedmethod avoids biases and inconsistencies among consumerpreferences and their ranges. Furthermore, it determines a set

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of desirable trade-offs and provides optimal alternatives byassessing and prioritizing available information. Future workwill focus on: (1) confirming results through comparison toreal market data, and (2) applications of the methodologyto different product and consumer groups to test the generalapplicability of the proposed method.

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