UNIVERSITY CATHOLIC OF LOUVAIN
Final report for the course
of research methodology:
“Conjoint Analysis Methodology”
Authors:
González Calleros Juan Manuel
Stanciule scu Adrian
Responsable:
Christophe Benavent
Introduction
When we have to deal with problems related on how to explain the
preferences of a consumer between different objects with the help of attributes
and characteristics that describe the object [6], the solution is the use of conjoint
analysis or experimental choice analysis.
Conjoint analysis represents a widely applied methodology for measuring
and analyzing consumer preferences. It was born from psychometric works that
tries to reveal the behavior of a consumer in different dimensions such as social,
individual and as an economic agent [6], the term Conjoint analysis were coined
by Green and Srinivasan to refer to a number of paradigms in psychology,
economics and marketing that are concerned with the quantitative description of
consumer preferences or value trade-offs [2].
Different authors classify the conjoint analysis as an explicative method
because it explains an ordinal variable (preference) through several nominal
independent variables (attributes) [6]. The ambition of the method is to explain
and describe the behavior of the consumer [6]. It is useful to understand why
consumers prefer or choose certain products or services, perhaps in new
conditions [3].
1. Objectives
Conjoint analysis has two major objectives: (i) to determine the contribution
of predictor variables (attribute levels) to consumer overall preferences, and (ii)
to establish a valid model of consumer judgments useful in predicting consumer
acceptance of any combination of attributes, even those not originally evaluated
by consumers [4], [6].
In order to achieve these objectives, coefficients called utilities (or partworths) are estimated for the various attribute levels making upon the
alternatives of interest by decomposing the measured overall preferences for
product profiles into these part-worth utilities according to some a priori defined
combination rule which specifies how subjects are assumed to integrate those
separate part-worth utilities to arrive at an overall preference or choice. The
profile utility is an overall utility or ‘worth’ of a profile calculated by combining
all utilities of attributes levels defined in that profile, according to the assumed
combination rule [3].
2. Theoretical presentation
The name "Conjoint analysis" implies the study of the joint effects.
Marketing applications are studying the joint effects of multiple product
attributes on product choice. There are a couple of alternative Conjoint Analysis
Methodologies. Some of them are named below:
Stimulus Construction
Data Collection
Model Type
Measurement Scale
Estimation Procedure
Simulation Analysis
Two Factor at a Time; Full Factorial
Design; Fractional Factorial Design
Two Factor at a Time Tradeoff
Analysis;
Full
Profile
Concept
Evaluation
Compensatory
and
NonCompensatory Models Part Worth
Function; Vector Model; Mixed Model;
Ideal Point Model;
Rating Scale; Paired Comparisons;
Constant Sum; Rank Order
Metric and Non-Metric Regression;
MONANOVA; PREFMAP; LINMAP;
Nonmetric
Tradeoff;
Multiple
Regression; LOGIT; PROBIT; Hybrid;
TOBIT; Discrete Choice
Maximum Utility; Average Utility
(Bradley-Terry-Luce); LOGIT; PROBIT
3.1. Conjoint measurement
Consumers or decision makers often think in terms of concepts, objects or
solutions, rather than relative numerical values. Conjoint measurement (as
distinguished from conjoint analysis) permits the use of rank or rating data,
when evaluating pairs of attributes or attribute profiles (rather than single
attributes). Based on this rank or rating input, the conjoint measurement
procedures are applied to identify a mathematical function of the m brand
attributes, which:
(1) is interval scaled (produces a set of interval scaled output),
(2) best corresponds to the set of subjective evaluations (ordinal judgments) of
the brand alternatives made by the respondent, and
(3) is either a categorical or polynomial function in the attributes for the rank
order data.
The conjoint measurement model assumes that:
(1) the set of objects being evaluated is at least weakly ordered (may contain
ties),
(2) each object evaluated may be represented by an additive combination of
separate utilities existing for the individual attribute levels, and
(3) the derived evaluation model is interval scaled and comes as close as
possible to recovering the original rank order [non- metric] or rating [metric]
input data.
The power of conjoint measurement to convert non-metric input into interval
scaled output has resulted in many methodological advancements, including
multidimensional scaling
and
conjoint analysis. Several different
implementations of conjoint measurement are evidenced in conjoint analysis
algorithms and computer programs. These implementations reflect both
algorithmic differences and alternative approaches to data collection and
measurement. The most noticeable are categorical conjoint measurement,
monotone ANOVA models, Ordinary Least Squares (OLS) regression methods
and linear programming methods. In the following, it will be discussed only the
OLS regression approach that offers a simple, yet robust method of deriving
alternative forms of respondent utilities (part-worth, vector, or ideal point
models). The attractiveness of the OLS model is in part a result of the ability to
scale respondent choices using rating scales, rather than rankings. The ability to
implement designs having larger numbers of attributes and levels (through
fractional factorial designs) has made this methodology the defacto standard for
conjoint analysis. The objective of OLS conjoint analysis is to produce a set of
additive part-worth utilities (vector or ideal point utilities may also be estimated)
that identify each respondent's preference for each level of a set of product
attributes. In application, the OLS model solve s for utilities using a dummy
matrix of independent variables. Each independent variable indicates the
presence or absence of a particular attribute level. The dependent variable is the
respondent's evaluation of one of the profiles described by the independent
variables. This model is expressed:
where:
• B = the beta weights estimated in the regression
• X = the matrix of dummy values identifying the levels of the factorial
design, and
• y = the ranking or rating evaluations of the respondent.
The first step in the analysis is to develop either a full or fractional factorial
design. The use of fractional factorial designs permits the estimation of a
parameter for the main effect of each attribute included in the analysis. The
utilities are additive.
3.2 The measurement of preference
The measurement of preference is an established part of consumer research
that is based in expectancy value models of attitude theory and measurement. In
conjoint analysis, there is an examination of preference for a set of brands or
other choice alternatives that are described by an inventory of attributes. The
domain of preference research in conjoint analysis is both broad and multifaceted. It extends to such diverse issues as how many attributes should be
measured; the influence of the number of attribute levels; the appropriateness of
measuring choice behavior rather than rating or ranking choice alternatives; the
advantages of constructing individualized rather than generic attribute sets. A
second line of preference research has focused on the appropriateness of
alternate scaling methodologies for the measuring of preferences. Modeling the
decision process itself is a third major area of research, and includes the
appropriateness of alternative decision models (compensatory , conjunctive,
disjunctive, Elimination by Aspect, etc.) that may be used either singularly or in
combination to predict preference; the form of the utility preference model
estimated for a given attribute (part worth, linear, or ideal point); and the type of
simulation models used to estimate choice preferences.
3.3. Preference models
Utility preference models are the mathematical formulations that define the
utility levels for each of the attributes. In practice, the attributes are modeled as
either a piecewise linear (part-worth), linear, or curvilinear function.
The Part-Worth Model is the simplest of the utility estimation models. This
model represents attribute utilities by a piecewise linear curve. This curve is
formed by a set of straight lines that connect the point estimates of the utilities
for the attribute levels. The part-worth function is defined as:
where:
•
•
•
sj = preference for the stimulus object at level j
fp = the function representing the part worth of each of the j different
levels of the stimulus object, Yjp for the pth attribute.
Yjp = the level of the pth attribute for the jth stimulus object.
The part-worth model reflects a utility function that defines a different
utility (part-worth) value for each of the j levels of a given attribute. Because of
design considerations, most conjoint studies constrain the number of levels to be
less than 5, though in actuality, the number of levels varies from 2 to 9 or more.
The implications of specifying a given preference model (part-worth,
linear, or ideal point) extend beyond the actual shape of the preference curve
being modeled. Each preference model requires that a different number of
parameters be estimated. The part worth model requires that each level of an
attribute be defined by a dummy variable distinct column within the design
matrix. As would be expected, a total of j-1 dummy variables are required to
estimate j levels.
The Vector Model is represented by a single linear function that assumes
preference will increase as the quantity of attribute p increases (preference
decreases if the function is negative). Preference for the jth attribute is defined as:
where:
• Wp = the individual's weights assigned to each of the p attributes. One
weight is derived for each attribute
• Yjp = Level of the pth attribute for the jth Stimulus
The vector model for the attribute with four levels would appear as a straight
line, with the levels on the line. The vector model requires that a single
parameter be estimated for each variable treated as a vector. In contrast to the
part-worth model, the vector model defines the attribute variable not as a series
of dummy variables, but as a single linear variable where the values are the
measured values or levels associated with the attribute.
The Ideal Point Model. The ideal point function is operationalized as a
curvilinear function that defines an optimum or ideal amount of an attribute. The
ideal point model is appropriate for many qualitative attributes, such as those
associated with taste or smell. The ideal point model establishes an inverse
relationship between preferences and the weighted distance (dj2) between the
location of the jth stimulus and the individual's ideal point, Xp. The ideal point
model is expressed as:
where:
• Yjp = Level of the jth Stimulus with respect to the individual's ideal point,
• Xp = the Individual's ideal point, p
• Wp = the individual's weights assigned to each of the p attributes. One
weight is derived for each attribute
• Yjp = level of the pth attribute for the jth Stimulus
The ideal-point model for the attribute with three levels would appear as a
curve with the center of the curve higher than either end. The highest point being
the ideal quantity of the attribute.
Mathematically, the implication of specifying each of the models ultimately
extends to the number of parameters that must be estimated. The vector model
treats the variable Yjp as a continuous (interval scaled) variable, such that only t
parameters (j=1,...,t) must be estimated. For the ideal point model, 2t parameters
must be estimated (Wp and Xp), and for the part-worth model, (q-1)t parameters
must be estimated, where q is specified to the number of levels for each of the t
attributes.
3. Analysis Procedure
Developing a conjoint analysis involves the following steps:
1. Selecting the attributes (characteristics) that are assumed to influence the
choice behavior of interest, is the first task to do in conjoint analysis. Defining the
attributes and the population, creates the framework of the study [6].
2. The attributes that are selected need to be classified, this means, to determine
the point of reference (if there is one) for the evaluation of the attribute, and the
unit of measure for the attribute [6]. This measure could be numerical or
categorical levels [3].
3. The next step is to cho ose the form in which the combinations of attributes are
to be presented to respondents [7], according to some statistical design. Thus, the
researched products are described in terms of profiles. Each profile is a
combination of attribute levels for the selected attributes [3].
4. Data Collection plays an important role as it defines how the information will
be collected. For this purpose five methods are described in [6] :
• Trade-off tables, the respondent have to choose among defined responses.
• Techniques of complete profiles, each respondent is exposed to a group of
combinations of attributes, and after selecting the attributes combination
(concept) it is demanded to qualify the choice.
• Composition techniques, is a model to measure the importance of the
attributes and its evaluation.
• Hybrid techniques each respondent receive both an evaluation task auto explained, and a small ensemble of profiles to evaluate.
• Analysis conjoint adaptable, the respondent is exposed to a profile and
each time they choose among two options.
5. Before searching a method to reduce the data collected, it is mandatory to
analyze the attributes and determine if they constitute a:
• determinant set of attributes, if they are important and differentiating,
• independence between them, measuring the correlation of the attributes
(how related are they), expecting a low value to continue,
• manipulability: the attributes must be understandable and identifiable.
6. Finally, select the technique to be used to analyze the collected data. The partworth model is one of the simpler models used to express the utilities of the
various attributes [7], [8].
7. The data is processed by statistical software written specially for conjoint
analysis which comes in a variety of forms. For example, Sawtooth Software
offers a suite of conjoint software packages: Adaptative Conjoint Analysis (ACA),
Traditional Full-Profile Conjoint Analysis, Choice-based Conjoint (CBC) and
Partial-Profile CBC [9]. These packages are discussed in the next section.
4.1 Selecting a package to process the data
The application of conjoint analysis techniques implies the study of the joint
effects of multiple products attributes on product preferences or product choice.
In a conjoint study, the object of study is the utility, which is the preference of an
individual for an object among some other objects; a cause of it will be more
useful, because it will have a bigger usage value [6]. It is know, that the user does
not have the choice of an ideal object, the domain is limited in size, so the user
decision is limited to a set of objects. If it is supposed that the individual chooses
based on the attributes of the object, each attribute represent a partial utility for
the object [6]. So, the utility of an object, for a consumer (its usability value), is in
function of its partial utilities.
It is paradoxical that many new developments in the conjoint analysis field have
made the methods better than ever, but they have also made it more difficult to
choose among them. Many differentiating limitations, which earlier caused
researchers to reject one conjoint analysis in the favor of another, have been
overcome, thus blurring the lines between the unique capabilities of the
approaches.
Sawtooth Software is one of the most important developers of analytical and
survey interviewing systems which are some of the most widely used in the
world. They have designed software packages that bring unique advantages to
different research situations [9].
4.1.1 Adapatative Conjoint Analysis (ACA)
ACA’s main advantage is its ability to measure more attributes than is advisable
with traditional full-profile conjoint. In ACA, respondents do not evaluate all
attributes at the same time, which helps solve the problem of “ information
overload” that is present in many full-profiles study. The idea is that respondents
cannot effectively process more than about 6 attributes at a time in full profile
context. ACA can include up to 30 attributes, although typ ical ACA projects
involve about 8 to 15 attributes. With 6 or fewer attributes, ACA’s results are
similar to the full-profile approach, though there is a little compelling reason to
use ACA in these situations.
In terms of limitations, the foremost is that ACA must be computer –administer.
The interview adapts to respondents’ previous answer, which cannot be done via
paper-and-pencil. Like most traditional conjoint approaches, ACA is main-effects
model. This means that part-worth utilities for attributes are measured in an “ all
else equal” context, without the inclusion of attribute interaction. ACA also
exhibits another limitation with respect to pricing studies: when price is included
as just one of many variables, its importance is likely to be understand , and the
degree of understanding increases as the number of attributes studied increases.
ACA is a hybrid approach, combing stated evaluations about attributes and
levels with conjoint pair wise comparison. The first part of the interview
approximates the self-explicated approach. Respondents rank (or rate) attribute
levels, and then assign a weight (importance) to each attribute. Using the
information from the self-explicated section, ACA then presents trade-off
questions. Two or more products are shown, and the respondents indicate which
is preferred, using a relative rating scale. The product combinations are tailored
to each respondent, to ensure that each is relevant and meaningfully challenging.
Each of the products is displayed in partial-profile, meaning that only a subset
(usually two or three) of the attributes is shown for any given question. Because
of the self-explicated introductory section, the adaptive nature of the
questionnaire, and the ratings-based conjoint tradeoffs, ACA is able to stabilize
estimates of respondent’s preferences for more attributes using smaller sample
sizes than the other conjoint methods. ACA does well for modeling highinvolvement purchases, where respondents focus on each of a number of
product attributes before making a carefully considered decision. Purchases for
low involvement product categories described on only a few attributes along
with pricing research studies are better handled using another method.
4.1.2 Traditional Full-Profile Conjoint Analysis (CVA)
Full-profile conjoint has been a mainstay of the conjoint community for a long
time now. The full profile approach is useful for measuring up to about six
attributes. That number varies from project to project depending on the length of
the attribute level text, the respondents’ familiarity with the category, and
whether attributes are shown as prototypes or pictures. CVA is designed for
paper-and-pencil studies, whereas ACA must be administered via computer.
CVA can also be used for computerized interviews in a CAPI or Internet survey.
CVA calculates a set of part worth for each individual, using traditional fullprofile card-sort (either ratings or ranked) or pair wise ratings. Up to 30
attributes with 15 levels can be measured. Through the use of compound
attributes, in a limited way CVA can measure interactions between attributes
such as brand and price. Compound attributes are created by including all
combinations of levels from two or more attributes. For example, two attributes
each with two levels can be combined into a single four-level attribute. However,
interactions can only be measured in a limited sense with this approach.
Interactions between attributes with more than 2 or 3 levels each are better
measured using CBC.
CVA can design pair wise conjoint questionnaires, or single concept (card-sort)
designs. Showing one product at a time encourages respondents to evaluate
products individually, rather than in direct comparison with a competitive set of
products. It focuses more on probing the acceptability of an offering rather than
the differences between competitive products. If the comparative task is desired,
CVA’s pair wise approach may be used. Another alternative is to conduct a cardsort exercise. However, respondents view one product per card, in the process of
evaluating the deck they usually compare them side-by-side and in sets. Because
respondents see the products in full-profile (all attributes at once), respondents
tend to use simplification strategies if faced with too much information to
process. Respondents may key on two or three salient attributes and largely
ignore the others.
4.1.3 Choice-based Conjoint (CBC)
CBC interviews closely mimic the purchase process for products in competitive
contexts. Instead of rating or ranking product concepts, respondents are shown a
set of products on the screen (in full-profiles) and asked to indicate which one
they would purchase:
If you were shopping for a credit card, and these were your only options,
which would you choose?
VISA
Mastercard
Discover
NONE: I would
$40 annual fee
$20 annual fee
No annual fee
defer my purchase
10% interest rate
18% interest rate
14% interest rate
$2,000 credit limit $5,000 credit limit $1,000 credit limit
As in the real world, respondents can decline to purchase in a CBC interview by
choosing “ None.” If the aim of conjoint research is to predict product or service
choices, it seems natural to use data resulting from choices.
Choice tasks are more immediate and concrete than abstract rating or ranking
tasks. They seem to ask respondents how they would choose now, given a set of
potential offerings. Choice tasks show sets of products, and therefore mimic
buying behavior in competitive contexts. Because choice -based questions show
sets of products in full-profile, they encourage even more respondent
simplification than traditional full-profile questions. Attributes that are
important will get even greater emphasis (importance), and less important
factors will receive less emphasis relative to CVA or ACA.
CBC can measure up to 30 attributes with 15 levels each. In contrast to either
ACA or CVA (which automatically provide respondent level part worth
preference scores), CBC results have traditionally been analyzed at the aggregate,
or group level. But with the availability of latent class and hierarchical Bayes
(HB) estimation methods, group-based and individual-level analyses are now
accessible and practical.
There are a number of ways to analyze choice results:
• Aggregate Choice Analysis: was the first and generally only form of
analyzing CBC results, prior to advances in algorithms and the availability
of faster computers. It was argued that aggregate analysis could permit
estimation of subtle interaction effects (between brand and price), due to
its ability to leverage a great deal of data across respondents. For most
commercial applications, respondents often cannot provide enough
information with even ratings- or sorting -based approaches to measure
interactions at the individual level. While this advantage seems to favor
aggregate analysis from choice data, academics and practitioners have
argued that consumers have unique preferences and idiosyncrasies, and
that aggregate-level models, which assume homogeneity, cannot be as
accurate as individual-level models.
• Latent Class Analysis: addresses respondent heterogeneity in choice data.
Instead of developing a single set of part worths to represent all
respondents (aggregate analysis), Latent Class simultaneously detects
homogeneous respondent segments and calculates segment-level part
worths. If the market is truly segmented, Latent Class can reveal much
about market structure (including group membership for respondents)
and improve the predictability over aggregate choice models. Subtle
interactions also can be modeled in Latent Class, which seems to offer a
compromise position, leveraging the benefits of aggregate estimation
while recognizing market heterogeneity.
• ICE (Individual Choice Estimation): is largely an interesting historical
side note now, though some researchers still enthusiastically use this
technique for individual-level estimation from CBC data. Before
computers became fast enough to permit estimation of moderate to large
CBC datasets under hierarchical Bayes (HB) within a reasonable amount
of time, ICE offered a compelling speed advantage. Computers are now
fast enough that this is usually not an issue, and in general, the industry
has embraced HB.
HB (Hierarchical Bayes Estimation) HB offers a very powerful way for
“ borrowing” information from every respondent in the data set to improve the
accuracy and stability of each individual’s part worths. It has consistently proven
successful in reducing the IIA (Independence from Irrelevant Alternatives)
problem and in improving the predictive validity of both individual -level
models and market simulation share results. HB estimation can employ either
main effects or models that additionally include interaction terms. But,
researchers are finding that many (if not most) of the interaction effects that were
discovered using aggregate CBC analysis were actually due to unrecognized
heterogeneity.
4.1.4 Partial-Profile CBC
Many researchers that favor choice-based conjoint rather than ratings-based
approaches have looked for ways to increase the number of attributes that can
effectively be measured using CBC. One solution that is gaining momentum over
the last few years is partial-profile CBC. With partial-profile CBC, each choice
question includes a subset of the total number of attributes being studied. These
attributes are randomly rotated into the tasks, so across all tasks in the survey
each respondent typically considers all attributes and levels.
The problem with partial-profile CBC is that the data are spread quite thin,
because each task has many attribute omissions, and the response is still the less
informative (though more natural) 0/ 1 choice. As a result, partial-profile CBC
requires larger sample sizes to stabilize results (relative to ACA), and individuallevel estimation under HB doesn’t always produce stable individual level part
worths. Despite these shortcomings, some researchers who used to use ACA for
studying many attributes have shifted to partial-profile choice. The individuallevel parameters have less stability than with ACA, but if the main goal is
achieving accurate market simulations (and large enough samples are used),
some researchers are willing to give up the individual-level stability. Partialprofile CBC results tend to reflect greater discrimination between most and least
important attributes relative to ACA, though it is not a given that this means
improved accuracy in predicting real world choices.
4.1.5 Choosing a method
The problem that arises is which method should be chosen. The answer to this
question is: the chosen method is the one that adequately reflects how buyers
make decision in the actual marketplace. This includes not only the competitive
context, but the way in which products are described (text), displayed (multimedia or physical prototypes), and considered.
If there is a need to study many attributes, ACA or possibly partial-profile CBC
should be considered. If there is a need to include attribute interactions in your
models, you should probably use CBC. In many cases, survey populations don't
have access to PCs, and it may be to o expensive to bring PCs to them, or viceversa. If the study must be administered paper-and-pencil, consider using CVA
or CBC with its paper-and-pencil module. In case of dealing with relatively small
sample sizes, you should be cautious about using CBC, unless respondents are
able to answer more than the usual number of choice tasks. ACA and CVA are
able to stabilize estimates using relatively smaller samples than CBC.
Many researchers include more than one conjoint method in their surveys. For
example, some studies need to measure a dozen or more attributes, and also
require brand-specific demand curves. ACA followed by CBC can solve this
problem within a single questionnaire. ACA would include all the attributes,
while brand, price, and a few key performance variables would be studied using
CBC. ACA provides the product design and feature importance model, while
CBC provides price sensitivity estimates for each brand and a powerful pricing
simulator.
5. Application Domain
Conjoint analysis has been use in the following domains:
• virtual reality studies [3][5]
• analysis of the segmentation of the markets, optimization of the product
based on constraints in its production, simulation of market strategies,
causes of bad diffusion of French daily, evaluation of services of an
enterprise, extension of product’s lines, the study of preference of groups
of objects, the study of consumer behavior, study of decisions in
individual medium [6]
• international comparison of consumers of different countries [1]
• marketing new product/ concept identification, pricing, advertising and
distribution , to family decision making; tourism, tax analysis; time
management; direct foreign; and medicine [8].
Bibliography
[1] Böcker. F. Hausruckinger, G, Herker, A,"Pays d'origine et qualités écologiques
comme caractéristique des biens de consommation durables : Une analyse comparative du
comportement des consommateurs français et allemands". Recherche et applications en
Marketing Vol VI N°391 21-30, 1991.
[2] Dijkstra, J. , W.A.H. Roelen and H.J.P. Timmermans (1996). “ Conjoint
Measurement In Virtual Environments: A Framework” . Timmermans, H.J.P.
(ed.) 1996. DDSS 96. Proceedings 3rd Design and Decision Support Systems in
Architecture and Urban Planning Conference, Vol. 1: Architecture Pro ceedings,
Eindhoven University of Technology, Eindhoven, pp. 132-142.
[3] Dijkstra, J. and H.J.P. Timmermans, "Conjoint Analysis and Virtual Reality - a
Review" In: Proceedings of 4-rd Design & Decision Support Systems in
Architecture & Urban Planning Conference, 1998.
[4] Hair, J.F., R.E. Anderson, R.L. Tatham and W.C. Black, “Conjoint analysis, in
Multivariate data Analysis”, Prentice Hall, Englewood Cliffs NJ, pp. 556-599, 1995.
[5] Ifedayo A., Adelola, Abdur Rahman, Sara L., Cox, “ Conjoint analysis in
virtual reality based powered wheelchair rehabilitation of children with
disabilities” , Center On Disabilities Technology And Persons With Disabilities
Conference 2003, available on
http:/ / www.csun.edu/ cod/ conf/ 2003/ proceedings/ 263.htm.
[6] Jean-Claude Liquet et Christophe Benavent, “ L’analyse et ses applications en
marketing” , Equipe de recherche en Marketing – IAE de Lille, available on
http:/ / christophe.benavent.free.fr/ IMG/ pdf/ conjointe.pdf
[7] QuickMBA: Knowledge to Power Your Business, Marketing, “ Conjoint
analysis” ,
available
on
http:/ / www.quickmba.com/ marketing/ research/ conjoint/
[8] Scott M. Smith, “Conjoint Analysis Tutorial” , Professor of Marketing
Marriott School of Management Brigham Young University, available on
http:/ / marketing.byu.edu/ htmlpages/ tutorials/ conjoint.htm.
[9] Bryan Ome, “ Which conjoint method should I use?” , Sawtooth Software,
Research papers, 2003.