Aesthetics versus Function: Assessing Relative Customer ... · is between aesthetics and...

ISSN: 0971-1023 | NMIMS Management ReviewVolume XXIX April-May 2016

Abstract

Should a design manager invest more in improving

aesthetics (hedonic benefit) or function (utilitarian

benefit)? The answer depends upon the relative

consumer preference for hedonic attributes over

utilitarian attributes, or vice versa. Therefore, it is

important for designers to understand how customers

choose between competing products with different

levels of hedonic and utilitarian benefits. Choosing a

product from a choice set requires customers to make

tradeoffs between design attributes such as aesthetics

and functionality that make up the product

alternatives. This article introduces an experimental

design methodology to estimate tradeoff exchange

rate between any two product attributes. The

proposed method uses a discrete choice experiment,

combined with point allocation across the alternatives

in the choice set, to measure the preferences of the

respondents. This approach combined with Fieller's

theorem allows us to obtain information on the

respondent's most preferred product alternative as

well as information on his or her relative attribute

preferences.

Keywords: Conjoint analysis; Fieller's theorem;

Hedonic attributes; Utilitarian attributes; Pareto

optimal choice sets.

Aesthetics versus Function:Assessing Relative Customer Preference

Ravindra ChitturiPallavi Chitturi

Aesthetics versus Function: Assessing Relative Customer Preference

Dr. Gurumurthy Kalyanaram: Editor, Professor, and former Dean, Research, NMIMS University.

Dr. Gurumurthy Kalyanaram is currently advising MIT's Asia School of Business. He is a Research Professor at Tata

Institute of Social Sciences, and a practice professor at City University of New York. He is a distinguished academic who

has served as Dean, Director, Advisor and Professor globally including in The University of Texas, International

University of Japan, American University, and Amrita University. Additionally, he has lectured, taught and given

presentations at many universities and conferences all over the world, including Boston University, Jiang Xi University

of Finance, KIMEP, London School of Economics, Massachusetts Institute of Technology, St. Petersburgh State

University, and Vanderbilt University.

Dr. Kalyanaram is a highly-cited scholar, whose research covers management science, education and public policy,

economics and innovation. He serves as a management and policy consultant to many organizations.

Dr. Kalyanaram has been a distinguished scholar at the prestigious Woodrow Wilson Center for International Scholars,

and the East-European and Russian Research Center. He has served on several policy boards, including the Texas

Strategic Economic Development Commission.

Dr. Kalyanaram got his doctoral degree from Massachusetts Institute of Technology, and he has been recognized by

MIT with Harold Lobdell Award. He has also been recognized by several professional organizations for his contributions

to research. Currently, he serves as the President of global MIT South Asian Alumni Association.

Editorial from Dr. Gurumurthy KalyanaramISSN: 0971-1023 | NMIMS Management ReviewVolume XXIX April-May 201610 11

Changes

cities of India, and therefore street

Contents

mall farmers. Majority of the

farmers (82%) borrow less than

Rs 5 lakhs, and 18% borrow

between Rs 5 – 10 lakhs on a

per annum basis. Most farmers

(65.79%) ar

** p < .01 + Reliability coefficie

References

Table 23: The Results of Mann-Whitney U Test for DOWJONES Index Daily ReturnsDr. Rosy Kalra

Mr. Piyuesh Pandey

Aesthetics versus Function: Assessing Relative Customer PreferenceAesthetics versus Function: Assessing Relative Customer Preference

INTRODUCTION

Does aesthetics follow function—as is generally

accepted, or does function follow aesthetics? The

answer is not one or the other—it depends upon the

context. Customers struggle with this question every

day. Consumers purchase products to serve a useful

purpose (i.e., solve a problem or alleviate pain) as well

as to enhance experiential pleasure. Often the goals of

serving a useful purpose and increasing pleasure

conflict with each other. A diner may struggle between

fruit or a delicious chocolate cake that comes with

negative health consequences. Sometimes the choice

is between aesthetics and functionality—aesthetics

for pleasure and functionality to serve a useful

purpose. For example, car buyers often have to choose

between an optional safety feature such as 4- wheel

drive (serving a useful purpose) and a panoramic

sunroof (enhancing experiential pleasure). Prior work

involving tradeoffs between hedonic attributes that

increase positive feelings and utilitarian attributes that

reduce negative feelings has shown that depending on

the context, the relative value of hedonic versus

utilitarian attributes can be significantly higher or

lower. Dhar and Wertenbroch (2000) show that

consumers value utilitarian attributes relatively more

in acquisition decisions (buying) and value hedonic

attributes relatively more in forfeiture decisions

(selling). Chitturi (2015a) and Chitturi et al., (2007)

show that relative consumer preference changes as

per the principles of functional and hedonic

dominance in the context of consumer wants and

needs. Therefore, in order to enhance sales, it is

important for designers and marketers to be able to

assess relative consumer preference between any two

determinant attributes requiring tradeoffs at the time

of purchase; for example, assessing relative customer

preference between hedonic (aesthetics) and

utilitarian (function) attributes. In this research, we

develop an experimental design methodology to

collect data and apply Fieller's theorem to estimate

relative customer preference between product

aesthetics and product functionality. In the next

section, we review the literature on relative

preference assessment. This is followed by a

d i s c u s s i o n o f t h e p ro p o s e d m et h o d o l o g y,

experimental design, and conceptual model. The

paper concludes with data collection, data analysis,

discussion of results, and managerial implications.

Literature Review

Product design benefits can be broadly categorized

along hedonic and utilitarian dimensions (Batra and

Ahtola 1990; Dhar and Wertenbroch 2000; Okada

2005). In recent years, there has been an emphasis on

creating products that look aesthetically pleasing in

addition to being functionally satisfactory. Luce,

B ett m a n , a n d Pay n e ( 2 0 0 1 ) , a n d C h i tt u r i ,

Raghunathan, and Mahajan (2007) demonstrate how

product attribute tradeoffs in general, and those

involving hedonic and utilitarian attributes in

particular, influence consumer choice. However, prior

research does not help designers and marketers in

estimating which incremental investment would lead

to greater customer preference i .e. , would

improvement in hedonic attributes or improvement in

utilitarian attributes lead to greater customer

preference? Answering this research question is

critical to improving Return-on-Investment (ROI).

Therefore, there is a need to develop a methodology

that could help with the estimation of the tradeoff

exchange rate between hedonic and utilitarian

attributes.

Due to budget and time constraints, designers and

managers are often compelled to choose among

various attributes and associated benefits. If there is

no budget or time constraint, then perhaps the best

solution is to maximize on both hedonic and utilitarian

dimensions. However, more often than not, product

designers and managers are forced to make a choice

between selecting one attribute versus the other for a

variety of reasons. In such situations, we believe that

designers and marketing managers would make better

decisions if they are able to estimate the tradeoff

exchange rate between attributes based on relative

customer preference. In the following sections, we

show how Fieller's theorem can be used to find a

confidence interval for the tradeoff exchange rate c.

Conjoint analysis and discrete choice experiments are

among the most widely used methodologies in both

academia and industry for measuring and analyzing

the preferences or choices of respondents. The

contribution by Luce and Tukey (1964) is viewed as the

origin of conjoint analysis. In a conjoint task,

respondents sort, rank or rate a set of profiles. These

profiles are experimentally designed and are

described by multiple factors (attributes) and levels.

The results from conjoint analysis provide insights into

how respondents perceive and evaluate certain

attributes of interest.

Discrete choice experiments are a method related to

conjoint analysis and are often called choice based

conjoint. This method involves the design of profiles

on the basis of attributes specified at certain levels.

However, instead of ranking or rating all profiles, as is

usually done in classic conjoint studies, respondents

are asked to repeatedly choose one alternative from

different sets of profiles offered to them. Probabilistic

choice models such as multinomial logit or probit

models are applied to the choice data arising from such

experiments. An early article describing the

advantages of this approach for conjoint analysis was

by Louviere and Woodworth (1983).

Choice modeling and preference regression are

popular approaches used by researchers to

understand consumer preference formation (Ghose

and Lowengart 2012). Choice models are widely used

when internal and external cues can be identified by

consumers to choose between different types of

brands (Richardson et al., 1994). The preference

regression approach would indicate what internal cues

must be improved to enhance consumer preference

(Olson and Jacoby 1973). In this research, we propose

a method that identifies if the intrinsic attributes of a

product such as aesthetics and functionality

significantly influence consumer preference.

Furthermore, we use perato- optimal design of choice

options with points allocation instead of simple

ranking combined with Fieller's theorem to assess the

most preferred option as well as relative attribute

preference.

The advantage of discrete choice experiments over

classic conjoint analysis is that data collection involves

simulated decisions (or hypothetical choices)

providing a more realistic and simpler task for

respondents than rankings or ratings. In recent years,

this method has become increasingly popular as a way

to more directly study choice behavior (Batsell and

Louviere 1991). Examples of areas in which choice

experiments have been used include environmental

science (Adamowicz, Boxall, and Williams 1998),

health (Propper 1995), marketing (Kamakura and

Srivastava 1984; Johnson and Olberts 1991), tourism

(Haider and Ewing 1990), and design (Chitturi 2015a;

Chitturi 2009). A disadvantage of choice experiments is

that choices are less informative than corresponding

ratings and therefore require large numbers of

observations to obtain reliable estimates. However, if a

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

INTRODUCTION

Does aesthetics follow function—as is generally

accepted, or does function follow aesthetics? The

answer is not one or the other—it depends upon the

context. Customers struggle with this question every

day. Consumers purchase products to serve a useful

purpose (i.e., solve a problem or alleviate pain) as well

as to enhance experiential pleasure. Often the goals of

serving a useful purpose and increasing pleasure

conflict with each other. A diner may struggle between

fruit or a delicious chocolate cake that comes with

negative health consequences. Sometimes the choice

is between aesthetics and functionality—aesthetics

for pleasure and functionality to serve a useful

purpose. For example, car buyers often have to choose

between an optional safety feature such as 4- wheel

drive (serving a useful purpose) and a panoramic

sunroof (enhancing experiential pleasure). Prior work

involving tradeoffs between hedonic attributes that

increase positive feelings and utilitarian attributes that

reduce negative feelings has shown that depending on

the context, the relative value of hedonic versus

utilitarian attributes can be significantly higher or

lower. Dhar and Wertenbroch (2000) show that

consumers value utilitarian attributes relatively more

in acquisition decisions (buying) and value hedonic

attributes relatively more in forfeiture decisions

(selling). Chitturi (2015a) and Chitturi et al., (2007)

show that relative consumer preference changes as

per the principles of functional and hedonic

dominance in the context of consumer wants and

needs. Therefore, in order to enhance sales, it is

important for designers and marketers to be able to

assess relative consumer preference between any two

determinant attributes requiring tradeoffs at the time

of purchase; for example, assessing relative customer

preference between hedonic (aesthetics) and

utilitarian (function) attributes. In this research, we

develop an experimental design methodology to

collect data and apply Fieller's theorem to estimate

relative customer preference between product

aesthetics and product functionality. In the next

section, we review the literature on relative

preference assessment. This is followed by a

d i s c u s s i o n o f t h e p ro p o s e d m et h o d o l o g y,

experimental design, and conceptual model. The

paper concludes with data collection, data analysis,

discussion of results, and managerial implications.

Literature Review

Product design benefits can be broadly categorized

along hedonic and utilitarian dimensions (Batra and

Ahtola 1990; Dhar and Wertenbroch 2000; Okada

2005). In recent years, there has been an emphasis on

creating products that look aesthetically pleasing in

addition to being functionally satisfactory. Luce,

B ett m a n , a n d Pay n e ( 2 0 0 1 ) , a n d C h i tt u r i ,

Raghunathan, and Mahajan (2007) demonstrate how

product attribute tradeoffs in general, and those

involving hedonic and utilitarian attributes in

particular, influence consumer choice. However, prior

research does not help designers and marketers in

estimating which incremental investment would lead

to greater customer preference i .e. , would

improvement in hedonic attributes or improvement in

utilitarian attributes lead to greater customer

preference? Answering this research question is

critical to improving Return-on-Investment (ROI).

Therefore, there is a need to develop a methodology

that could help with the estimation of the tradeoff

exchange rate between hedonic and utilitarian

attributes.

Due to budget and time constraints, designers and

managers are often compelled to choose among

various attributes and associated benefits. If there is

no budget or time constraint, then perhaps the best

solution is to maximize on both hedonic and utilitarian

dimensions. However, more often than not, product

designers and managers are forced to make a choice

between selecting one attribute versus the other for a

variety of reasons. In such situations, we believe that

designers and marketing managers would make better

decisions if they are able to estimate the tradeoff

exchange rate between attributes based on relative

customer preference. In the following sections, we

show how Fieller's theorem can be used to find a

confidence interval for the tradeoff exchange rate c.

Conjoint analysis and discrete choice experiments are

among the most widely used methodologies in both

academia and industry for measuring and analyzing

the preferences or choices of respondents. The

contribution by Luce and Tukey (1964) is viewed as the

origin of conjoint analysis. In a conjoint task,

respondents sort, rank or rate a set of profiles. These

profiles are experimentally designed and are

described by multiple factors (attributes) and levels.

The results from conjoint analysis provide insights into

how respondents perceive and evaluate certain

attributes of interest.

Discrete choice experiments are a method related to

conjoint analysis and are often called choice based

conjoint. This method involves the design of profiles

on the basis of attributes specified at certain levels.

However, instead of ranking or rating all profiles, as is

usually done in classic conjoint studies, respondents

are asked to repeatedly choose one alternative from

different sets of profiles offered to them. Probabilistic

choice models such as multinomial logit or probit

models are applied to the choice data arising from such

experiments. An early article describing the

advantages of this approach for conjoint analysis was

by Louviere and Woodworth (1983).

Choice modeling and preference regression are

popular approaches used by researchers to

understand consumer preference formation (Ghose

and Lowengart 2012). Choice models are widely used

when internal and external cues can be identified by

consumers to choose between different types of

brands (Richardson et al., 1994). The preference

regression approach would indicate what internal cues

must be improved to enhance consumer preference

(Olson and Jacoby 1973). In this research, we propose

a method that identifies if the intrinsic attributes of a

product such as aesthetics and functionality

significantly influence consumer preference.

Furthermore, we use perato- optimal design of choice

options with points allocation instead of simple

ranking combined with Fieller's theorem to assess the

most preferred option as well as relative attribute

preference.

The advantage of discrete choice experiments over

classic conjoint analysis is that data collection involves

simulated decisions (or hypothetical choices)

providing a more realistic and simpler task for

respondents than rankings or ratings. In recent years,

this method has become increasingly popular as a way

to more directly study choice behavior (Batsell and

Louviere 1991). Examples of areas in which choice

experiments have been used include environmental

science (Adamowicz, Boxall, and Williams 1998),

health (Propper 1995), marketing (Kamakura and

Srivastava 1984; Johnson and Olberts 1991), tourism

(Haider and Ewing 1990), and design (Chitturi 2015a;

Chitturi 2009). A disadvantage of choice experiments is

that choices are less informative than corresponding

ratings and therefore require large numbers of

observations to obtain reliable estimates. However, if a

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

subject is asked to allocate (say) 100 points across the

alternatives in a choice set so as to reflect their

preferences, we can obtain information on his or her

most preferred alternative as well as information on

his or her relative preferences. Using this approach, we

combine the benefits of the discrete choice method

and the classical conjoint method to estimate the

tradeoff exchange rate between hedonic and

utilitarian design attributes.

Proposed Methodology: Pareto-optimal

Design

To study consumers' relative preference for the

hedonic and utilitarian attributes of a product, we

consider three levels of both the hedonic and

utilitarian dimension: low (-1), medium (0) and high

(1). If we present a choice set with the profiles {11, 10,

01}, the first profile dominates the others and the

respondent's choice is trivially made. Similarly, if we

present a choice set with the profiles {00, 10, 01}, the

first profile is dominated and will not be selected. For

this reason, choice sets given to a respondent should

have no dominating or dominated profiles. Choice sets

with no dominating or dominated profiles are called

Pareto optimal subsets. Wiley (1978) recognized the

need for Pareto optimal choice sets in choice

experiments. Raghavarao and Wiley (1998) and

Raghavarao and Zhang (2002) gave further results on

Pareto optimal sets in choice experiments.

Although experiments with choice sets of the same

size are more common, studies involving choice sets of

varying sizes have also been applied to study choice

behavior with pareto-optimal designs (Koelemeijer

and Oppewal 1999). Two Pareto optimal choice sets 2 for estimating the main effects of a 3 design are {-11,

00, 1-1} and {01, 10}. Selected respondents are

randomly divided into two groups. Each group is then

presented with one of the above two choice sets and

respondents are asked to allocate points across the

alternatives so as to reflect their preferences. Since the

above two choice sets have no dominating or

dominated profiles, different respondents will make

different allocations and we can gain an insight into the

importance of hedonic and utilitarian attributes.

This approach to decision making assumes that

consumers are able to determine which alternative in

a choice set provides the highest overall value. The

assumption of “preference ordering” implies that

consumers have a clear preference ranking between

any set of options such that it allows them to know

whether one alternative is at least as good as another.

In practice, subjects may arrive at decisions not with

clearly ranked preferences, but rather, as a result of

being forced to choose. Also, if a respondent does not

have a “most preferred” alternative, it may result in

indecision and a tendency to avoid commitment.

Therefore, respondents are also presented with a 'no-

choice' option within each choice set. Respondents

may choose the 'no-choice' option when none of the

alternatives appears attractive, or when the decision

maker expects to find better alternatives by continuing

to search (Dhar 1997). This leads to two Pareto-

optimal choice sets used in this research: (-11, 00, 1-1,

No-choice), and (01, 10, No-choice).

Conceptual Model and Estimation

Let y represent the points allocated to the jth profile in ij

the ith choice set. The response consists of the average

of the points allocated to any alternative from the ith

choice set. The average will be modeled using a ijy

main effects only model:

where is the general mean; , are unknown parameters, and , are defined as follows: Hb Ub ijHd ijUd

dH ij = 1, if the hedonic attribute is at its high level in the jth profile of the ith choice set

= 0, if the hedonic attribute is at its medium level in the jth profile of the ith choice set

= -1, if the hedonic attribute is at its low level in the jth profile of the ith choice set

dUij = 1, if the utilitarian attribute is at its high level in the jth profile of the ith choice set

= 0, if the utilitarian attribute is at its medium level in the jth profile of the ith choice set

= -1, if the utilitarian attribute is at its low level in the jth profile of the ith choice set

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

subject is asked to allocate (say) 100 points across the

alternatives in a choice set so as to reflect their

preferences, we can obtain information on his or her

most preferred alternative as well as information on

his or her relative preferences. Using this approach, we

combine the benefits of the discrete choice method

and the classical conjoint method to estimate the

tradeoff exchange rate between hedonic and

utilitarian design attributes.

Proposed Methodology: Pareto-optimal

Design

To study consumers' relative preference for the

hedonic and utilitarian attributes of a product, we

consider three levels of both the hedonic and

utilitarian dimension: low (-1), medium (0) and high

(1). If we present a choice set with the profiles {11, 10,

01}, the first profile dominates the others and the

respondent's choice is trivially made. Similarly, if we

present a choice set with the profiles {00, 10, 01}, the

first profile is dominated and will not be selected. For

this reason, choice sets given to a respondent should

have no dominating or dominated profiles. Choice sets

with no dominating or dominated profiles are called

Pareto optimal subsets. Wiley (1978) recognized the

need for Pareto optimal choice sets in choice

experiments. Raghavarao and Wiley (1998) and

Raghavarao and Zhang (2002) gave further results on

Pareto optimal sets in choice experiments.

Although experiments with choice sets of the same

size are more common, studies involving choice sets of

varying sizes have also been applied to study choice

behavior with pareto-optimal designs (Koelemeijer

and Oppewal 1999). Two Pareto optimal choice sets 2 for estimating the main effects of a 3 design are {-11,

00, 1-1} and {01, 10}. Selected respondents are

randomly divided into two groups. Each group is then

presented with one of the above two choice sets and

respondents are asked to allocate points across the

alternatives so as to reflect their preferences. Since the

above two choice sets have no dominating or

dominated profiles, different respondents will make

different allocations and we can gain an insight into the

importance of hedonic and utilitarian attributes.

This approach to decision making assumes that

consumers are able to determine which alternative in

a choice set provides the highest overall value. The

assumption of “preference ordering” implies that

consumers have a clear preference ranking between

any set of options such that it allows them to know

whether one alternative is at least as good as another.

In practice, subjects may arrive at decisions not with

clearly ranked preferences, but rather, as a result of

being forced to choose. Also, if a respondent does not

have a “most preferred” alternative, it may result in

indecision and a tendency to avoid commitment.

Therefore, respondents are also presented with a 'no-

choice' option within each choice set. Respondents

may choose the 'no-choice' option when none of the

alternatives appears attractive, or when the decision

maker expects to find better alternatives by continuing

to search (Dhar 1997). This leads to two Pareto-

optimal choice sets used in this research: (-11, 00, 1-1,

No-choice), and (01, 10, No-choice).

Conceptual Model and Estimation

Let y represent the points allocated to the jth profile in ij

the ith choice set. The response consists of the average

of the points allocated to any alternative from the ith

choice set. The average will be modeled using a ijy

main effects only model:

where is the general mean; , are unknown parameters, and , are defined as follows: Hb Ub ijHd ijUd

dH ij = 1, if the hedonic attribute is at its high level in the jth profile of the ith choice set

= 0, if the hedonic attribute is at its medium level in the jth profile of the ith choice set

= -1, if the hedonic attribute is at its low level in the jth profile of the ith choice set

dUij = 1, if the utilitarian attribute is at its high level in the jth profile of the ith choice set

= 0, if the utilitarian attribute is at its medium level in the jth profile of the ith choice set

= -1, if the utilitarian attribute is at its low level in the jth profile of the ith choice set

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

The product category of cell phones was selected for

this research. Cell phones were chosen because they

are purchased directly by college students, are widely

used, and are highly familiar. Also, they are of such

value that their purchase will require more

deliberation than the purchase of a relatively

inexpensive consumable. Moreover, cell phones can

be clearly defined which allows respondents to make

comparisons among competing profiles. The cell

phone stimuli (photos) were pre-tested for their visual

appeal.

Data Collection. Each task consisted of a Pareto

optimal choice set composed of three (or four)

alternatives which included the no-choice option. Each

alternative was described in terms of two groups of

attributes: aesthetics and functionality. The cell phone

task needed subjects to imagine that their cell phone

broke down and they want to purchase a new one. Cell

phones were described in terms of functional

attributes (network coverage, battery capacity, and

sound clarity) and aesthetic attributes (oyster flip

phone, phone color, program ring tune). For the choice

set composed of four alternatives {-11, 00, 1-1, no

choice}, henceforth called choice set 1, subjects were

instructed to distribute 100 points across the

alternatives so as to reflect their preferences. For the

choice set composed of three alternatives {01, 10, no

alternatives so as to reflect their preferences.

Photographs were included to portray the hedonic

attributes of the product.

The cover page of the survey stated that the researcher

was interested in understanding how consumers make

purchase decisions. It emphasized that there were no

right or wrong answers in the survey. Page two of the

survey introduced the cell phone purchase task. Page

three contained the cell phone task in a matrix format

with the alternatives (cell phones) representing

columns in the matrix and the attributes (aesthetics

and functionality) representing rows. A no-choice

option was also included as one alternative (see Figure

1). Page four of the survey asked respondents to

indicate the level of importance they would give to cell

phone attributes. Page four also included questions on

the respondent's demographic characteristics such as

age and gender. Data collected was captured on a

spreadsheet.

*To validate the style and attractiveness (hedonic) manipulation, a separate group of twenty subjects was asked to rate eleven cell phone photographs in terms of their attractiveness on a ten-point scale. Three cell phones with average ratings of 3.7, 5.7, and 8 were chosen to represent the low, medium and high levels of the hedonic dimension of a cell phone.

FIGURE 1EXAMPLE OF CELL PHONE TASK

Cell phone A Cell phone B Cell phone C No Cell Phone

Functionality

Network Coverage: 98%

Battery Capacity : 3 days

Sound Clarity : very high

Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : No

Functionality

Sound Clarity : high

Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : Yes

Functionality

Battery Capacity : 1 day

Sound Clarity :medium

Style & Attractiveness* Oyster flip phone : Yes Change phone colors: Yes Program ring tune : Yes

Choose not to

buy a cell phone

Points: __________ Points: ___________ Points: __________ Points:_________

*All brand symbols were removed from the cell phone pictures in the paper questionnaire.

CELL-PHONE A CELL-PHONE B CELL-PHONE C

Results. Forty two subjects completed the survey of which 21 were presented choice set 1 with four alternatives {-

11, 00, 1-1, no choice}, and 21 were presented choice set 2 with three alternatives {01, 10, no choice}. The average

points allocated to each alternative are given in Table 1 and Table 2.

Table 1. Average points allocated to each alternative in Choice Set 1.

Choice Set 1 (Number of Subjects = 21) ij

High functionality Low hedonics 32.00

Medium functionality Medium hedonics 29.90

Low functionality High hedonics 28.95

No choice

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

The product category of cell phones was selected for

this research. Cell phones were chosen because they

are purchased directly by college students, are widely

used, and are highly familiar. Also, they are of such

value that their purchase will require more

deliberation than the purchase of a relatively

inexpensive consumable. Moreover, cell phones can

be clearly defined which allows respondents to make

comparisons among competing profiles. The cell

phone stimuli (photos) were pre-tested for their visual

appeal.

Data Collection. Each task consisted of a Pareto

optimal choice set composed of three (or four)

alternatives which included the no-choice option. Each

alternative was described in terms of two groups of

attributes: aesthetics and functionality. The cell phone

task needed subjects to imagine that their cell phone

broke down and they want to purchase a new one. Cell

phones were described in terms of functional

attributes (network coverage, battery capacity, and

sound clarity) and aesthetic attributes (oyster flip

phone, phone color, program ring tune). For the choice

set composed of four alternatives {-11, 00, 1-1, no

alternatives so as to reflect their preferences. For the

choice set composed of three alternatives {01, 10, no

alternatives so as to reflect their preferences.

Photographs were included to portray the hedonic

attributes of the product.

The cover page of the survey stated that the researcher

was interested in understanding how consumers make

purchase decisions. It emphasized that there were no

right or wrong answers in the survey. Page two of the

survey introduced the cell phone purchase task. Page

three contained the cell phone task in a matrix format

with the alternatives (cell phones) representing

columns in the matrix and the attributes (aesthetics

and functionality) representing rows. A no-choice

option was also included as one alternative (see Figure

1). Page four of the survey asked respondents to

indicate the level of importance they would give to cell

phone attributes. Page four also included questions on

the respondent's demographic characteristics such as

age and gender. Data collected was captured on a

spreadsheet.

*To validate the style and attractiveness (hedonic) manipulation, a separate group of twenty subjects was asked to rate eleven cell phone photographs in terms of their attractiveness on a ten-point scale. Three cell phones with average ratings of 3.7, 5.7, and 8 were chosen to represent the low, medium and high levels of the hedonic dimension of a cell phone.

FIGURE 1EXAMPLE OF CELL PHONE TASK

Cell phone A Cell phone B Cell phone C No Cell Phone

Functionality

Sound Clarity : very high

Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : No

Functionality

Sound Clarity : high

Style & Attractiveness* Oyster flip phone : No Change phone colors: No Program ring tune : Yes

Functionality

Battery Capacity : 1 day

Sound Clarity :medium

Style & Attractiveness* Oyster flip phone : Yes Change phone colors: Yes Program ring tune : Yes

Choose not to

buy a cell phone

Points: __________ Points: ___________ Points: __________ Points:_________

*All brand symbols were removed from the cell phone pictures in the paper questionnaire.

CELL-PHONE A CELL-PHONE B CELL-PHONE C

Results. Forty two subjects completed the survey of which 21 were presented choice set 1 with four alternatives {-

11, 00, 1-1, no choice}, and 21 were presented choice set 2 with three alternatives {01, 10, no choice}. The average

points allocated to each alternative are given in Table 1 and Table 2.

Table 1. Average points allocated to each alternative in Choice Set 1.

Choice Set 1 (Number of Subjects = 21) ij

High functionality Low hedonics 32.00

Medium functionality Medium hedonics 29.90

Low functionality High hedonics 28.95

No choice

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References

Mr. Piyuesh Pandey

Table 2. Average points allocated to each alternative in Choice Set 2

Choice Set 2 (Number of Subjects = 21)

High functionality Medium hedonics 31.62

Medium functionality High hedonics 38.48

No choice 4.90

Based on the points allocated, the sample variance-

covariance matrices were calculated, and weighted

least squares was used to estimate the main effects of

functional and aesthetic benefits. Both the functional

effect (Z = 2.99, p = 0) and the aesthetic effect (Z = 2.84,

p = 0) are significant in this study. As predicted, both

the aesthetics and the functional dimensions of a cell

phone are salient and significantly influence customer

preference. It also shows that cell phones are not a

purely functional device and customers value both the

aesthetics and functional benefits offered by cell

phones.

We obtain the following estimates:

As stated above, both the functional effect (Z = 2.99, p

= 0) and the aesthetics effect (Z = 2.84, p = 0) are

significant. Substituting the above estimates in (1) we

get the following confidence interval for c: (0.220245,

4.441052).

GENERAL DISCUSSION

What is the role of different types and levels of

attributes in the formation of customer preference?

Does attribute tradeoff involving aesthetics and

functional attributes alter consumer preference for a

product? These are some of the questions that have

been explored in prior research. However, prior work

on consumer tradeoffs involving aesthetics and

functional attributes has shown that consumer

preference between aesthetics and function is

dictated by the principles of functional and hedonic

dominance (Chitturi 2015a; Chitturi 2015b; Chitturi et

al., 2007). For example, a designer would like to design

aesthetic and functional benefits into a product to

maximize consumer preference because consumer

preference between aesthetics and function varies

depending upon their minimum functional needs. On

the other hand, a marketing manager would like to

price a product to leverage greater willingness-to-pay

for aesthetic benefits over functional benefits based

on the principle of hedonic dominance (Chitturi et al.,

2007). Therefore, it is important for designers and

marketers to be able to assess relative preference

between hedonic versus utilitarian benefits offered by

the product. This article introduces an experimental

design methodology to estimate the tradeoff

exchange rate between any two product attributes.

The proposed method uses a discrete choice

experiment, combined with point allocation across the

alternatives in the choice set, to measure preferences

of the respondents. This approach combined with

Fieller's theorem allows us to obtain information on

the respondent's most preferred product alternative

as well as information on his or her relative attribute

preferences.

Why does Consumer Preference Alter between

Aesthetics Versus Function?

Consistent with the principle of functional

precedence, customers are more likely to choose a

more utilitarian product over a more hedonic product

(Chitturi, Raghunathan, and Mahajan 2007), unless

they are able to justify their desire for hedonic

consumption (Okada 2005). In the absence of a valid

justification, the negative feelings of guilt and anxiety

prevent them from choosing a more hedonic product

over a more utilitarian one (Chitturi, Raghunathan,

and Mahajan 2007). In addition, customers feel

uncertain about functional performance when they

contemplate choosing an aesthetically superior

product that is also functionally inferior. This

anticipatory feeling of anxiety results from concerns

about the ability of the functionally inferior product to

meet the minimum utilitarian needs of the customer

(Chitturi, Raghunathan, and Mahajan 2007).

Collectively, the feelings of guilt and anxiety

discourage customers from choosing a hedonically

superior product in the absence of a justification for

hedonic consumption (Okada 2005). Under such

circumstances, the utilitarian benefits offered by a

product have greater influence on customer

preference compared to hedonic benefits.

It has been shown that higher levels of functional

attributes lead to greater customer confidence in the

ability of the product to meet the customers' minimum

utilitarian needs. As the confidence associated with

the utilitarian performance of the product goes up, the

a s s o c i ate d a nx i et y co m e s d ow n ( C h i tt u r i ,

Raghunathan, and Mahajan 2007). Reduced anxiety

with utilitarian performance leads to greater customer

focus on hedonic benefits and lesser focus on

utilitarian benefits—i.e., customers give greater

importance to hedonic benefits over utilitarian

benefits. Furthermore, reduced anxiety and increased

confidence offer valid justification to give greater

importance to hedonic consumption due to its

improved self-expressive benefits as per the principle

of hedonic dominance (Chitturi et al., 2007)).

Managerial Implications

All products offer benefits that can be categorized into

two types—hedonic and utilitarian (Batra and Ahtola

1990; Dhar and Wertenbroch 2000; Okada 2005). As a

result, product designers need to be cognizant of the

attributes they design into a product and how these

attributes contribute to the hedonic and utilitarian

benefit dimensions. Depending upon the cost of

designing a set of attributes for a product, the

designers would like the product to be priced so as to

at least recover the cost of developing the product.

However, marketing managers have to price the

product based on the benefits it offers and the changes

in the relative consumer preference between hedonic

and functional attributes offered by the product. They

are looking to maximize sales and profit.

Understanding how customers choose between

competing products that offer different levels of

various attributes is critical to effective product design

decisions. In this article, we use a methodology that

combines a discrete choice approach with conjoint

analysis. This approach allows us to combine ranking

as well as rating information with small sample sizes.

The paper uses Fieller's theorem to estimate the

tradeoff exchange rate between the aesthetic and

functional benefits offered by the competing products

in a choice set. In this paper, we show how the

proposed discrete choice experimental design

combined with Fieller's theorem can estimate the

relative preference between any two determinant

attributes that influence purchase decisions. As an

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

Table 2. Average points allocated to each alternative in Choice Set 2

Choice Set 2 (Number of Subjects = 21)

High functionality Medium hedonics 31.62

Medium functionality High hedonics 38.48

No choice 4.90

Based on the points allocated, the sample variance-

covariance matrices were calculated, and weighted

least squares was used to estimate the main effects of

functional and aesthetic benefits. Both the functional

effect (Z = 2.99, p = 0) and the aesthetic effect (Z = 2.84,

p = 0) are significant in this study. As predicted, both

the aesthetics and the functional dimensions of a cell

phone are salient and significantly influence customer

preference. It also shows that cell phones are not a

purely functional device and customers value both the

aesthetics and functional benefits offered by cell

phones.

We obtain the following estimates:

As stated above, both the functional effect (Z = 2.99, p

= 0) and the aesthetics effect (Z = 2.84, p = 0) are

significant. Substituting the above estimates in (1) we

get the following confidence interval for c: (0.220245,

4.441052).

GENERAL DISCUSSION

What is the role of different types and levels of

attributes in the formation of customer preference?

Does attribute tradeoff involving aesthetics and

functional attributes alter consumer preference for a

product? These are some of the questions that have

been explored in prior research. However, prior work

on consumer tradeoffs involving aesthetics and

functional attributes has shown that consumer

preference between aesthetics and function is

dictated by the principles of functional and hedonic

dominance (Chitturi 2015a; Chitturi 2015b; Chitturi et

al., 2007). For example, a designer would like to design

aesthetic and functional benefits into a product to

maximize consumer preference because consumer

preference between aesthetics and function varies

depending upon their minimum functional needs. On

the other hand, a marketing manager would like to

price a product to leverage greater willingness-to-pay

for aesthetic benefits over functional benefits based

on the principle of hedonic dominance (Chitturi et al.,

2007). Therefore, it is important for designers and

marketers to be able to assess relative preference

between hedonic versus utilitarian benefits offered by

the product. This article introduces an experimental

design methodology to estimate the tradeoff

exchange rate between any two product attributes.

The proposed method uses a discrete choice

experiment, combined with point allocation across the

alternatives in the choice set, to measure preferences

of the respondents. This approach combined with

Fieller's theorem allows us to obtain information on

the respondent's most preferred product alternative

as well as information on his or her relative attribute

preferences.

Why does Consumer Preference Alter between

Aesthetics Versus Function?

Consistent with the principle of functional

precedence, customers are more likely to choose a

more utilitarian product over a more hedonic product

(Chitturi, Raghunathan, and Mahajan 2007), unless

they are able to justify their desire for hedonic

consumption (Okada 2005). In the absence of a valid

justification, the negative feelings of guilt and anxiety

prevent them from choosing a more hedonic product

over a more utilitarian one (Chitturi, Raghunathan,

and Mahajan 2007). In addition, customers feel

uncertain about functional performance when they

contemplate choosing an aesthetically superior

product that is also functionally inferior. This

anticipatory feeling of anxiety results from concerns

about the ability of the functionally inferior product to

meet the minimum utilitarian needs of the customer

(Chitturi, Raghunathan, and Mahajan 2007).

Collectively, the feelings of guilt and anxiety

discourage customers from choosing a hedonically

superior product in the absence of a justification for

hedonic consumption (Okada 2005). Under such

circumstances, the utilitarian benefits offered by a

product have greater influence on customer

preference compared to hedonic benefits.

It has been shown that higher levels of functional

attributes lead to greater customer confidence in the

ability of the product to meet the customers' minimum

utilitarian needs. As the confidence associated with

the utilitarian performance of the product goes up, the

a s s o c i ate d a nx i et y co m e s d ow n ( C h i tt u r i ,

Raghunathan, and Mahajan 2007). Reduced anxiety

with utilitarian performance leads to greater customer

focus on hedonic benefits and lesser focus on

utilitarian benefits—i.e., customers give greater

importance to hedonic benefits over utilitarian

benefits. Furthermore, reduced anxiety and increased

confidence offer valid justification to give greater

importance to hedonic consumption due to its

improved self-expressive benefits as per the principle

of hedonic dominance (Chitturi et al., 2007)).

Managerial Implications

All products offer benefits that can be categorized into

two types—hedonic and utilitarian (Batra and Ahtola

1990; Dhar and Wertenbroch 2000; Okada 2005). As a

result, product designers need to be cognizant of the

attributes they design into a product and how these

attributes contribute to the hedonic and utilitarian

benefit dimensions. Depending upon the cost of

designing a set of attributes for a product, the

designers would like the product to be priced so as to

at least recover the cost of developing the product.

However, marketing managers have to price the

product based on the benefits it offers and the changes

in the relative consumer preference between hedonic

and functional attributes offered by the product. They

are looking to maximize sales and profit.

Understanding how customers choose between

competing products that offer different levels of

various attributes is critical to effective product design

decisions. In this article, we use a methodology that

combines a discrete choice approach with conjoint

analysis. This approach allows us to combine ranking

as well as rating information with small sample sizes.

The paper uses Fieller's theorem to estimate the

tradeoff exchange rate between the aesthetic and

functional benefits offered by the competing products

in a choice set. In this paper, we show how the

proposed discrete choice experimental design

combined with Fieller's theorem can estimate the

relative preference between any two determinant

attributes that influence purchase decisions. As an

Changes

Contents

(65.79%) ar

References

Mr. Piyuesh Pandey

example, we test the proposed methodology to

estimate relative customer preference between

aesthetics and functionality for the category of cell

phones. The same methodology can be replicated for

any other two attributes of a product in any product

category.

How does the knowledge of relative customer

preference between any two attributes under

consideration by design engineers and managers

benefit the product development team? A clear and

precise understanding of relative customer preference

between determinant attributes is critical to

optimizing product design leading to a greater

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