J Intell Manuf
DOI 10.1007/s10845-012-0699-5
Perceived feature utility-based product family design: a mobile
phone 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 mass
customization and personalization, effectively eliciting consumer needs across different market segments is critical.
Although functional performance specifications and adequacy of various design forms can be measured directly
and objectively, many designers and engineers struggle with
clearly evaluating product criteria requiring subjective consumer input; the fact that these inputs change over time
further complicates the process. To appropriately evaluate
product criteria, an effective design decision-making analysis is required. In this study, we propose a methodology
to assure effective elicitation of needs and their inclusion
in design decision making and illustrate it using a mobile
phone product family design scenario. First, consumer
perceived utility of design features is gathered using a questionnaire (500+ responses) and then modeled using multiattribute utility theory to facilitate the evaluation of a
product family while responding to needs across customer
clusters shaped by demographics. The methodology goal is
to determine the relative goodness of a product family in comparison to its competition. We also compare and evaluate the
application of the proposed method to conjoint analysis.
G. E. Okudan
School of Engineering Design, The Pennsylvania State University,
University Park,
PA 16802, USA
G. E. Okudan · T. H. Kim
Department of Industrial and Manufacturing Engineering,
The Pennsylvania State University, University Park,
PA 16802, USA
e-mail: gek3@engr.psu.edu
M. C. Chiu (B)
Department of Industrial Engineering and Engineering Management,
National Tsing Hua University, HsinChu 30001, Taiwan, ROC
e-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 goodness or utility of a product improves as the result of decisions pertaining to functional concepts. Thus, manufacturers
typically make investments to upgrade their products. For
the most part, however, these development practices—especially in the production of mobile phones—remain heavily
focused on increasing the number function options regardless of actual consumer wants or needs. These design and
manufacturing decisions tend to increase consumer confusion and frustration. A recent survey indicated that 61 % of
mobile phone owners believe there are too many unnecessary
features on their phones (PCWorld 2009).
Many people use products for their primary application
only. For example, they go no further than using their mobile
phones for making phone calls and their mp3 players for listening to music. For them, many built-in functions are rarely
or never used. Studies have highlighted the fact that overloaded mobile phone functionalities create unnecessary complexity for most users (e.g., Ling et al. 2007; Renaud and van
Biljon 2010). To improve customer satisfaction, manufacturers could effectively investigate consumer interest using
evaluation methods to screen out unimportant features, forms
and functions. Simultaneously, consumer diversity could be
taken into account to achieve an effective mass customization.
Consumer product preferences differ by age, race, gender,
and cultural background. Some gender studies have found
preferences for gender-specific colors: blue for males and
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J Intell Manuf
pink for females (Andree et al. 1990). Color assignment
studies have found preferences differ significantly across
age groups; for example, the preference for blue decreases
steadily, whereas the popularity of green and red increases
as age advances (Dittmar 2001). Age differences can also
impact shape preference; for instance older phone users who
have presbyopia often prefer larger keypads while younger
ones often prefer easy-to-carry slim phones. Accordingly,
manufacturers must customize products for certain populations—a practice cultivating the development of product
families.
This work presents an approach for choosing product families intended to satisfy customer needs accurately, based
on both product features and the demographics of potential
consumers. Section “Literature review” provides a summary
of the related literature. In Section “Proposed methodology”, we propose a two-stage methodology for capturing
consumer preferences and modeling utility to guide product
family development. We present our case study and discuss
results in section “Case study”. In Section “Comparison of
the presented method to conjoint analysis”, we compare our
proposed method to conjoint analysis (CA). Section “Discussion and conclusion” concludes the study and identifies
future work directions.
Literature review
Research within manufacturing companies must investigate
appropriate product design features pertaining to both form
and function in order to support their vision and improve
their market share. In the engineering or product design
domain, conceptual design methods such as Akao’s (1997)
quality function deployment (QFD), Wang’s (2002) fuzzy
sets, Saaty’s (1980) analytic hierarchy process (AHP) and
Pugh’s (1991) method have been used to screen out unimportant features or to evaluate priorities to satisfy consumer
requirements and desires.
Using QFD (Akao 1997), designers gather information
about consumer requirements in terms of importance ranking
to create direct associations with the functional requirements
(Pullman et al. 2002). QFD analysis gives designers sufficient
direction about which components require improvement to
accommodate consumer demand. However, this methodology neither provides an aggregate utility score for potential
design concepts, nor easily incorporates market segments
into a perceived utility analysis.
Analytic hierarchy process (Saaty’s 1980) involves pairwise analysis, comparing potential concepts in order to reach
a weighted order of preference. Using a standard 1–9 scale,
a value of 1 indicates equal utility (importance) in terms of
preference whereas a 9 indicates the extreme utility of a concept in comparison to another. When designers are faced
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with complex decision making, AHP provides a suitable tool
for systematically evaluating the relative importance of consumer requirements and the relative goodness of possible
concepts. AHP assumes all criteria to be mutually preferentially independent, and it aggregates the relative priorities across customer requirements in a linear form. However,
such an assumption is rather restrictive, only representing
rare practical cases.
Isiklar and Buyukozkan (2007) proposed a multi-criteria decision making (MCDM) method, which combines
AHP and TOPSIS (Technique for Preference by Similarity to the Ideal Solution) (Hwang et al. 1981). Their
method uses AHP to identify weights across multiple criteria in order to identify features based on their relevance to consumers’ preferences. TOPSIS, originally proposed by Hwang and Yoon as a way to identify the best
alternative with the shortest distance to the ideal solution, is then used to identify the best option from the
AHP findings. Isiklar and Buyukozkan’s study examining the mobile phone industry showed that their MCDM
method helps select a better potential solution by comparing choices across multiple criteria. However, this approach
may not completely take into consideration the relative
importance of certain criteria if the decision makers do
not have the up-to-date consumer preferences about specific
product functions. Additionally, AHP limitations can affect
analysis.
In an effort to solve the concept selection problem most
appropriately, Wang et al. (2006) proposed a multi-objective
optimization algorithm to determine the optimal selection
for 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 perceived
utility of design concepts is evaluated in a pairwise fashion
using a scale (better, 1; equal, 0; inferior, −1), coarser than
that used in AHP. In general, Pugh’s method is practical and
commonly used. However, if team members opine differently
with regard to consumer requirements or design criteria, time
may be wasted reaching a general agreement. Furthermore,
if a design team member has less than adequate knowledge
about the design challenge at hand, the probability of selecting the right concept will decrease (Pugh 1991). However, the
method helps to choose quickly the best conceptual design
when each designer independently selects criteria for comparison.
When users are faced with linguistic assessments of utility such as “slightly better” and “much better,” the fuzzy set
method (Wang 2002) can be used to help determine the most
appropriate design concept. Using this approach, ambiguous
linguistic terms are represented by arithmetic operations for
evaluating design functions (e.g., Liu et al. 2012). QFD, AHP,
and Pugh’s concept screening method can also be modeled
J Intell Manuf
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) evolved
during the 1970s. Cattin and Wittink (1981) reported that
more than thousand CA applications had been conducted
in the first decade after its introduction. CA measures how
consumer preferences and perceptions change and is frequently used to analyze the relative importance each product
feature has on purchasing decisions (Barone et al. 2007).
It enables a design team to easily and effectively assemble the most highly valued combination of features before
launching a new product. As described by Green et al.
(1981), the CA process first requires data collection to estimate each respondent’s parameters for the utility function;
then subject background variables are related to utility functions to identify potential market segments; finally, a set of
design configurations is evaluated using choice simulators to
determine market share information. Although CA may be
used to classify consumer needs for market research (Pullman et al. 2002) or to analyze consumer preference factors
in the product design decision-making process (Du et al.
2006), it does not always provide valid results when testing new products because respondents tend to overestimate
their preferences toward less important factors (Barone et al.
2007).
Kansei Engineering (KE) focuses on consumers’ emotional responses to product forms and functions. It fosters an
understanding of consumer preferences by evaluating their
psychological interactions with product features and appearances (Nagamachi 1995). Within this framework, analysis
of design elements helps understand latent consumer preferences and can potentially predict consumption in unknown
cultural environments (Veryzer 1993). KE starts by semantically capturing consumer feeling (Kansei) about a product
and progresses to associating potential design characteristics
that correspond to the captured Kansei. The final step focuses
on adjusting design configurations to elicit the intended
Kansei from the consumers. Recently, KE has been used
in unison with latent semantic analysis (Smith et al. 2012).
However, KE focuses primarily on a product’s ergonomic
features (Nagamachi 1995); thus, incorporating quantitative or technical features increases its analytic challenges.
The existing concept selection and marketing research
methods may neglect the interdependence among criteria or
the uncertainty in customer perceptions. In many, consumer
perception is incorporated while developing new products;
yet, consumers have been shown to overestimate or underestimate their preferences (Barone et al. 2007). To respond
to these issues, we propose a method that not only makes
use of market information to elicit the important product
characteristics using historical data mining but also tackles
interdependence and uncertainty issues through the implementation of the Multiple Attribute Utility Theory (MAUT)
while mapping product variants to market segments. We
demonstrate the method using a mobile phone product family
to analyze the appropriateness of product family members to
a particular market segment.
Product family refers to developing a set of products that
shares common components yet exhibits adequate differentiation intended to target different market niches (Jiao and
Tseng 1999; Simpson 2004). The ultimate goal of designing
a product family is to provide a variety of products to the
market with an effectiveness close to that of mass production
(Jiao et al. 2007).
Proposed methodology
Our proposed method has two complementary stages. It
extends and aggregates our prior work (Kim and Okudan
2009a,b; Kim et al. 2010). The overall implementation flow
of the method is provided in Fig. 1.
Stage 1: Initial matching of consumer preferences
and product features
Historical data mining
Historical data mining aims to identify the most significant
design features that affect market shares of leading companies (Kim and Okudan 2009a,b). The authors conducted
a multiple regression analysis using actual market data to
determine whether form or function features had a greater
impact on market shares as a way to guide limited resource
allocation. This analysis classified of all mobile phone design
features as either form or function according to patent analysis. If a feature had a design patent, it was deemed a form
feature; if it had a utility patent, it was classified as a function
feature. In this paper, we use multiple regression analysis to
identify the significant design features influencing product
purchase, based on historical market data.
This research analyzed the product features of 1028
mobile phones released between 2003 and 2008. Unimportant variables were eliminated using the Variance Inflation
Factor (VIF) method to quantify the severity of multicollinearity in an ordinary least squares (OLS) regression analysis. For the remaining variables, regression models were
formed and the best fitting model was determined using
Mallow’s Cp method, which is dependent on the number
of variables and the sample size as well as the error terms.
Finally, weights of important design variables were determined through partial regression coefficients. Further background on VIF and Mallow’s Cp are provided in Kim and
Okudan (2009a,b).
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Fig. 1 Implementation flow of
the proposed method
Historical Data
Mining
Logistic
Regression
(Filtering to
Identify Features)
Confirm Features
Using Surveys
Determine /Develop
Possible Matching
Concepts for
Consumer Clusters
Assess Utility of
Concepts for
Consumer Clusters
Stage 1
Stage 2
Initial matching of consumers’
preference of product features (form & function)
Multi-attribute
utility function
based assessment
Confirm Concept Model Fit to Customer Cluster through Stages 1 & 2
Logistic regression filtering to identify salient
characteristics
For many products, it is difficult to find sales or market share data across companies that are associated with
specific consumer characteristics. Using logistic regression,
analysts can identify which features are preferred by various
consumer groups to better customize a given product. Kim
et al. (2010) applied logistic regression to associate preferences with consumer age, origin (i.e., Asian, American)
and gender; the method can also be applied to other factors (e.g., geographic locations or income levels). Filtering
through logistic regression provides odds ratios on how consumer preference responses differ from reference variables.
The results are then evaluated for statistical significance by
comparing the response probabilities. Using the regression
models, estimation is made of the relationship between one
or 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 market
shares and variation across gender, age and origin clusters
are identified, the results do not provide an aggregate utility
of a design. Here, utility is defined as a measure of satisfaction consumers receive from a product or service (Cleaver
2011). In stage 2, we develop MAUT models to rank order
the consumer preferences for mobile phone models using the
pre-selected significant mobile phone features determined
during stage 1. Using MAUT and accounting for uncertainty,
we evaluate the appropriateness of the mobile phone models
for all consumer groups in the aggregate (i.e., across all features in the product). MAUT eliminates the limitations found
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in prior applications, such as additive linear modeling (AHP),
accounting for all features rather than single ones (KE), and
the needed choice simulation (CA). Although we assess the
utility of actual models for our case study, conceptual models
can also be used.
Case study
To demonstrate the methodology, this study used our previously gathered data for 1028 mobile phone models (Kim et al.
2010; Kim and Okudan 2009a,b). Feature evaluation and
multiple regression analysis, using market share as the dependent variable, led to the identification of potential regression
models (see Table 1). From these, a selection was made using
Mallow’s Cp (lowest Cp value).
A survey was developed and administered to determine
consumer preferences associated with mobile phone characteristics. Survey questions, designed according to the historical data mining, are provided in Kim (2009). All features
found to be significant were included as survey questions.
In total, 527 participants (274 U.S citizens, 253 non-U.S
citizens of Asian origin) took part in the survey (Kim and
Okudan 2009b). Their ages ranged from 14 to 39. Results
were categorized in four age groups: 14–17, 18–22, 23–29,
and 30–39. The U.S. citizens included 142 males and 132
females. Of the non-U.S citizens, 125 were males and 128
were females, primarily Asians living in the U.S. and predominantly Korean. The participants spanned age, gender,
education level, and ethnicity categories.
Eleven questions were developed to identify user preferences for mobile phone factors, design characteristics, and
frequency of function use. Based on the results, consumer
preferences were analyzed using logistic regression. Then
x
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x
based on stage 1 activities (see Sect. “Historical data mining”
and “Logistic regression filtering to identify salient characteristics”), we determined the important design features for
consumers. Using this data, we identified salient consumer
preferences in order to match the most appropriate mobile
phone models for each of 16 different groups formed in a
matrix 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 ), and
form (k9 ), were found to impact consumer preferences significantly and in different ways across the groups.
In Sect. “Case study”, we demonstrate how the perceived
feature utility-based product family design can be used in
two ways: (1) studying market offerings to arrive at potential
models for a company to benchmark and further develop as a
product family, and (2) studying a company’s own products
to better target specific consumer groups.
x
x
x
x
93.7 87.5
85.1 78.1
11
7
9.3
x
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x
93.9 87.8
11
7.8 x
x
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x
x
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93.1 87.4
10
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93.5 88.2
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x
x
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91.7 85.9
10
6.1 x
x
9
6.1 x
x
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x
x
90.2 84.6
5.7
92.8 87.8
8
4.9 x
x
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x
Utility assessment of models in a market as a foundation
for a product family
9
R 2 (adj) Cp Weight Contacts Outgoing Shared DataSpeed Battery Talk DisplayPixels CameraPixels Block Clamshell 3G Camera MP3 IM Bluetooth
#Variables R 2
Table 1 Top 8 best-fitting models in terms of Cp
J Intell Manuf
After examining the market offerings of summer 2009, we
identified past and present mobile phone models satisfying
the consumer preferences for each consumer group. Models
matching the U.S. sample are provided in Fig. 2, and those
for the Asian sample are provided in Fig. 3.
Nine mobile phone attributes (determined in stage 1 as the
evaluative attribute set for fulfilling customer preferences)
were used for utility assessment. In this stage, single attribute
utility (SAU) functions to reflect consumer’s priorities using
the certainty equivalent concept were formulated according
to Keeney and Raiffa (1976). While the boundaries for attribute utility functions are ultimately defined by the designer
or decision analyst modeling the preferences, a utility scale
of 0–1 is often used. The most preferred attribute level will
return the best utility value of 1 (U Best = 1), while the least
preferred level will yield the worst, 0 (U Worst = 0)
Exponential SAU formulation, shown in Eq. (1), was used
in the current study as it requires the estimation of only one
parameter: risk tolerance (RT). The RT values for attributes
were assessed by determining the best and worst data values
and applying certainty equivalent (CE) analysis. The CE is
the value of an attribute for which the consumer is indifferent
between the CE and the probabilistic expected consequence.
The risk attitude of the designer can be modeled in utility
functions (Eqs. 1–3). For monotonic increasing functions, the
risk prone utility will have an expected consequence smaller
than the CE, while the risk averse utility indicates that the
expected consequence is higher than the CE. Accordingly, the
risk prone attitude reflects that the designer is confident of
achieving 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 lack
of it.
Ui (xi j ) = Ai − Bi ∗ e
Ai = ⎡
⎣e
Bi = ⎡
⎣e
e
−Min(xi j )
RTi
−Min(xi j )
RTi
−Min(xi j )
RTi
(1)
−Min(xi j )
RTi
−e
1
−e
⎤
−Max(xi j )
RTi
(2)
⎦
⎤
−Max(xi j )
RTi
(3)
⎦
where,
xi j : The jth value of attribute i
RTi = Risk tolerance for the attribute i
Min(xi j ) = Minimum value of the attribute i across all
alternatives
Max(xi j ) = Maximum value of the attribute i across all
alternatives
values increase, their utility values also increase. The
designer is risk averse for screen size (k3 ), camera (k4 ), battery life (k5 ), memory (k6 ), and transfer speed (k7 ). CE3 of
screen size is 2.2 inch, and RT 3 value is 0.3. RT 4 of camera
is calculated as 1.5, and its CE4 = 3.5 megapixels. With
CE5 = 375 min, battery life is determined as RT 5 = 60.
The risk tolerance of memory, RT 6 , is calculated as 120,
and CE6 indicates 140 mb. CE7 of transfer speed is 6.5 mbs,
and the corresponding is RT 7 = 3.0. In the same manner,
CE8 = 2.5, RT 8 = 1; and CE9 = 1.5, and RT 9 0.3 are
assessed.
We modeled the aggregation of the SAU functions to
reflect the overall view of the designer while taking into
account risk and uncertainty. To assess the fitness of mobile
phone model alternatives for each customer group with an
aggregated utility approach, the multiplicative form was
used. Applying the following relationship (Eq. 14), the multiplicative form may be shown to help solve decision problems
by ranking alternatives based on their utility scores.
n
1 (K ki Ui (xi j ) + 1) − 1
(13)
Uall (x) =
k
i=1
A consumer’s preference across each attribute range is represented by the mathematical expression of the SAU functions.
All attributes in this case study are monotonic. The SAU functions, developed using the above explained procedure for the
nine attributes, are as follows:
x1 j
U1 (x1 j ) = 1.007 + 12.265 ∗ e−( 400 )
U2 (x2 j ) = −0.685 − 354.900 ∗ e−(
U3 (x3 j ) = 1.157 + 651.154 ∗ e
x2 j
20
x3 j
−( 0.3
x4
(4)
)
j
U4 (x4 j ) = 1.093 + 2.600 ∗ e−( 1.5 )
U5 (x5 j ) = 1.089 + 161.685 ∗ e
−(
x6
j
x7
j
x5 j
60
U8 (x8 j ) = 1.019 + 2.769 ∗ e
3
x
−( 81 j
U9 (x9 j ) = 1.157 + 466.573 ∗ e−(
(5)
(6)
(8)
(9)
)
(10)
)
(11)
x9
j
0.5
(14)
i=1
where,
Uall (x): The total utility
xi j : The jth value of attribute i
U i (xi j ): The single attribute utility for attribute i
ki : Attribute-scaling parameter for attribute i
K : Normalizing constant
(7)
)
U6 (x6 j ) = 1.163 + 1.374 ∗ e−( 120 )
U7 (x7 j ) = 1.134 + 3.766 ∗ e−(
)
n
1+K =
(1 + K ki )
)
(12)
The SAU function of the attribute “weight” (k2 ) implies a
monotonically decreasing exponential function. For increasing values of weight (k2 ), its utility will decrease. This monotonically decreasing condition indicates that if value of the
attribute (k2 ) on the consequence axis is reduced, the utility improves. In this case, the designer is risk prone for the
weight attribute (k2 ). The certainty equivalent (CE2 ) is 116g,
and the corresponding (RT 2 ) value is 20.
The balance of the attributes can be modeled using
monotonically increasing functions. When their consequence
To measure the relative influence of each attribute, attribute trade-off parameter (ki ) values were determined. These
reflect the designer’s desire for trade-offs under uncertainty.
With respect to each consumer group, the importance of each
attribute was determined. For example, a risk assessment was
conducted for the group including U.S. females in the 30–39
age group. The rank ordering for importance of all attributes
was established as: Keypad (k8 ) = Form (k9 ) = Battery life
(k5 ) > Screen Size (k3 ) > Weight (k2 ) > Contact (k1 ) =
Transfer speed (k7 ) > Camera (k4 ) > Memory (k6 ). Based
upon consumer preferences as compiled by the survey data,
results show that the most significant attributes were ranked
as keypad, form, and battery life.
Beyond a rank order of importance, specific ki values
were determined through standard decision scenarios. First,
k8 was calculated, as it had the highest importance. Next, we
assumed that the consumer was confronted with the decision
scenario shown in Fig. 4, in which (∗ ) represents best value of
the attribute, and (◦ ) represents the worst value. This scenario
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Fig. 4 Keypad trade-off
decision scenario: a
probabilistic, versus b certain
situation
(a)
is a von Neumann–Morgenstern utility and is built upon a
multi-axiomatic formulation where the expected value of the
utility function can be presented as shown in Eq. (15). We
compared the indifference point by asking the consumer at
what probability value she would feel indifferent between a
probabilistic outcome of getting the best or the worst result,
and the certain outcome of getting the best in one attribute but
the worst in all others. Figure 4 shows this decision scenario
for 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, U Best is equal to 1. Equation (15) demonstrates
this relationship and calculates the value of k8 . When p8 value
is 0.5, the consumer finds the probabilistic case and the certain situation (shown on the right in Fig. 4) to be equal. This
indicates that keypad (k8 )’s scaling (trade-off) factor is 0.5.
Keypad’s alternative consequences are presented in quantitative means as follows: Full QWERTY keyboard & Touch
screen = 1, Full QWERTY Keyboard = 2, Touch screen =
3, QWERTY keypad & Touch screen = 4, QWERTY keypad
= 5.
E(u) = p8∗ 1 + (1 − p8 )∗ (0) = k∗8 Uall (X 1◦ , . . . , X 7◦ , X 8∗ , X 9◦ )
= k∗8 U8 (X 8∗ ) = k∗8 1 = k8
(15)
In Eq. 15, pi denotes the probability, and E(u) is the
expected value of the utility function. Simplification of
(Eq. 15) yields p8 = k8 = 0.5. For calculating the trade-off
parameter for form (k9 ), keypad k8 and form k9 are compared to arrive at an indifference point. Quantification of the
form consequences are done by assigning numerical values
to the alternatives: Flip (folder) = 1, Slider = 2, Block = 3,
and Clamshell = 4. As a result of the calculations shown in
Eqs. (16–18), k9 is found to be equal to k8 .
The utility comparison was completed to evaluate the
relationship between k5 and k3 . An indifference point
was achieved when the battery life = 420 min and screen
size = 2.5 inches. k5 was determined to be 0.5. Using the
methodology shown in Eqs. (16–18), k3 was determined to
be 0.471. Using a similar decision process, the remainder of
the ki ’s were determined as: k2 = 0.344, k1 = 0.1527, k7 =
0.1527, k4 = 0.121, and k6 = 0.049. Attribute trade-off
parameters reflect designer’s preferences in the decision scenarios. The multiplicative formulation was scaled between 0
123
(b)
and 1 using the scaling constant, K . Using Eq. 14, K value
was calculated as −0.97. Each age, origin, and gender group
was evaluated using the aggregated utility. The overall preference rankings for mobile phone alternatives shown in Figs. 2
and 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)
k1∗ u1 (1000) + k∗2 u2 (125) + k∗3 u3 (1.9) + k∗4 u4 (1.3)
+k∗5 u5 (300) + k∗6 u6 (20) + k∗7 u7 (3.6)
+k∗8 u8 (5.0) + k∗9 u9 (3.0)
= k∗1 u1 (1000) + k∗2 u2 (125) + k∗3 u3 (1.9) + k∗4 u4 (1.3)
+k∗5 u5 (300) + k∗6 u6 (20) + k∗7 u7 (3.6)
(17)
+k∗8 u8 (1.0) + k∗9 u9 (4.0)
∗
∗
(18)
k8 (1) + 0 = 0 + k9 (1)
According to the consumer preference rankings shown
Table 2, U.S. males and females in the 14–22 year category are most likely to select “Block or Flip” style as form
and “Full QWERTY keyboard” as keypad. On the other
hand, U.S. males and females in the 23–29 age group are
more likely to select “Block” as form and “Touch screen”
as keypad. For this group, large screen size and long battery
life are also critical factors in purchasing decisions. Asian
males and females between the ages of 14 and 22 prefer
“Slider” as form and “Full QWERTY keyboard and touch
screen” as keypad, and colorful designs influence their preferences. 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 preferred
form. High resolution camera and easy user interface are
also among the significant factors with high impact on consumers’ response.
Assessing the aggregated utility can help provide the
best trade-offs under uncertainty and help rank order mobile
phone alternatives. Our results suggest that for the 14–17 age
group, 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 parentheses). The highest ranking preferred models for the 18–22
age groups are behold t919 (0.98) and N9 (0.59). Among
the 23–29 age group, bl40 new chocolate (0.94), gm750
J Intell Manuf
Table 2 Aggregated utility ranking for each age and gender group
Models
Criteria
Contact
capacity
U1 (x1 )
Weight
U2 (x2 )
Screen
size
U3 (x3 )
Camera
U (x4 )
Battery
life
U3 (x5 )
Memory
U3 (x6 )
Transfer
speed
U3 (x7 )
Keypad
U3 (x8 )
Form
U3 (x9 )
U(x)
Rank
0.519
0
1
1
0.256
1
1
0
1
0.66
2
USA male 14–17
EnvTouch Vx11000
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
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
0.731
0
1
1
0
0.734
1
1
1
0.64
1
USA female 14–17
Asia male 14–17
Impression SGH A877
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
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
Asia female 14–17
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
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
USA female 18–22
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
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
Asia female18–22
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
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
USA female 23–29
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
123
J Intell Manuf
Table 2 continued
Models
Criteria
Contact
capacity
U1 (x1 )
Weight
U2 (x2 )
Screen
size
U3 (x3 )
Camera
U (x4 )
Battery
life
U3 (x5 )
Memory
U3 (x6 )
Transfer
speed
U3 (x7 )
Keypad
U3 (x8 )
Form
U3 (x9 )
U(x)
Rank
1
0.122
0.923
0.905
0
0.972
1
1
1
0.88
1
Asia female 23–29
LG GD900
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
HTC Touch HD
1
0
1
1
0.876
0
1
1
1
0.88
1
S800 Jet
HTC Hero
0
0
1
0.153
0
0.241
1
1
0
1
1
0.901
0
1
1
1
1
1
0.49
0.61
3
2
2
USA male 30–39
USA female 30–39
Nokia 6700 Classic
0
0.444
0.731
1
0
0.83
1
1
0
0.81
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 30–39
I8000 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 30–39
B7610 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
Easy In ter face
Lon g batter y life
Tou ch Scr een
Camer a
QWERTY
F lip/Slide
Lar ge Scr een
USA
Ma le
Fe m a le
39
29
22
17
14
14
17
22
29
39
AS IA
Fig. 5 The allocation of mobile phone features and potential product
family with 16 members
(0.99), sgha777 (0.57), and LG gd900 (0.88) models are popular preferences. For the 30–39 age group, HTC touch HD
(0.88), w510 (0.95) and s8003 jet (0.87) were ranked the
highest.
Figure 5 illustrates the preference of mobile phone features based on origin, gender, and age. The 16 family members are clustered to highlight the key phone features per
segment.
123
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 daunting task. To reduce the size of the product family, our proposed derivation for customer perceived utility data can
help determine a smaller mobile phone family design. If a
company has the means to develop only eight new products, clustering methods can be used to generate appropriate
options. For example, Fig. 6 depicts the features of eight
mobile phones that can satisfy consumer needs classified
J Intell Manuf
Asian buyers will not be so among U.S. buyers. The drawback to this categorization is that every phone will have 5 or
6 key features that will raise the price. Alternatively, Fig. 9
provides a clustering with 3 key features for the 14–17 year
old group, 4 key features for the 18–22 group, and 6 key
features for the remaining two groups.
Utility assessment of phone models to investigate product
family member fit to consumer group
Fig. 7 The product family with 8 members based on gender and age
In addition to investigating product models for their consumer group fit as a basis for developing a product family
design or simply for use as a benchmark, a company can also
assess the appropriateness of their existing offerings for specific consumer groups. This study used four mobile phone
models from Samsung (shown in Fig. 10) to investigate their
fit for U.S. females aged 30–39.
Table 3 presents utility ranking data achieved through the
implementation of MAUT on the nine attributes found to
significantly impact consumer preferences. Results show that
the overall utility ranking of the Samsung D880 and Samsung
i607 are nearly identical. Thus, one of these alternatives could
be removed from the market to simplify company operations
and consumer purchasing decisions. It may also be seen that
the utility values for the remaining two models are inferior in
comparison to the D880 and the i607; the implication is that
unless they satisfy other consumer group preferences, they
should be withdrawn from the market.
Fig. 8 The product family with 4 members based on gender and origin
Comparison of the presented method to conjoint
analysis
Fig. 9 The product family with 4 members based on age
only by age and origin. Figure 7 shows the same products overlaid on a gender axis. These two cluster options
cover all market segments by adding one or two design features.
In the same manner, distinguishing only four new products
for a product family is achievable. Two possible solutions are
presented in Figs. 8 and 9. The difference between them is the
clustering criteria. Figure 8 divides customers by origin and
gender. In this arrangement, mobile phones popular among
Conjoint analysis (CA), a well-established and widely used
methodology, requires surveyed consumers to discern their
preferences. In our proposed method, we use consumer surveys for analysis; hence, here we show the methodological
differences and implementation results between the two techniques. 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. The
same data set was utilized for the CA process as was for our
proposed method. Note that for neither method do we extend
our comparison to the validation; however, various methods
exist 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 this
attribute set, no specific method for the selection was
dictated. Gustafsson et al. (1999) recommended selecting
attributes that: (1) can impact the purchasing decisions,
123
J Intell Manuf
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
Contact
capacity
U1 (x1 j )
USA female 30–39
ki
0.153
RT
400
CE
2000
Samsung X820 0
Samsung E700 1
Samsung D880 0
Samsung i607
1
Table 4 Methodological
comparison
Weight
U2 (x2 j )
Screen
Size
U3 (x3 j )
Camera
U4 (x4 j )
Battery
Life
U5 (x5 j )
Memory
U6 (x6 j )
Transfer
Speed
U7 (x7 j )
Keypad
U8 (x8 j )
Form
U9 (x9 j )
0.344
20
116
1
0.377
0.008
0.129
0.471
0.3
2.2
0
0.539
0.898
0.898
0.121
1.5
3.5
0
0.741
0
1
0.500
60
375
0
0.616
0.884
1
0.049
120
140
0.457
0
0.330
0.357
0.1527
3
6.5
0
0
0.552
0.552
0.500
1
3
0
0.881
1
0.935
0.500
.5
3.5
0
0.731
1
0.381
Rank
0.36
0.89
0.95
0.94
4
3
1
2
Steps
Conjoint analysis
Historical data mining and MAUT
1
Identify a limited number of
attributes and their levels
2
Configure attributes and levels
into individual concepts
3
Conduct surveys to gauge
preferences
Using regression (or part-worth
models), attributes and levels
with impact on the preference
are identified
Validate the results both
internally and externally.
Using historical market share (or sales) information and real
product features (form and function) and employing
multiple regression identify significant attributes
Conduct surveys using the identified attributes from step 1
to gauge preferences for specific market segments (e.g.,
origin, age, gender)
Using logistic regression test the significance of varying
preferences across market segments
For each market segment, single attribute utility functions
and the aggregation function are formulated
4
5
(2) can be altered, and (3) can be used in benchmarking. For a comparison with our proposed method, the same
attributes were applied to the CA. Further, the comparison
includes only a subset of mobile phones shown in Fig. 10.
123
Uall (x)
Using actual designs to benchmark or potential concepts to
evaluate, utility calculations are used to compare designs,
and eventually fit designs to market segments (and
eliminate designs with limited utility)
Although our proposed methodology originated using 16
attributes (see Table 1), applying the historical data mining of actual market share information reduced them to 9
(Table 5).
J Intell Manuf
Table 5 Attributes and levels
No.
Attribute
(Notation)
No. of levels
Levels
1
2
3
4
5
6
7
8
Contact capacity (n)
Weight (grams)
Screen size (pixels)
Camera (pixels)
Battery life (h)
Memory (Mb)
Transfer speed (kbps)
Keypad (various types)
x1j
x2j
x3j
x4j
x 5j
x6j
x7j
x8 j
2
4
3
3
4
4
2
3
9
Form (Clamshell, Block, Slider)
x 9j
3
1000, 3000
66, 85, 105, 116
1.8, 2.0, 2.3
1.3, 3.0, 5.0
300, 350, 400, 450
20, 60, 64, 80
3.6, 5.6
1 (Full QWERTY keyboard &
Touch screen), 3 (QWERTY keypad
& Touch screen), 5 (QWERTY keypad)
1, 2, 3
Table 6 Concept configurations
Concepts
Contact capacity
Weight
Screen size
Camera
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 individual
concepts
Gustafsson et al. (1999) liken this step to a design of experiments applied to preference decisions, requiring the configuration of concept combinations for all attributes and their
levels. The case at hand would require configuring 20,736 different concepts. In the literature, fractional factorial designs
are recommended to reduce the number of configurations.
Thus, only 2 levels for each attribute are considered, yielding 128 configurations, and a 29−5 fractional design reducing
the configurations to 16 is adopted. Table 6 lists these concept combinations, where −1 and 1 represent the lowest and
highest levels of the attributes.
Battery life
Memory
Transfer speed
Keypad
Form
Step 3: Conduct surveys for preference assessment
At this stage, a rank ordering of concepts for each individual
participant is collected by reaching a sample of the target
population through appropriate means. This study uses previously collected survey data (from Kim and Okudan 2009b).
Step 4: Calculate part worth models
Based on the survey data, models are used to derive the
part worths. In general, linear functions are used where
the attributes are independent variables (Kohli and Krishnamurti 1989). Equation 19 presents a typical part worth
model, where y is the preference measure; x1 − xn are
123
J Intell Manuf
Table 7 Comparison of the rank ordering of concepts
Models
Concepts
Samsung X820
Samsung E700
Samsung D880
Samsung i607
Methods
Conjoint
analysis
(CA)
Presented
method
(PM)
Rank
agreement
0.79
0.86
0.89
0.92
0.36
0.89
0.95
0.94
Same across methods, 4
Same across methods, 3
2 for CA, 1 PM
1 for CA, 2 PM
attributes; and b, c,…, z are part worths. Such models can
be solved using the collected data, taking into account the
type of attributes (categorical vs. continuous) using regression, ANOVA or ANCOVA models; for this analysis, we used
regression. First individual attribute part worth models were
constructed; next all were aggregated assuming linearity. As
for the weights of individual attributes during aggregation,
normalized weights from the proposed method were used to
ensure a head-to-head comparison. Table 7 provides a comparison of the results under both methods.
y = a + bx1 + cx2 + · · · + zxn
(19)
As may be observed, there is a rank reversal for the top
two concepts. It is possible that because only a 29−5 design
was used involving only two levels for each attribute, there
may well be room to improve the predictive power of the
part worth models. However, the original task becomes computationally expensive as it requires perusing all possible
20,736 concept configurations. This problem is well recognized. Scholars have proposed the use of heuristics to efficiently search the feasible solution space utilizing individual
part worth models (e.g., Kohli and Krishnamurti 1989; Kohli
and Sukumar 1990).
Discussion and conclusion
By evaluating the most salient features of mobile phone forms
and functions, companies can determine how to best appropriate limited resources. In stage 1 of our analysis, historical
data mining, logistic regression filtering, and survey results
provide important features for use in the design decision making process. In stage 2, the SAU functions aid in evaluating
the “goodness” of alternatives for specific consumer group
preferences. As such, consumer preference rankings for the
determined alternatives of mobile phones can be established.
Based on the highest utility rankings, mobile phone models
most suited for each age, origin, and gender groups can be
identified.
123
As illustrated by our proposed methodology and analysis,
companies can determine the suitability of design alternatives for different customer groups. Product family designs
developed this way not only avoid over-design but also are
positioned for higher customer satisfaction. The elimination
of product models that render very close utilities can reduce
supply chain complexity and thereby provide higher competitiveness for a company. Moreover, the number of product family members can be adjusted in accordance with a
company’s marketing strategy, allowing flexibility in product design and better resource allocation.
Our proposed approach eliminates certain limitations of
other concept selection and marketing research methods. For
example, the use here of MAUT-based utility ranking does
not require variables to be mutually preferentially independent whereas AHP requires, and CA assumes mutual and
preferential independence; MAUT’s multiplicative aggregation form allows for trade-offs as shown in this case study.
Moreover, our approach eliminates the time-consuming pairwise judgments that both Pugh’s method and AHP require,
because once the SAU functions are constructed, the utility
evaluation of a design concept requires only identifying the
design features and placing them in the aggregate utility function. Perhaps more importantly, because of the significant
market share feature identification (stage 1), potential overestimation or underestimation results derived from surveybased preference estimation are largely eliminated. Finally,
because utility functions capture the probabilistic nature of
the preference decisions, consumer choice simulations necessary for CA are not required. In our comparison, we have
also shown that realistic problems with more than 10 attributes and varying levels can make implementing CA more
expensive.
In summary, our methodology demonstrates that the most
appropriate one from a set of mobile phone alternatives can
be recommended by matching consumer preferences and
attributes using historical data mining, logistic regression filtering, and consumer surveys, and then confirming the aggregated utility values of product preferences using MAUT. This
proposed method provides a reliable way of fitting design
decision-making alternatives to market segments in order to
satisfy consumer requirements and a technique to help predict which future mobile phone designs have a high likelihood of success in the marketplace—allowing the goal of
mass customization and personalization to be achieved. In
addition, our proposed analysis considers clustering market
segments based on gender, origin, and age, enabling companies to determine the size of a product family according to
its marketing strategy and financial situation.
Although the solo applications of MAUT and CA have
limitations in determining feasible trade-offs, our proposed
method avoids biases and inconsistencies among consumer
preferences and their ranges. Furthermore, it determines a set
J Intell Manuf
of desirable trade-offs and provides optimal alternatives by
assessing and prioritizing available information. Future work
will focus on: (1) confirming results through comparison to
real market data, and (2) applications of the methodology
to different product and consumer groups to test the general
applicability of the proposed method.
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