In this paper we carry out fully Bayesian analysis of the general heterogeneity model, which is a mixture of random effects model, and its special cases, the random coefficient model and the latent class model. Our application comes from Conjoint analysis and we are especially interested in what is gained by the general heterogeneity model in comparison to the other two when modeling consumers' heterogeneous preferences. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Choice-Based Conjoint (CBC) models are often used for pricing decisions, especially when scanner data models cannot be applied. Up to date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting real-world shop data. In this contribution, we measure the performance of a Latent Class CBC model not by means of an experimental hold-out sample but via aggregate scanner data. We find that the CBC model does not accurately predict real-world market shares, thus leading to wrong pricing decisions. In order to improve its forecasting performance, we propose a correction scheme based on scanner data. Our empirical analysis shows that the hybrid method improves the performance measures considerably. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Though recycled toilet paper (RTP) is more environmental friendly than toilet paper made of virgin fiber, they are also more expensive. The price difference has made manufacturers hesitate about marketing RTP in shops, because they assume that consumers are reluctant to buy it. However, no research has been done in Taiwan to explore consumers¡¦ willingness to pay (WTP) for RTP. This study is an attempt to do so by the use of two methods: contingent evaluation method (CVM) and conjoint analysis. Both methods were administered through a survey, which had a convenience sample of 192 female Kaohsiung citizens as respondents. Results based on CVM showed that the mean WTP for RTP was NT 151, which was a little higher than the price set by Homemaker¡¦s Union and Foundation¡Xan NGO which was the main channel selling RTP to the domestic sector in Taiwan. This suggests that manufacturers might have underestimated the market for RTP. To check the validities of the CVM and conjoint method, two criteria were measured: the respondents¡¦ past environmental behavior (PEB) and their willingness to make sacrifice for environmental causes (WTP3). The WTP as estimated by CVM, namely WTP1, was significantly related to both of these criteria (rs = .16, .32, ps < .05), showing that the CVM had a certain degree of validity. The importance that respondents attached to the eco-label of RTP was assessed by conjoint analysis. Validity of the importance scores thus obtained, however, was quite low: the correlation between these scores and WTP1, PEB and WTP3 were .05, .12 and .20 respectively, with only the last one significant. Results also suggest that environmental knowledge and health and safety concerns for products were not related to WTP1. Keywords: green consumption, contingent valuation method, conjoint analysis, environmental behavior, eco-label, green mark.

Thesis (doctoral)--Linköping University, 1996. / Includes abstract. Includes bibliographical references.

This paper presents the fully Bayesian analysis of the latent class model using a new approach towards MCMC estimation in the context of mixture models. The approach starts with estimating unidentified models for various numbers of classes. Exact Bayes' factors are computed by the bridge sampling estimator to compare different models and select the number of classes. Estimation of the unidentified model is carried out using the random permutation sampler. From the unidentified model estimates for model parameters that are not class specific are derived. Then, the exploration of the MCMC output from the unconstrained model yields suitable identifiability constraints. Finally, the constrained version of the permutation sampler is used to estimate group specific parameters. Conjoint data from the Austrian mineral water market serve to illustrate the method. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

No description available.

Conjoint analysis is one of the most important tools to support product development, pricing and positioning decisions in management practice. For this purpose various models have been developed. It is widely accepted that models that take consumer heterogeneity into account, outperform aggregate models in terms of hold-out tasks. The aim of our study is to investigate empirically whether predictions of choice-based conjoint models which incorporate heterogeneity can successfully be generalized to a whole market. To date no studies exist that examine the real world performance of choice-based conjoint models by use of aggregate scanner panel data. Our analysis is based on four commercial choice-based conjoint pricing studies including a total of 43 stock keeping units (SKU) and the corresponding weekly scanning data for approximately two years. An aggregate model serves as a benchmark for the performance of two models that take heterogeneity into account, hierarchical Bayes and latent class. Our empirical analysis demonstrates that, in contrast to the performance using hold-out tasks, the real world performance of hierarchical Bayes and latent class is similar to the performance of the aggregate model. Our results indicate that heterogeneity cannot be generalized to a whole market and suggest that aggregate models are sufficient to predict market shares. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

In many studies, choice based conjoint analysis is used to build a market simulator to develop marketing strategies; i.e., shares-of-preference are taken as market share forecasts. However, conjoint data are collected in interview situations, which may differ considerably from real shopping behavior. In this paper, we test the internal and external validity of four commercial choice based conjoint pricing studies including a total of 43 brands. We use conjoint and sales data to assess the relative performance of two modern approaches to estimate conjoint parameters: the segmentation based Latent Class model and the individual level Hierarchical Bayes approach. Our paper confirms previous results of the internal superiority of the Hierarchical Bayes approach. The main result of our investigation is that internal validity does not predict external validity and that Latent Class shows the same real world performance as Hierarchical Bayes. Both models show an average error of 4.2% in market share level prediction and a correlation of 69% between conjoint forecasts and real market shares. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"