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1998, Marketing Science
Experimental conjoint choice analysis is among the most frequently used methods for measuring and analyzing consumer preferences. The data from such experiments have been typically analyzed with the Multinomial Logit (MNL) model. However, there are several problems associated with the standard MNL model because it is based on the assumption that the error terms of the underlying random utilities are independent across alternatives, choice sets, and subjects. The Multinomial Probit model (MNP) is well known to alleviate this assumption of independence of the error terms. Accounting for covariances in utilities in modeling choice experiments with the MNP is important because variation of the coefficients in the choice model may occur due to context effects. Previous research has shown that subjects' utilities for alternatives depend on the choice context, that is, the particular set of alternatives evaluated. Simonson and Tversky's tradeoff contrast principle describes the effect of the choice context on attribute importance and patterns of choice. They distinguish local contrast effects, which are caused by the alternatives in the offered set only, and background contrast effects, which are due to the influence of alternatives previously considered in choice experiments. These effects are hypothesized to cause correlations in the utilities of alternatives within and across choice sets, respectively.
1996
We develop a Multinomial Probit model with an X-factor covariance structure tha t can be used to estimate conjoint choice models. This model can be used fo r predictions which is not possible for general Multinomial Probit models. The model also solves problems related to the identification of the general Multinomial Probi t model for Conjoint choice experiments. We show that in an application our mode l fits the data significantly better than the Multinomial Logit model and Independen t Probit model. We assess the predictive validity of the model, and perform a Mont e
1998
Several (ratings-based) conjoint analysis and experimental choice (choice-based conjoint) models are compared on their ability to predict both aggregate choice shares among the sample and individual choices in an availability validation task. While there was a weak relationship between validations at the individual and aggregate levels, several models stand out. In general, models capturing individual differences validated well at both the individual and aggregate level. The hierarchical Bayes choice and conjoint models validated particularly well.
2011
the behavioral literature provides ample evidence that consumer preferences are partly driven by the context provided by the set of alternatives. three important context effects are the compromise, attraction, and similarity effects. because these context effects affect choices in a systematic and predictable way, it should be possible to incorporate them in a choice model. However, the literature does not offer such a choice model. this study fills this gap by proposing a discrete-choice model that decomposes a product's utility into a contextfree partworth utility and a context-dependent component capturing all three context effects. model estimation results on choice-based conjoint data involving digital cameras provide convincing statistical evidence for context effects. the estimated context effects are consistent with the predictions from the behavioral literature, and accounting for context effects leads to better predictions both in and out of sample. to illustrate the benefit from incorporating context effects in a choice model, the authors discuss how firms could utilize the context sensitivity of consumers to design more profitable product lines.
Papers in Regional Science, 1991
This paper describes the application of the extended or universal logit model to decompositional or "stated" choice modeling in order to increase the scope and validity of such choice models. In this approach, choice experiments are designed that permit the estimation of utility functions that include the effects of context variables like choice set composition and decision background. The approach is illustrated with some simple calculated examples concerning consumer choice of shopping center, housing, and transportation mode.
Journal of the American Statistical Association, 2007
Almost without exception, everything human beings undertake involves a choice. In recent years, there has been a growing interest in the development and application of quantitative statistical methods to study choices made by individuals with the purpose of gaining a better understanding both of how choices are made and of forecasting future choice responses. In this primer, the authors provide an unintimidating introduction to the main techniques of choice analysis and include detail on themes such as data collection and preparation, model estimation and interpretation, and the design of choice experiments. A companion website to the book provides practice data sets and software to estimate the main discrete choice models such as multinomial logit, nested logit, and mixed logit. This primer will be an invaluable resource to students as well of immense value to consultants/professionals, researchers, and anyone else interested in choice analysis and modeling.
Journal of Marketing Research, 2000
Response latencies provide information about consumers' choice behavior in a conjoint choice experiment. The authors use filtered response latencies to scale the covariance matrix of a multinomial probit model and show that this leads to better model fit and holdout predictions, even if the response latencies in the holdout task are not used. The authors provide an empirical application along with a tentative explanation for the findings of the effect of response latencies.
Journal of Consumer Psychology, 2000
Context effects refer to the shifts in shares when another alternative is introduced in the choice set. The alternative can be asymmetrically dominated, asymmetrically dominating, totally dominated, or totally dominating. We developed a theoretically derived model based on the shifts in attribute valuation as a potential explanation for all context effects. First, the model is tested using data from previously published studies. As predicted, the results showed a high correlation between shifts in valuation and changes in the choice shares. The model is also tested using 2 studies that extend the design of the choice sets to include better alternatives in a search context and the removal of an alternative. The strong relation justifies the case for comparative valuation as an underlying mechanism for context effects. Assuming this valuation, the article illustrates how the framework can be used to develop new product strategies taking into account the values of the unchosen alternatives.
2000
cause-related marketing (Varadarajan and Menon, 1988) share the notion that customers may choose products for reasons other than the way the products themselves are expected to perform. For example, customers may choose to buy a product that claims to have been made with less damage to the environment even if there is no advantage in quality or performance. Some products confound unobservable benefits of social responsibility with actual product characteristics. For example, organically grown produce may be chosen not only because of a belief that pesticides hurt the environment, but because consumers think products grown without pesticides taste better. Products also can reflect more than one socially responsible practice. Organic shade-grown coffee purports to help the environment, personal health, and human rights. Often, more socially responsible versions of a product carry a price premium which implicitly reflects higher costs associated with socially responsible business practices. Many consumers say they are willing to pay such premiums, depending on whether a particular practice falls within a domain that they support (Sen and Bhattacharya, 2001). Estimating price-attribute trade-offs in product choice has a long tradition in the marketing literature, although little of it has examined socially responsible or sustainable choices. The most common methods for examining the effect of trade-offs between price and other attributes have employed conjoint analysis (Carroll and Green, 1995). A more recent approach incorporated willingness to pay and various attitudinal variables into a discrete choice model as latent variables (Ashok, Dillon, and Yuan, 2002). Although multinomial choice models are well-established in the marketing literature (e.g.
Journal of Marketing Research, 2006
To date, no attempt has been made to design efficient choice experiments by means of the G-and V-optimality criteria. These criteria are known to make precise response predictions, which is exactly what choice experiments aim to do. In this article, the authors elaborate on the G-and V-optimality criteria for the multinomial logit model and compare their prediction performances with those of the D-and A-optimality criteria. They make use of Bayesian design methods that integrate the optimality criteria over a prior distribution of likely parameter values. They employ a modified Fedorov algorithm to generate the optimal choice designs. They also discuss other aspects of the designs, such as level overlap, utility balance, estimation performance, and computational effectiveness.
Journal of Business Research, 1991
We propose an iterative stepwise forward selection procedure to screen a subset of important variables from a large pool of candidate variables in choice-based conjoint analysis. The method involves computing weighted correlations between candidate variables and residuals from the prior best fitting multinomial logit (MNL) model. Candidate variables that pass the screening step are then introduced and the MNL model is refitted using standard MNL software. A series of diagnostic tests is carried out before each iteration cycle. The procedure can be implemented easily using commonly available statistical software. We illustrate the application of the proposed method on a large data set. G. Chakraborty et al. experiments can be modeled to estimate relative importance of the attributes, as well as part-worths associated with different levels of the attributes. This information provides considerable insight into consumers' decision making processes and is often used to predict marketplace behavior through choice simulators. Although different types of choice modeling procedures have been reported in the literature (Currim, 1982;, the more popular ones appear to be variants of the multinomial logit model (MNL).
2002
There is an emerging consensus among disciplines dealing with human decision making that the context in which a decision is made is an important determinant of outcomes. This consensus has been slow in the making because much of what is known about context effects has evolved from a desire to demonstrate the untenability of certain common assumptions upon which tractable models of behavior have generally been built. This paper seeks to bring disparate disciplinary perspectives to bear on the relation between context and choice, to formulate (1) recommendations for improvements to the state-of-the-practice of Random Utility Models (RUMs) of choice behavior, and (2) a future research agenda to guide the further incorporation of context into these models of choice behavior.
Seoul Journal of Business, 2013
It is customary in conjoint studies to introduce the same set of potential explanatory variables for each subject, so as best to allow any possible trade-offs to be made. However, this presumption can mask the possibility of some subjects' considering only a subset of the presented attributes. Moreover, such subsets of relevant attributes can vary considerably across the population. This paper presents a model which allows researchers to identify relevant explanatory variables for each subject separately. This is accomplished via a solution to the well-known variable selection problem in the context of discrete choice models; the proposed solution can be widely applied throughout choice studies and in fact to other response types, such as ratings, direct paired comparisons, and ranks, with appropriate changes in likelihood function. When estimated on a choice-based conjoint data for dial-readout scale products, the proposed model is strongly preferred to the traditional random-effect specification for choice-based conjoint. A sizeable group of subjects, approximately 63%, were found to consider proper subsets of all attributes presented. There was a great deal of heterogeneity in attributes deemed relevant across subjects: the proportion of subjects who did not consider a given attribute among the six used in the study ranged from 17.4% to 41.3%. For those who did consider a given attribute, estimated attribute level part-worths were essentially identical for the proposed model and the traditional random-effect conjoint model; but this was not the case for non-considered attributes. In fact, the traditional model was found to suffer from systematic biases in aggregate part-worth magnitudes. Finally, and most important for marketing practice, allowing for the possibility that some subject may not consider particular attributes can lead to substantial design and revenue differences in supposedly 'optimal' products, at both the individual-and the aggregate-level.
2000
1 The authors would like to thank the SEI Center for Advanced Studies in Management at Wharton for partially supporting this research and for supporting the 7 th Triennial Choice Conference held at the Wharton School. The first two authors (session co-chairs) and the third to twelfth authors are listed alphabetically.
Transportation Research Part E: Logistics and Transportation Review, 2012
ABSTRACT: This paper investigates alternative methods to account for preference heterogeneity in choice experiments. The main interest lies in assessing the different results obtainable when investigating heterogeneity in various ways. This comparison can be performed on the basis of model performance and, more interesting, by evaluating willingness to pay measures. Preference heterogeneity analysis relates to the methods used to search for it. Socioeconomic variables can be interacted with attributes and/or alternative-specific constants. Similarly one can consider different subsets of data (strata variables) and estimate a multinomial logit model for each of them. Heterogeneity in preferences can be investigated by including it in the systematic component of utility or in the stochastic one. Mixed logit and latent class models are examples of the first approach. The former, in its random variable specification, allows for random taste variations assuming a specific distribution of the attribute coefficients over the population and permit to capture additional heterogeneity by consenting parameters to vary across individuals both randomly and systematically with observable variables. In other words it accounts for heterogeneity in the mean and in the variance of the distribution of the random parameters due to individual characteristics. Latent class models capture heterogeneity by considering a discrete underlying distribution of tastes. The small number of mass points are the unobserved segments or behavioral groups within which preferences are assumed homogeneous. The probability of membership in a latent class can be additionally made a function of individual characteristics. Alternatively, heterogeneity can be incorporated in terms of the random component of utility. The covariance heterogeneity model adopts the second approach representing a generalization of the nested logit model and can be used to explain heteroscedastic error structures in the data. It allows the inclusive value parameter to be a function of choice alternative attributes and/or individual characteristics. An alternative method refers to an extension of the multinomial logit model in which the integration of unobserved heterogeneity is performed through random error components distributed according to a tree. An interesting improvement in modeling preference heterogeneity is related to its simultaneous inclusion in both systematic and stochastic parts. A valid example is the inclusion of an error component part in a random coefficient specification of the mixed multinomial logit model. The empirical data used for comparing the various methods tested relates to departure airport choice in a multi-airport region. The area of study includes two regions in central Italy, Marche and Emilia-Romagna, and four airports: Ancona, Rimini, Forlì and Bologna. A fractional factorial experimental design was adopted to construct a four alternative choice set and five hypothetical choice exercises in each questionnaire. The selection of the potentially most important attributes and their relative levels was developed on the basis of previous research.
ABSTRACT: This paper investigates alternative methods to account for preference heterogeneity in choice experiments. The main interest lies in assessing the different results obtainable when investigating heterogeneity in various ways. This comparison can be performed on the basis of model performance and, more interesting, by evaluating willingness to pay measures. Preference heterogeneity analysis relates to the methods used to search for it. Socioeconomic variables can be interacted with attributes and/or alternative-specific constants. Similarly one can consider different subsets of data (strata variables) and estimate a multinomial logit model for each of them. Heterogeneity in preferences can be investigated by including it in the systematic component of utility or in the stochastic one. Mixed logit and latent class models are examples of the first approach. The former, in its random variable specification, allows for random taste variations assuming a specific distribution of the attribute coefficients over the population and permit to capture additional heterogeneity by consenting parameters to vary across individuals both randomly and systematically with observable variables. In other words it accounts for heterogeneity in the mean and in the variance of the distribution of the random parameters due to individual characteristics. Latent class models capture heterogeneity by considering a discrete underlying distribution of tastes. The small number of mass points are the unobserved segments or behavioral groups within which preferences are assumed homogeneous. The probability of membership in a latent class can be additionally made a function of individual characteristics. Alternatively, heterogeneity can be incorporated in terms of the random component of utility. The covariance heterogeneity model adopts the second approach representing a generalization of the nested logit model and can be used to explain heteroscedastic error structures in the data. It allows the inclusive value parameter to be a function of choice alternative attributes and/or individual characteristics. An alternative method refers to an extension of the multinomial logit model in which the integration of unobserved heterogeneity is performed through random error components distributed according to a tree. An interesting improvement in modeling preference heterogeneity is related to its simultaneous inclusion in both systematic and stochastic parts. A valid example is the inclusion of an error component part in a random coefficient specification of the mixed multinomial logit model. The empirical data used for comparing the various methods tested relates to departure airport choice in a multi-airport region. The area of study includes two regions in central Italy, Marche and Emilia-Romagna, and four airports: Ancona, Rimini, Forlì and Bologna. A fractional factorial experimental design was adopted to construct a four alternative choice set and five hypothetical choice exercises in each questionnaire. The selection of the potentially most important attributes and their relative levels was developed on the basis of previous research.
Marketing Letters, 2008
1 The authors would like to thank the SEI Center for Advanced Studies in Management at Wharton for partially supporting this research and for supporting the 7 th Triennial Choice Conference held at the Wharton School. The first two authors (session co-chairs) and the third to twelfth authors are listed alphabetically.
Australian Journal of Agricultural and Resource Economics, 2019
This study assesses the comparability of discrete choice experiment, ranking conjoint analysis, and multi-profile best worst scaling in a non-hypothetical context in terms of estimated partworths, willingness to pay, response consistency, and external validity. Overall, the results suggest that (1) the conjoint analysis formats that were used in this study provide similar estimated WTP, but different estimated partworths and computed external validity, (2) the inclusion of the full ranking information in the estimation of the parameters of interest affects the estimated partworths, but not the estimated willingness to pay, and (3) it is more appropriate to use multi-profile best worst scaling over discrete choice experiment and ranking conjoint analysis because it has better predictive power of consumers' preferences and provides estimated willingness to pay comparable to those obtained in the others conjoint analysis formats. The best worst scaling' cognitive process could be considered clearness for participants implying significant increment of it predictive power.
SSRN Electronic Journal, 2017
Choice experiments designed to extend beyond the classic application of choice among perfect substitutes have become popular in marketing research. In these experiments, often referred to as menu based choice, respondents face choice sets that may comprise substitutes, complements, and offers that provide utility independently, or any mixture of these three types. The inferential challenge posed by data from such experiments is in the calibration of utility functions that accommodate a mix of substitutes, complements, and "independent" offers. Moreover, while a prior understanding of the product categories under study may, for example, suggest that two offers in a set are essentially perfect substitutes, this may not be true for all respondents. To address these challenges, we combine Besag's (1972, 1974) autologistic choice model with a flexible hierarchical prior structure. We explain from first principles how the autologistic choice model improves on the multivariate probit model, and on models that include cross-price effects in the utility function. We develop Bayesian inference for the autologistic choice model, including its intractable normalizing constant. Finally, we find empirical support for our model in a menu based conjoint experiment investigating demand for game consoles and accessories and we illustrate implications for optimal pricing.
2007
In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life the respondent does not always make a choice: often he/she does not prefer any of the alternatives offered. Therefore, including a no-choice option in a choice set makes a conjoint choice experiment more realistic. In the literature three different models are used to analyze the results of a conjoint choice experiment with a no-choice option: the no-choice multinomial logit model, the extended no-choice multinomial logit model and the nested no-choice multinomial logit model. We develop optimal designs for each of these models using the D-optimality criterion and the modified Fedorov algorithm. We compare the optimal designs with a reference design that was constructed ignoring the no-choice option and we discuss the impact of the different designs and models on the precision of estimation and the predictive accuracy based on a simulation study.
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