review articles conjoint last decade

Author(s) Title Year Journal Topic

2010

2004

2002

2004

2004

Rao V.R. 2004

Rubin D. 2004 The author proposes the use of posterior predictive checks in evaluation of the models

Preference model

Papies D., Eggers F. and Wlömert N.

Music for free? How free ad-funded downloadsaffect consumer choice

Journal of the Academy Marketing Science

Consumer Preferences for online music, market segmentation and willingness-to-pay

Part-worth function, 6 attributes, 4-5 levels,

Murphy W., Dacin P. and Ford N.

Sales Contest Effectiveness: An Examination of Sales Contest Design Preferencesof Field Sales Forces


Understanding salespersons' preferences for various contest designs

Part-worth function 5 attributes, 2-3 levels,

Hofstede F.T., Kim Y. and Wedel M.

Bayesian Prediction in Hybrid ConjointAnalysis

Journal of Marketing Research

The heterogeneity in self-stated and estimated part-worths in hybrid conjoint studies and their relationship. The authors reanalyze the data collected by Srinivasan and Park (1997), who studied MBA students who were choosing among job offers

Part-worth, 2 attributes at 2 levels, 3attributes at3 levels, 2 at 4 levels, and 1 at 6 levels

Bradlow E.T., Hu Y. and Ho T.

A Learning-Based Model for Imputing Missing Levels in Partial Conjoint Profiles


The problem of incomplete attribute information and the potential pitfalls of imputing missing attribute levels

Vector model, 6 attributes, 2 levels

Alba J., Cooke A.D.J.

When Absence Begets Inference in ConjointAnalysis


A comment on themodel developed by Bradlow, Hu, and Ho

The authors ask for solutions to attribute density in conjoint research such as: to understand whether and how respondents deal with missing information, to reduce density before the implementation of the conjoint procedure and the need for cross-disciplinary work

Comments on Conjoint Analysis with PartialProfiles



The authors ask for: other ways to conceptualize the problem, managerial aspects of the BHH procedure , the role of price in solving the problem and a data collection procedure for partial profiles. One or two of the previous profiles need to be complete (not partial). Issues regrding BHH’s assumption of the independence of counts when multiple attributes are missing

Design and Modeling in Conjoint Analysiswith Partial Profiles



2004

2005

2000

2004

2002


Modeling Behavioral Regularities ofConsumer Learning in Conjoint Analysis


Note of the authors proposing several extensions of their own model of consumer learning in conjoint analysis

They present a clarification of the original model, propose an integration of several new imputation rules add new measurement metrics for pattern matching, and draw a roadmap for further real-world tests. The authors also discuss general modeling challenges when researchers want to mathematically define and integrate behavioral regularities into traditional quantitative domains. They conclude by suggesting several critical success factors for modeling behavioral regularities in marketing. The authors encourage collaborations not only between behavioral researchers and modelers within the marketing domain itself but also across different fields (e.g., economics, operations, psychology, sociology, statistics) as a way to undertake challenging and important research in marketing in the future

Ding M., Grewal J. and Liechty J.

Incentive-Aligned Conjoint Analysis


The authors propose the incentive-aligned conjoint analysis instead of hypothetical studies. Field experiment in a Chinese restaurant (S1) and a second study that uses snacks as the context (S2)

S1: part-worth model, 8 attributes, 2-4 levels S2: 4 attributes, 2-5 levels

Haaijer R., Kamakura W. and

Wedel M.

Response Latencies in the Analysis ofConjoint Choice Experiments


The authors use filteredresponse latencies to scale the covariance matrix of a multinomial probit model and show that this leads to better model fit and holdout predictions. They used data from a technological product, collected bySawtooth Systems.

Vector model, 6 attributes, 2-6 levels: brand (6), speed (4), technological

type (6), digitizing

option (3), facsimile (2), and price (4)

Toubia O., Hauser J. and Simester D.

Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis


The authors propose a CBC question-design methodthat adapts questions by using the previous answers from that respondent (i.e., individual adaptation)

Path worth, 8 attributes, 2-4

levels

Andrews R., Ansari A. and Currim I.

Hierarchical Bayes Versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery


The authors reanalyze the idea of Vriens, Wedel, and Wilms (1996) who founded that finite mixture (FM) conjoint models had the best overall performance of nine conjoint segmentationmethods in terms of fit, prediction, and parameter recovery.

Path worth, 6 product

attributes at 3 levels each

2004

2001

2007

Moore W.L. 2004

2009

Urban G.L., Hauser J.R.

“Listening In” to Find and Explore NewCombinations of Customer Needs

Journal of Marketing

The authors did a dynamic presentation of complementary methods for understanding customer-needs combinations: truck example

The authors describe and evaluate the methodologieswith formal analysis, Monte Carlo simulation (calibrated on real data), and a “proof-of-concept” applicationin the pickup-truck category (more than 1000 Web-based respondents). The application identified opportunities fornew truck platforms worth approximately $2.4 billion–$3.2 billion and $1 billion–$2 billion, respectively. The authors compared complementary methods for understanding customer-needs combinations: Qualitative and ethnographic, Tailored Interviews, Segmentation, Interest or intent, AIO studies, Conjoint analyses, Truck clinics, Listening in. See complete article for more details.

Wathne K. H., Biong H. and Heide

J.B.

Choice of Supplier in Embedded Markets: Relationship and Marketing Program Effects


The authors develop a conceptual framework of how relationship and marketing variables influence choice of supplier and test the framework empirically in the context of business-to-business services.

Vector model, 4 factors each with 2 levels

Hennig-Thurau T., Henning V., Sattler H., Eggers F., and

Houston M. B.

The Last Picture Show? Timing andOrder of Movie Distribution Channels


The authors discuss different scenarios and their implications for movie studios and other industry players, and barriers for theimplementation of the revenue-maximizing distribution models are critically reflected.

Part-worth, 5 attributes, 2-5

levels

A cross-validity comparison of rating-based and choice-based conjoint analysis models

International Journal of

Research in Marketing

The paper compares OLS, hierarchical Bayes (HB), and latent segment, rating-based conjoint models to HB and latent segment choice-based conjoint models.

Part-worth, 7 attributes, 3

levels

Dong S., Ding M. and Huber J.

A simple mechanism to incentive-align conjoint experiments



The authors propose an alternative mechanism to incentive-align conjoint based on inferred rank order for situations where conjoint practitioners have more than one version of real products

Part-worth, 7attributes, each with 3

levels

2007

2004

2010

Eggers F., Sattler H. 2009

2008

Baumgartner B., Steiner W.J.

Are consumers heterogeneous in their preferences for odd and even prices?Findings from a choice-based conjoint study



The authors analyze consumers' preferences for 9-ending versus 0-endingprices at the individual level. Two product categories: chocolate drinks and notebooks

part-worth, 2 attributes, 5 and 3 levels

Otter T., Tuchler R., and Frqhwirth-

Schnatter S.

Capturing consumer heterogeneity in metric conjoint analysisusing Bayesian mixture models



A comparison of the random coefficients model (RCM) and the latent class model (LCM) using simulated data illustrates that the RCM dominates the LCM if the underlying distribution is strictly continuous. Application to the mineral water market


Decker R., Trusov M.

Estimating aggregate consumer preferences from online product reviews



The authors are trying to find the answer to the question: how to turn the available plentitude of individual consumer opinions into aggregate consumerpreferences? Product review data from the mobile phone market

part-worth, 23 attributes, 2-4

levels

Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels



The authors introduce hybrid individualized two-level choice-based conjoint (HIT-CBC), which combinesself-explicated preference measurement (SE) with choice-based conjoint analysis (CBC). The authors tested HIT-CBC in an empirical study pertaining to European flights

part-worth, CBC: 3

attributes, 3,5 levels, The HIT-

CBC reduces the number of levels at two: the best and

worst level, the authors started the empirical study with 6

attributes, 3-6 levels

Vermeulen B., Goos P. and

Vandebroek M.

Models and optimal designs for conjoint choice experiments including ano-choice option



The improvement of realily of an experimental conjoint analysis by using a no-choice option in a choice set

The authors developed optimal designs for the no-choice multinomial logit model, the extended no-choice multinomial logit model, and the nested no-choice multinomial logit model using the D-optimality criterion and the modified Fedorov algorithm and compare these optimal designs with a reference design, which is constructed while ignoring the no-choice option, in terms of estimation and prediction accuracy. They conclude that taking into account the no-choice option when designing a no-choice experiment only has a marginal effect on the estimation and prediction accuracy as long as the model, used for estimation, matches the data-generating model

2009

2003

Kim T., Lee H. 2009

2007

2001

Wuyts S., Verhoef P.C., and Prins R.

Partner selection in B2B information service markets



The first research which combines conjoint analysis with a between-subjectsexperimental design to test the effect of contingency factors. Experiment on factors influencing the choice of a research market company.

Linear, six attributes of

two levels each,

Andrews R., Currim I.

Retention of latent segments in regression-based marketing models



This study investigates via simulation the performance of seven segmentretention criteria used with finite mixture regression models for normal data

The study shows that one criterion, Akaike’s Information Criterion (AIC) with a per-parameter penalty factor of 3 (AIC3), is clearly the best criterion to use across a wide variety of model specifications and data configurations, having the highest success rate and producing very low parameter bias. See complete article for more details

External validity of marketsegmentation methodsA study of buyers of prestige cosmetic brands

European Journal of Marketing

The article compares and validates the results of two clustering methods for the segmentation of the market for prestige cosmetics in Korea

Taking into account the existance of this segmentation methods: automatic interaction detection and its multivariate variant; canonical analysis; factor analysis; cluster analysis; regressionanalysis; discriminant analysis; multidimensional scaling; conjoint analysis and componential segmentation, the authors reach the conclusion that traditional K-means clustering fails to produce segments that could have been useful in practice, whereas the innovative alternative of mixture regression modelling generats segments that have clear marketing strategy potential

Sichtmann C., Stingel S.

Limit conjoint analysis andVickrey auction as methods to elicit consumers’ willingness-to-payAn empirical comparison


This paper aims to analyze the differences in WTP elicited by conjoint analysis (LCA) and Vickrey auctions (VA) methods and their validity in high and low involvement situations.

Part-worth, 3 attributes, 3,2,3

levels

Jaeger S. R., Hedderley D. and

MacFie H. J. H.

Methodological issues in conjoint analysis: a case study


The authors did a choice-based conjoint study for measuring the consumer preferences for pre-packed apple selection packs. They also discuss the differences between psyhical prototype stimuli and realistic pictorial presentation and the need of prior training and warm-up of the respondents


levels

Davies G., Brito E. 2004

2010

2007

2003

2007

2005

Price and quality competition between brands and own brandsA value systems perspective


Conjoint analysis is used to measure the quality of the competing products by comparing the ratings given by consumers for the edible products and available chemical analysis for detergents


levels

Creusen M., Veryzer R. and Schoormans J.

Product value importance and consumer preference for visual complexity and symmetry


This paper therefore, seeks to assess how preference for visual complexity and symmetry depends on the type of product value that is important to people

Vector, 2 attribute, 2

levels, preferred level of each visual

design principle (high or low)

Silayoi P., Speece M

The importance of packaging attributes: a conjoint analysis approach


The paper aims to investigate the need for information regarding the consumer psychology for developing packages


levels

Liechty J.C., Fong D. and DeSarbo W.

Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis

Marketing science

the structure underlying preferences can change during the administration of repeated measurements (e.g., conjoint analysis) and data collection because of effects from learning, fatigue, boredom, and so on

The authors propose a new class of hierarchical dynamic Bayesian models applied to simulated conjoint data and explore the performance of these new dynamic models, incorporating individual-level heterogeneity across a number of possible types of dynamic effects demonstrating the derived benefits versus static models. The authors also introduce the idea of an unbiased dynamic estimate, and demonstrate that using a counterbalanced design is important from an estimation perspective when parameter dynamics are present. See complete article for more details

Liu Q., Otter T. and Allenby G.

Investigating Endogeneity Bias in Conjoint Models

Marketing science

The authors re-examine the endogeneity bias identified by Hauser and Toubia (HT), and explain its presence using traditional econometric methods

The authors reach the conclusion that the likelihood principle is implicit to the Bayesian approach to statistics where the posterior distribution is derived from the prior distribution and the likelihood. Bayesian analysis conditions on the data to draw inferences about unobservable parameters in the analysis. In a conjoint analysis, it provides an answer to the question "Given the data at hand, what do I know about the part-worths?" Their view is that the answer to this question is more managerially relevant than the corresponding frequentist question concerning performance of an estimator across multiple datasets. See complete article for more details

Hauser J., Toubia O.

The Impact of Utility Balance and Endogeneity inConjoint Analysis

Marketing science

The authors use formal models, simulations, and empirical data to suggest that adaptive metricutility balance leads to partworth estimates that are relatively biased—smaller partworths are upwardly biased relative to larger partworths.

The biases and inefficiencies are real and in the direction predicted. The authors provide stylized models and more general explanations with which to understand and isolate the cause of these phenomena. Furthermore, empirically, they find no evidence that metric utilitybalanced questions reduce response error. Contrary to common wisdom, orthogonality (efficiency) in metric questions appears to be a more important goal than utility balance. See complete article for more details.

2005

2008

2008

2007

2003

Evgeniou T., Boussios C. and

Zacharia G.Generalized Robust Conjoint Estimation

Marketing science

They propose a method based on computationallyefficient optimization techniques. They compare their method with standard logistic regression, hierarchical Bayes, and the polyhedral methods using standard, widely used simulation data

They reach the conclusion that their approach significantly outperforms both the method of Toubia et al. (2004) and standard logistic regression; is less sensitive to noise, high response error; is relatively weaker when data from an orthogonal design are used. (this limitation indicates that it may be important to combine the proposed method with a method similar in spirit for designing questionnaires) it's a simple method for handling heterogeneity lead to promising results with performance often similar to that of HB and estimates the interaction coefficients significantly better than all other methods

Gilbride T., Lenk P., and Brazell J.

Market Share Constraints and the Loss Functionin Choice Based Conjoint Analysis

Marketing science

This paper presents a Bayesian decisiontheoretic approach to incorporating base case market shares into conjoint analysis via the lossfunction. Simulateddata for both the multinomial logit and correlated probit discrete choice models.

MNL: 1 attribute, 4

levels, CBC: 20binary

attributes and the price

De Bruyn A., Liechty J., Huizingh

E. and Lilien G.

Offering Online Recommendations withMinimum Customer Input Through Conjoint-Based Decision Aids

Marketing science

The authors compare compare three algorithms—cluster classification, Bayesian treed regression, and stepwise componential regression—to develop an optimal sequence of questions and predict online visitors’ preferences

Part-worth, 5 attributes, 2,3

levels

Toubia O., Hauser J. and Garcia R.

Probabilistic Polyhedral Methods forAdaptive Choice-Based Conjoint Analysis:Theory and Application

Marketing science

Polyhedral methods for choice-based conjoint analysis. the authors tested the following four question-selectionmethods:orthogonal design; aggregate customization; deterministic polyhedral; probabilistic polyhedra.Wine industry

Part-worth, 5 features at 4 levels each

Toubia O., Simester D. and

Hauser J.Fast Polyhedral Adaptive Conjoint Estimation

Marketing science

They propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming.

The method uses centrality con-cepts and ellipsoid shape approximations. The authors tested the method using a series of Monte Carlo simula-tions. The findings confirm that the polyhedral algorithm is particularly suited to contexts where re-searchers are limited to asking relatively few questions compared to the number of parameters. By isolating the impact of the question design component, they found that the relative accuracy of the method is due, at least in part, to the design of the questions. Their simulations suggest that hybrid polyhedral ques-tion-selection methods could be used to enhance existing estimation methods. See complete article for more details

Data collection method Stimulus set construction

Choice

Scheffe tests

The author proposes the use of posterior predictive checks in evaluation of the models

Stimulus presentation

Measurement scale dependent var.

Estimation method

Full profile, 2 540 respondents Random sampling, 3 stimuli and a no-choice-option

Verbal description

Multinomial logit

Full profile, 796 respondents Fractional factorial design with SPSS ORTHOPLAN, 16 full profiles

Verbal description

Rank order, 1 (the most preferred)to 16 (the least preferred)

Full profile, 108 MBA students Fractional factorial design, 18 profiles, as well as 6 holdout profiles

Verbal description

Self-explicated and rating scores. The model estimates a set of scaling constants for each respondent

They developt a finite mixture regression model for full profileconjoint

The model assumes that consumers learnand update after each stimulus (partial profile) about the pattern underlying the product attributes, their levels, and the correlations between them. Experiment: Full profile 130 undergraduate students

20 digitalcamera profiles, 4 as holdouts for validation

Verbal description. The learning based-model was based on a experiment composed of two phases: learning(prior) and rating.

Rating scale, 0–9 Likert scale, choice

Hierarchical Bayesianapproach to account for heterogeneity



Choice

choice HB and AC

choice


Full profile, S1:108 undergraduate and graduate students, S2: 59 senior undergraduate students

Fractional factorial design. S1: 3 groups of 12 choicesets. Each choice set had 3 profiles (Chinese meals) anda “none of the above” option. The restaurant served the meal theychose. S2: 27 conjoint tasks, 30 unique snack combos for the holdout task

Physical products

Rating 1–7 “agree–disagree” scale, choice

Insamplehit rate and log-marginal probability

Full profile, 200 respondentsRandom sampling, 20 individualized choice sets with 3 alternatives and a one no choice

Verbal description

They develop a multinomial probit (MNP)

model

Full profile, 354 Web-based respondents

Before respondentsanswered the stated-choice questions, they revieweddetailed descriptions of the levels of each feature and couldaccess the descriptions at any time by clicking the feature’slogo. 4 sets with 8 features

Verbal description

Full profile, 150 consumersEach data set contains the

evaluations of 150 consumerson either 18 or 27 profiles (Factor 6). See complete article for more details.

Verbal description

Finite mixture, HB models

16 cards

choice

Choice


Full profile, 114 customer accounts, 37 key account

managersVerbal

descriptionRating scale 1

to 16

they used two ordinary least

squares regression

models

Full profile, 1770 consumers Random sampling, seven choice sets and a “no consumption” option

Verbal and pictorial

description Hierarchical

Bayes routine

S1: Full profile, (88) S2: Full profile (89

respondents)

S1: Fractional factorial design, 16 profiles, as well as 6 holdout profiles,

S2: 16choice sets, 1 which included 2

automobiles as wellas the option to continue to shop

Verbal description

Rating scale, 0–10 scale and second study choice

hierarchical Bayesian

multinomiallogit model

Full profile S1: 41 and S2: 44 respondents

S1: 36 profiles produced by SAS experimental design were divided

into 12 sets with 3profiles for each conjoint choice set, S2: 19 options plus the option of no

purchase

Verbal description

Hierarchical Bayesian


choice

choice

Full profile, 167 students Fractional factorial design, 18 choice sets

Verbal description

Hierarchical Bayes mixture

of normals model

Full profile, 213 Austrian consumers

Fractional factorial design, 15 different product-profiles

Verbal description

20-point rating scales

estimate boththe RCM and

the LCM by the Markov Chain

MonteCarlo methods

Full-text reviews, 20,419online product reviews

The recommended negative binomial regression (NBR) model is supported by an additional ACA study using the concerning attributes. Thisevaluation identifies benefits that can result when combining both methods to reach a more reliable estimation of the preferences existing in a market of interest. The suggested methodology enables the estimation of parameters, which allow inferences on the relative effect of product attributes and brand names on the overall evaluation of the products. See complete article for more details.

Full profile, 100 simulated respondents

fractional factorial design, 3 alternatives and an additional none

optionVerbal

descriptionmultinomial

logit


fractional factorial design,

Rank order

Choice

Full profile, 133 respondents Verbal description

11-point scale

ordinary least squares (OLS)



Full profile, 179 online interviews

Fractional factorial design, 3 sets, 16 stimuli

verbal description

linear regression

Full profile, 120 subjects Fractional factorial design, 15 choice sets, 4 stimuli

Psyhical prototype

stimuli, photographic images and

verbal description

Multinomial logit

Rating

-

full ranking ANOVA

Full profile, 200 respondents Fractional factorial design, 3 products from the same category,

Psyhical products

Regression model

Full profile, 422 respondents Fractional factorial design, 8 VCR products

Realistic pictures,

pictorial model

Seven-point scale ranging from “little preference” to “a lot of

preference”

Full profile, 305 respondentsfractional factorial design, 8

combinations from 32 possible scenarios

verbal andvisual




choice

Choice


Full profile, MNL: 300 respondents, CBC: 425

respondents

MNL: 12 choice sets per respondent, CBC: 15

choice sets of 3 alternativesVerbal

descriptionMNL and

correlated probit

Full profile, 616 graduate and undergraduate students

4 partiallybalanced blocks using an orthogonal

fractional factorialdesign

Psyhical products

100-point preference

scaleRegression

model

Full profile, 2,255 wine consumers

2 sets of 12 choice-based questions, The first 10 questions

of each set were designed by a different method (the

order was rotated), The last 2 questions were randomly

selected holdouts. See complete article for more details.

Pictorial and verbal

description

They used as a comparation 4 methods: HB, AC, ACi and

Ace. See complete article

for more details.


Observations

Mean absolute error

Pretest feedback


Method for testing the validity

The authors analyze the attractiveness of online music business models from the consumer’s perspective

The results lead to an improved awareness of the determinantsof contest design preferences as well as insights and implications for sales managers seeking to design effectivecontests

The model has important influnce on predictive validity of CA

The authors compare various segmentation methods for conjoint analysis and show that the finitemixture regression approach by DeSarbo and colleagues(1992) has the highest predictive validity

4 as holdouts for validation

The model helps to select pairs that have the highest likelihood of canceling out those missing attributes. The results show that consumers’ imputation processes can be influenced by manipulating their prior information about a product category

The authors ask for solutions to attribute density in conjoint research such as: to understand whether and how respondents deal with missing information, to reduce density before the


-

-

They present a clarification of the original model, propose an integration of several new imputation rules add new measurement metrics for pattern matching, and draw a roadmap for further real-world tests. The authors also discuss general modeling challenges when researchers want to mathematically define and integrate behavioral regularities into traditional quantitative domains. They conclude by suggesting several critical success factors for modeling behavioral regularities in marketing. The authors encourage collaborations not only between behavioral researchers and modelers within the marketing domain itself but also across different fields (e.g., economics, operations, psychology, sociology, statistics) as a way to undertake challenging and important research in marketing

Out-of-sample predictions

The results providea strong motivation for conjoint practitioners to consider conducting studies in realistic settings using incentive structures that require participants to “live with” their decisions. See complete article for more details.

Including response times in choice models results in better fit, provides more narrow confidenceintervals of the choice model parameter estimates, reduces heterogeneity, and provides better holdout predictions. if subjects spendmore time processing the information presented on the alternatives,choice heterogeneity decreases

The authors explore whether the success of aggregate customization can be extended to individual-level adaptive question design. The simulations suggest that polyhedral question design does well in many domains, particularly those in which heterogeneity and partworthmagnitudes are relatively large

8 additional holdout profiles to

assess the predictive validity

The authors show that FM and HB models are equally effective in recovering individual-levelparameters and predicting ratings of holdout profiles. Two surprising findings are that (1) HB performs well even when partworths come from amixture of distributions and (2) FM produces good parameter estimates,even at the individual level. The authors show that both models are quite robust to violations of underlying assumptions and that traditional individual-level models overfit the data

pretests

Additional holdout

new truck platforms worth approximately $2.4 billion–$3.2 billion and $1 billion–$2 billion, respectively. The authors compared complementary methods for understanding customer-needs combinations: Qualitative and ethnographic, Tailored Interviews, Segmentation, Interest or intent, AIO studies, Conjoint analyses, Truck clinics, Listening in. See complete article for more details.

The results show that: interpersonal relationships between buyers and suppliers serve as a switching barrier but are considerably less important than both firm-level switching costs and marketing variables, interpersonal relationships do not play the frequently mentioned role of a buffer against price and product competition, buyers and suppliers hold systematicallydifferent views of the determinants of switching.

They used theremaining two tasks

for reliability and validity testing. They

also did a external validity check

The authors findthat the simultaneous release of movies in theaters and on rental home video generates maximum revenues for movie studios in the United States but has devastating effects on other players, such as theater chains.

Ind. Level: holdout sets, Choice share: MAE, BTL model

was used for rating based conjoint and the logit model for

choice-based conjoint.

Within both rating- and choice-based models, hierarchical Bayes models have higher hit rate and choice share validations than latent segment models. there does not seem to be compelling empirical evidence to choose choicebased over rating-based conjoint models (or vice versa).

S1: The RankOrdermechanism leads to substantial improvement in predictive performance when compared to non-aligned hypothetical choices. S2: The RankOrder mechanism leads to substantial improvement in predictive performance when compared to non-aligned hypothetical choices

-

The consumer behaviour is not rational in the sense that they prefer lower prices to higher prices; for the consumer with a clear brand preferences the 9-ending prices is a opportunity to buy the brand cheaper. See complete article for more results.

8 additional evaluations of

the 23 full-factorial design were generated as

holdoutprofiles

RCM dominates the LCM if theunderlying distribution is strictly continuous. The LCM was found to dominate the RCM in the discretecase as soon as the data conveys enough information to support the true number of classes. See complete article for more details.

The recommended negative binomial regression (NBR) model is supported by an additional ACA study using the concerning attributes. Thisevaluation identifies benefits that can result when combining both methods to reach a more reliable estimation of the preferences existing in a market of interest. The suggested methodology enables the estimation of parameters, which allow inferences on the relative effect of product

See complete article for more details.

4 additional holdout choice sets. A

validity test shows that this procedure can compete with

state-of-the-art CBC methods.

HIT-CBC avoids the problem of number-of-levels effect because it reduces every attribute to two levels. HIT-CBC introduces the possibility of using individualized willingness-to-pay measures as price levels, which results in more flexibility for modeling demand functions

extended no-choice multinomial logit model, and the nested no-choice multinomial logit model using the D-optimality criterion and the modified Fedorov algorithm and compare these optimal designs with a reference design, which is constructed while ignoring the no-choice option, in terms of estimation and prediction accuracy. They conclude that taking into account the no-choice option when designing a no-choice experiment only has a marginal effect on the estimation and prediction accuracy as

-

Price has a substantiveimpact on choice alone, while a strong brand name is helpful for the service provider only in theconsideration stage. See complete article for more results.

The study shows that one criterion, Akaike’s Information Criterion (AIC) with a per-parameter penalty factor of 3 (AIC3), is clearly the best criterion to use across a wide variety of model specifications See complete article for more details


In terms of validity, both methods do

not show satisfactory results for measuring WTP.

In low involvement situations VA seems to be able to reproduce WTP better than LCA. For high involvement products the results are contradictory.

Predicted choice probability

No substantial differences in the choice decisions made by using psyhical prototype stimuli and realistic pictorial presentation and also the warm up or training didn't had significant influence on internal validity. See complete article for more details

Follow-up sample

-

-

The main explanation for the differences observed in sellingprices and cost structures of competing value systems lay not in the interface costs between valuechains such as logistics, as expected, nor only in advertising costs, but in the internal costs of individual value system members

The effects of visual complexity and symmetry on consumers’ preferences depend on theproduct value to which consumers paid attention

The conjoint results indicate that perceptions about packaging technology (portrayingconvenience) play the most important role overall in consumer likelihood to buy


The authors reach the conclusion that the likelihood principle is implicit to the Bayesian approach to statistics where the posterior distribution is derived from the prior distribution and the likelihood. Bayesian analysis conditions on the data to draw inferences about unobservable parameters in the analysis. In a conjoint analysis, it provides an answer to the question "Given the data at hand, what do I know about the part-worths?" Their view is that the answer to this question is more managerially relevant than the corresponding frequentist question concerning performance of an estimator

The biases and inefficiencies are real and in the direction predicted. The authors provide stylized models and more general explanations with which to understand and isolate the cause of these phenomena. Furthermore, empirically, they find no evidence that metric utilitybalanced questions reduce response error. Contrary to common wisdom, orthogonality (efficiency) in metric questions

follow-up question

Holdout exercise

They reach the conclusion that their approach significantly outperforms both the method of Toubia et al. (2004) and standard logistic regression; is less sensitive to noise, high response error; is relatively weaker when data from an orthogonal design are used. (this limitation indicates that it may be important to combine the proposed method with a method similar in spirit for designing questionnaires) it's a simple method for handling heterogeneity lead to promising results with performance often similar to that of HB and estimates the interaction coefficients significantly better

The average representation of preferences changes relatively little using the loss function approach.The use of a normal distribution with mean 0 minimizes the adjustments at the individual level, and it is simple toillustrate the differences between the constrained and unconstrained analysis. See complete article for more details.

The authors explored howthe richness of preference models used in traditional conjoint analysis techniques could be leveraged todesign online decision aids without requiring the extensive and detailed inputs usually necessary forthese kinds of models. Thestepwise componential regression method achieved the same predictive accuracy as a full conjoint analysis

Holouts validation choice questions

The authors provide a probabilistic interpretation of polyhedral methods and propose improvements that incorporate response error and/or informative priors into individual-level question selection and estimation. See complete article for more details

The method uses centrality con-cepts and ellipsoid shape approximations. The authors tested the method using a series of Monte Carlo simula-tions. The findings confirm that the polyhedral algorithm is particularly suited to contexts where re-searchers are limited to asking relatively few questions compared to the number of parameters. By isolating the impact of the question design component, they found that the relative accuracy of the method is due, at least in part, to the design of the questions. Their simulations suggest that hybrid polyhedral ques-tion-selection methods

Author(s) Title Year Journal Topic

2010

2004

2002

2004

2004

Rao V.R. 2004

Rubin D. 2004

2004

Preference model

Papies D., Eggers F. and Wlömert N.

Music for free? How free ad-funded downloadsaffect consumer choice


Consumer Preferences for online music, market segmentation and willingness-to-pay

Part-worth function, 6 attributes, 4-5 levels,

Murphy W., Dacin P. and Ford N.

Sales Contest Effectiveness: An Examination of Sales Contest Design Preferencesof Field Sales Forces


Understanding salespersons' preferences for various contest designs

Part-worth function 5 attributes, 2-3 levels,

Hofstede F.T., Kim Y. and Wedel M.

Bayesian Prediction in Hybrid ConjointAnalysis


The heterogeneity in self-stated and estimated part-worths in hybrid conjoint studies and their relationship. The authors reanalyze the data collected by Srinivasan and Park (1997), who studied MBA students who were choosing among job offers

Part-worth, 2 attributes at 2 levels, 3attributes at3 levels, 2 at 4 levels, and 1 at 6 levels


A Learning-Based Model for Imputing Missing Levels in Partial Conjoint Profiles


The problem of incomplete attribute information and the potential pitfalls of imputing missing attribute levels

Vector model, 6 attributes, 2 levels

Alba J., Cooke A.D.J.

When Absence Begets Inference in ConjointAnalysis




Comments on Conjoint Analysis with PartialProfiles




Design and Modeling in Conjoint Analysiswith Partial Profiles





Modeling Behavioral Regularities ofConsumer Learning in Conjoint Analysis


Note of the authors proposing several extensions of their own model of consumer learning in conjoint analysis


2005

2000

2004

2002

2004

Ding M., Grewal J. and Liechty J.

Incentive-Aligned Conjoint Analysis


The authors propose the incentive-aligned conjoint analysis instead of hypothetical studies. Field experiment in a Chinese restaurant (S1) and a second study that uses snacks as the context (S2)

S1: part-worth model, 8 attributes, 2-4 levels S2: 4 attributes, 2-5 levels

Haaijer R., Kamakura W. and

Wedel M.

Response Latencies in the Analysis ofConjoint Choice Experiments


The authors use filteredresponse latencies to scale the covariance matrix of a multinomial probit model and show that this leads to better model fit and holdout predictions. They used data from a technological product, collected bySawtooth Systems.

Vector model, 6 attributes, 2-6 levels: brand (6), speed (4), technological

type (6), digitizing

option (3), facsimile (2), and price (4)

Toubia O., Hauser J. and Simester D.

Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis


The authors propose a CBC question-design methodthat adapts questions by using the previous answers from that respondent (i.e., individual adaptation)

Path worth, 8 attributes, 2-4

levels

Andrews R., Ansari A. and Currim I.

Hierarchical Bayes Versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery


The authors reanalyze the idea of Vriens, Wedel, and Wilms (1996) who founded that finite mixture (FM) conjoint models had the best overall performance of nine conjoint segmentationmethods in terms of fit, prediction, and parameter recovery.

Path worth, 6 product

attributes at 3 levels each

Urban G.L., Hauser J.R.

“Listening In” to Find and Explore NewCombinations of Customer Needs


The authors did a dynamic presentation of complementary methods for understanding customer-needs combinations: truck example


2001

2007

Moore W.L. 2004

2009

2007

Wathne K. H., Biong H. and Heide

J.B.

Choice of Supplier in Embedded Markets: Relationship and Marketing Program Effects


The authors develop a conceptual framework of how relationship and marketing variables influence choice of supplier and test the framework empirically in the context of business-to-business services.

Vector model, 4 factors each with 2 levels

Hennig-Thurau T., Henning V., Sattler H., Eggers F., and

Houston M. B.

The Last Picture Show? Timing andOrder of Movie Distribution Channels


The authors discuss different scenarios and their implications for movie studios and other industry players, and barriers for theimplementation of the revenue-maximizing distribution models are critically reflected.


levels

A cross-validity comparison of rating-based and choice-based conjoint analysis models



The paper compares OLS, hierarchical Bayes (HB), and latent segment, rating-based conjoint models to HB and latent segment choice-based conjoint models.


levels

Dong S., Ding M. and Huber J.

A simple mechanism to incentive-align conjoint experiments



The authors propose an alternative mechanism to incentive-align conjoint based on inferred rank order for situations where conjoint practitioners have more than one version of real products

Part-worth, 7attributes, each with 3

levels

Baumgartner B., Steiner W.J.

Are consumers heterogeneous in their preferences for odd and even prices?Findings from a choice-based conjoint study



The authors analyze consumers' preferences for 9-ending versus 0-endingprices at the individual level. Two product categories: chocolate drinks and notebooks


2004

2010

Eggers F., Sattler H. 2009

2008

2009

Otter T., Tuchler R., and Frqhwirth-

Schnatter S.

Capturing consumer heterogeneity in metric conjoint analysisusing Bayesian mixture models



A comparison of the random coefficients model (RCM) and the latent class model (LCM) using simulated data illustrates that the RCM dominates the LCM if the underlying distribution is strictly continuous. Application to the mineral water market


Decker R., Trusov M.

Estimating aggregate consumer preferences from online product reviews



The authors are trying to find the answer to the question: how to turn the available plentitude of individual consumer opinions into aggregate consumerpreferences? Product review data from the mobile phone market

part-worth, 23 attributes, 2-4

levels

Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels



The authors introduce hybrid individualized two-level choice-based conjoint (HIT-CBC), which combinesself-explicated preference measurement (SE) with choice-based conjoint analysis (CBC). The authors tested HIT-CBC in an empirical study pertaining to European flights

part-worth, CBC: 3

attributes, 3,5 levels, The HIT-

CBC reduces the number of levels at two: the best and

worst level, the authors started the empirical study with 6

attributes, 3-6 levels

Vermeulen B., Goos P. and

Vandebroek M.

Models and optimal designs for conjoint choice experiments including ano-choice option



The improvement of realily of an experimental conjoint analysis by using a no-choice option in a choice set


Wuyts S., Verhoef P.C., and Prins R.

Partner selection in B2B information service markets



The first research which combines conjoint analysis with a between-subjectsexperimental design to test the effect of contingency factors. Experiment on factors influencing the choice of a research market company.

Linear, six attributes of

two levels each,

2003

Kim T., Lee H. 2009

2007

2001

Davies G., Brito E. 2004

2010

Andrews R., Currim I.

Retention of latent segments in regression-based marketing models



This study investigates via simulation the performance of seven segmentretention criteria used with finite mixture regression models for normal data


External validity of marketsegmentation methodsA study of buyers of prestige cosmetic brands


The article compares and validates the results of two clustering methods for the segmentation of the market for prestige cosmetics in Korea


Sichtmann C., Stingel S.

Limit conjoint analysis andVickrey auction as methods to elicit consumers’ willingness-to-payAn empirical comparison


This paper aims to analyze the differences in WTP elicited by conjoint analysis (LCA) and Vickrey auctions (VA) methods and their validity in high and low involvement situations.

Part-worth, 3 attributes, 3,2,3

levels

Jaeger S. R., Hedderley D. and

MacFie H. J. H.

Methodological issues in conjoint analysis: a case study


The authors did a choice-based conjoint study for measuring the consumer preferences for pre-packed apple selection packs. They also discuss the differences between psyhical prototype stimuli and realistic pictorial presentation and the need of prior training and warm-up of the respondents


levels

Price and quality competition between brands and own brandsA value systems perspective


Conjoint analysis is used to measure the quality of the competing products by comparing the ratings given by consumers for the edible products and available chemical analysis for detergents


levels

Creusen M., Veryzer R. and Schoormans J.

Product value importance and consumer preference for visual complexity and symmetry


This paper therefore, seeks to assess how preference for visual complexity and symmetry depends on the type of product value that is important to people

Vector, 2 attribute, 2

levels, preferred level of each visual

design principle (high or low)

2007

2003

2007

2005

2005

2008

Silayoi P., Speece M

The importance of packaging attributes: a conjoint analysis approach


The paper aims to investigate the need for information regarding the consumer psychology for developing packages


levels

Liechty J.C., Fong D. and DeSarbo W.

Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis

Marketing science

the structure underlying preferences can change during the administration of repeated measurements (e.g., conjoint analysis) and data collection because of effects from learning, fatigue, boredom, and so on


Liu Q., Otter T. and Allenby G.

Investigating Endogeneity Bias in Conjoint Models

Marketing science

The authors re-examine the endogeneity bias identified by Hauser and Toubia (HT), and explain its presence using traditional econometric methods


Hauser J., Toubia O.

The Impact of Utility Balance and Endogeneity inConjoint Analysis

Marketing science

The authors use formal models, simulations, and empirical data to suggest that adaptive metricutility balance leads to partworth estimates that are relatively biased—smaller partworths are upwardly biased relative to larger partworths.


Evgeniou T., Boussios C. and

Zacharia G.Generalized Robust Conjoint Estimation

Marketing science

They propose a method based on computationallyefficient optimization techniques. They compare their method with standard logistic regression, hierarchical Bayes, and the polyhedral methods using standard, widely used simulation data


Gilbride T., Lenk P., and Brazell J.

Market Share Constraints and the Loss Functionin Choice Based Conjoint Analysis

Marketing science

This paper presents a Bayesian decisiontheoretic approach to incorporating base case market shares into conjoint analysis via the lossfunction. Simulateddata for both the multinomial logit and correlated probit discrete choice models.

MNL: 1 attribute, 4

levels, CBC: 20binary

attributes and the price

2008

2007

2003

De Bruyn A., Liechty J., Huizingh

E. and Lilien G.

Offering Online Recommendations withMinimum Customer Input Through Conjoint-Based Decision Aids

Marketing science

The authors compare compare three algorithms—cluster classification, Bayesian treed regression, and stepwise componential regression—to develop an optimal sequence of questions and predict online visitors’ preferences

Part-worth, 5 attributes, 2,3

levels

Toubia O., Hauser J. and Garcia R.

Probabilistic Polyhedral Methods forAdaptive Choice-Based Conjoint Analysis:Theory and Application

Marketing science

Polyhedral methods for choice-based conjoint analysis. the authors tested the following four question-selectionmethods:orthogonal design; aggregate customization; deterministic polyhedral; probabilistic polyhedra.Wine industry

Part-worth, 5 features at 4 levels each

Toubia O., Simester D. and

Hauser J.Fast Polyhedral Adaptive Conjoint Estimation

Marketing science

They propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming.


Data collection method Stimulus set construction

Choice

Scheffe tests

Stimulus presentation

Measurement scale dependent var.

Estimation method

Full profile, 2 540 respondents Random sampling, 3 stimuli and a no-choice-option

Verbal description

Multinomial logit

Full profile, 796 respondents Fractional factorial design with SPSS ORTHOPLAN, 16 full profiles

Verbal description

Rank order, 1 (the most preferred)to 16 (the least preferred)

Full profile, 108 MBA students Fractional factorial design, 18 profiles, as well as 6 holdout profiles

Verbal description

Self-explicated and rating scores. The model estimates a set of scaling constants for each respondent

They developt a finite mixture regression model for full profileconjoint

The model assumes that consumers learnand update after each stimulus (partial profile) about the pattern underlying the product attributes, their levels, and the correlations between them. Experiment: Full profile 130 undergraduate students

20 digitalcamera profiles, 4 as holdouts for validation

Verbal description. The learning based-model was based on a experiment composed of two phases: learning(prior) and rating.

Rating scale, 0–9 Likert scale, choice

Hierarchical Bayesianapproach to account for heterogeneity

Choice

choice HB and AC

choice

Full profile, S1:108 undergraduate and graduate students, S2: 59 senior undergraduate students

Fractional factorial design. S1: 3 groups of 12 choicesets. Each choice set had 3 profiles (Chinese meals) anda “none of the above” option. The restaurant served the meal theychose. S2: 27 conjoint tasks, 30 unique snack combos for the holdout task

Physical products

Rating 1–7 “agree–disagree” scale, choice

Insamplehit rate and log-marginal probability

Full profile, 200 respondentsRandom sampling, 20 individualized choice sets with 3 alternatives and a one no choice

Verbal description

They develop a multinomial probit (MNP)

model

Full profile, 354 Web-based respondents

Before respondentsanswered the stated-choice questions, they revieweddetailed descriptions of the levels of each feature and couldaccess the descriptions at any time by clicking the feature’slogo. 4 sets with 8 features

Verbal description

Full profile, 150 consumersEach data set contains the

evaluations of 150 consumerson either 18 or 27 profiles (Factor 6). See complete article for more details.

Verbal description

Finite mixture, HB models

16 cards

choice

Choice

choice

Full profile, 114 customer accounts, 37 key account

managersVerbal

descriptionRating scale 1

to 16

they used two ordinary least

squares regression

models

Full profile, 1770 consumers Random sampling, seven choice sets and a “no consumption” option

Verbal and pictorial

description Hierarchical

Bayes routine

S1: Full profile, (88) S2: Full profile (89

respondents)

S1: Fractional factorial design, 16 profiles, as well as 6 holdout profiles,

S2: 16choice sets, 1 which included 2

automobiles as wellas the option to continue to shop

Verbal description

Rating scale, 0–10 scale and second study choice

hierarchical Bayesian


Full profile S1: 41 and S2: 44 respondents

S1: 36 profiles produced by SAS experimental design were divided

into 12 sets with 3profiles for each conjoint choice set, S2: 19 options plus the option of no

purchase

Verbal description

Hierarchical Bayesian


Full profile, 167 students Fractional factorial design, 18 choice sets

Verbal description

Hierarchical Bayes mixture

of normals model

choice

fractional factorial design,

Full profile, 213 Austrian consumers

Fractional factorial design, 15 different product-profiles

Verbal description

20-point rating scales

estimate boththe RCM and

the LCM by the Markov Chain

MonteCarlo methods

Full-text reviews, 20,419online product reviews

The recommended negative binomial regression (NBR) model is supported by an additional ACA study using the concerning attributes. Thisevaluation identifies benefits that can result when combining both methods to reach a more reliable estimation of the preferences existing in a market of interest. The suggested methodology enables the estimation of parameters, which allow inferences on the relative effect of product attributes and brand names on the overall evaluation of the products. See complete article for more details.

Full profile, 100 simulated respondents

fractional factorial design, 3 alternatives and an additional none

optionVerbal

descriptionmultinomial

logit

Full profile, 133 respondents Verbal description

11-point scale

ordinary least squares (OLS)

Rank order

Choice

Rating

-

Full profile, 179 online interviews

Fractional factorial design, 3 sets, 16 stimuli

verbal description

linear regression

Full profile, 120 subjects Fractional factorial design, 15 choice sets, 4 stimuli

Psyhical prototype

stimuli, photographic images and

verbal description

Multinomial logit

Full profile, 200 respondents Fractional factorial design, 3 products from the same category,

Psyhical products

Regression model

Full profile, 422 respondents Fractional factorial design, 8 VCR products

Realistic pictures,

pictorial model

Seven-point scale ranging from “little preference” to “a lot of

preference”

full ranking ANOVA

choice

Full profile, 305 respondentsfractional factorial design, 8

combinations from 32 possible scenarios

verbal andvisual

Full profile, MNL: 300 respondents, CBC: 425

respondents

MNL: 12 choice sets per respondent, CBC: 15

choice sets of 3 alternativesVerbal

descriptionMNL and

correlated probit

Choice

Full profile, 616 graduate and undergraduate students

4 partiallybalanced blocks using an orthogonal

fractional factorialdesign

Psyhical products

100-point preference

scaleRegression

model

Full profile, 2,255 wine consumers

2 sets of 12 choice-based questions, The first 10 questions

of each set were designed by a different method (the

order was rotated), The last 2 questions were randomly

selected holdouts. See complete article for more details.

Pictorial and verbal

description

They used as a comparation 4 methods: HB, AC, ACi and

Ace. See complete article

for more details.

Observations

Mean absolute error

Pretest feedback

Method for testing the validity

The authors analyze the attractiveness of online music business models from the consumer’s perspective

The results lead to an improved awareness of the determinantsof contest design preferences as well as insights and implications for sales managers seeking to design effectivecontests

The model has important influnce on predictive validity of CA

The authors compare various segmentation methods for conjoint analysis and show that the finitemixture regression approach by DeSarbo and colleagues(1992) has the highest predictive validity

4 as holdouts for validation

The model helps to select pairs that have the highest likelihood of canceling out those missing attributes. The results show that consumers’ imputation processes can be influenced by manipulating their prior information about a product category

-

-

Out-of-sample predictions

The results providea strong motivation for conjoint practitioners to consider conducting studies in realistic settings using incentive structures that require participants to “live with” their decisions. See complete article for more details.

Including response times in choice models results in better fit, provides more narrow confidenceintervals of the choice model parameter estimates, reduces heterogeneity, and provides better holdout predictions. if subjects spendmore time processing the information presented on the alternatives,choice heterogeneity decreases

The authors explore whether the success of aggregate customization can be extended to individual-level adaptive question design. The simulations suggest that polyhedral question design does well in many domains, particularly those in which heterogeneity and partworthmagnitudes are relatively large

8 additional holdout profiles to

assess the predictive validity

The authors show that FM and HB models are equally effective in recovering individual-levelparameters and predicting ratings of holdout profiles. Two surprising findings are that (1) HB performs well even when partworths come from amixture of distributions and (2) FM produces good parameter estimates,even at the individual level. The authors show that both models are quite robust to violations of underlying assumptions and that traditional individual-level models overfit the data

pretests

Additional holdout

-

The results show that: interpersonal relationships between buyers and suppliers serve as a switching barrier but are considerably less important than both firm-level switching costs and marketing variables, interpersonal relationships do not play the frequently mentioned role of a buffer against price and product competition, buyers and suppliers hold systematicallydifferent views of the determinants of switching.

They used theremaining two tasks

for reliability and validity testing. They

also did a external validity check

The authors findthat the simultaneous release of movies in theaters and on rental home video generates maximum revenues for movie studios in the United States but has devastating effects on other players, such as theater chains.

Ind. Level: holdout sets, Choice share: MAE, BTL model

was used for rating based conjoint and the logit model for

choice-based conjoint.

Within both rating- and choice-based models, hierarchical Bayes models have higher hit rate and choice share validations than latent segment models. there does not seem to be compelling empirical evidence to choose choicebased over rating-based conjoint models (or vice versa).

S1: The RankOrdermechanism leads to substantial improvement in predictive performance when compared to non-aligned hypothetical choices. S2: The RankOrder mechanism leads to substantial improvement in predictive performance when compared to non-aligned hypothetical choices

The consumer behaviour is not rational in the sense that they prefer lower prices to higher prices; for the consumer with a clear brand preferences the 9-ending prices is a opportunity to buy the brand cheaper. See complete article for more results.

-

8 additional evaluations of

the 23 full-factorial design were generated as

holdoutprofiles

RCM dominates the LCM if theunderlying distribution is strictly continuous. The LCM was found to dominate the RCM in the discretecase as soon as the data conveys enough information to support the true number of classes. See complete article for more details.

4 additional holdout choice sets. A

validity test shows that this procedure can compete with

state-of-the-art CBC methods.

HIT-CBC avoids the problem of number-of-levels effect because it reduces every attribute to two levels. HIT-CBC introduces the possibility of using individualized willingness-to-pay measures as price levels, which results in more flexibility for modeling demand functions

Price has a substantiveimpact on choice alone, while a strong brand name is helpful for the service provider only in theconsideration stage. See complete article for more results.

Follow-up sample

-

In terms of validity, both methods do

not show satisfactory results for measuring WTP.

In low involvement situations VA seems to be able to reproduce WTP better than LCA. For high involvement products the results are contradictory.

Predicted choice probability

No substantial differences in the choice decisions made by using psyhical prototype stimuli and realistic pictorial presentation and also the warm up or training didn't had significant influence on internal validity. See complete article for more details

The main explanation for the differences observed in sellingprices and cost structures of competing value systems lay not in the interface costs between valuechains such as logistics, as expected, nor only in advertising costs, but in the internal costs of individual value system members

The effects of visual complexity and symmetry on consumers’ preferences depend on theproduct value to which consumers paid attention

-

follow-up question

The conjoint results indicate that perceptions about packaging technology (portrayingconvenience) play the most important role overall in consumer likelihood to buy

The average representation of preferences changes relatively little using the loss function approach.The use of a normal distribution with mean 0 minimizes the adjustments at the individual level, and it is simple toillustrate the differences between the constrained and unconstrained analysis. See complete article for more details.

Holdout exercise

The authors explored howthe richness of preference models used in traditional conjoint analysis techniques could be leveraged todesign online decision aids without requiring the extensive and detailed inputs usually necessary forthese kinds of models. Thestepwise componential regression method achieved the same predictive accuracy as a full conjoint analysis

Holouts validation choice questions

The authors provide a probabilistic interpretation of polyhedral methods and propose improvements that incorporate response error and/or informative priors into individual-level question selection and estimation. See complete article for more details

2007

2010

2002

2005

2003

2003

2002

2004

2005

The century of Bayes 2006

2007

2009

2010

2009

2010

2010

Predicting purchase decisions with different conjoint analysis methods: a Monte Carlo simulation

International journal of market research

Eye-tracking information processing in choice-based conjoint analysis


Willingness of adults in Europe to pay for a new vaccine: the application of discrete choice-based conjoint analysis


Determining the design of child-specific adoption advertisements: a conjoint analysis.


The use of combined conjoint approaches to improve market share predictions


Genetic Algorithms for product design: how well do they really work?


A novel approach to modelling the prescribing decision, integrating physician and patient influences


Information overload in conjoint experiments


An empirical comparison of methods to measure willingness to pay by examining the hypothetical bias



Conjoint respondents as adaptive decision makers


The truth is out there! How external validity can lead to better marketing decisions


Incorporating demographics into discrete choice analyses


Using statistical design experiment methodologies to identify customers’ needs


Using partial profile choice experiments to handle large numbers of attributes


The heterogeneous best-worst choice method in market research


2001

2004

2009

2009

2002

2010

2009

2009

2007

The No–Choice Alternative in Conjoint Choice Experiments


An investigation of country-of-origin effect using correspondence analysis: a cross-national context


A framework for designing new products and services


A maximum difference scaling application for customer satisfaction researchers


Egotists, Idealists and Corporate Animals - Segmenting Business Markets


Personal aspirations and the consumption of luxury goods


Rethinking data analysis - part two: some alternatives to frequentist approaches


Unravelling concealed cognitive structures - generalised linear modelling of hierarchical value maps


The choice between a five-point and a ten-point scale in the framework of customer satisfaction measurement


review articles conjoint last decade

Documents