Mixed Tree and Spatial Representation of Dissimilarity Judgments

Whereas previous research has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, focussing on the comparative fit at the aggregate level, we investigate whether there is heterogeneity among subjects in the extent to which their dissimilarity judgments are better represented by ultrametric tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data, that is formulated in a stochastic context, and entails a representation and a measurement model component. The latter involves distributional assumptions on the measurement error, and enables estimation by maximum likelihood. The representation component allows dissimilarity judgments to be represented either by a tree structure or by a spatial configuration, or a mixture of both. In order to investigate the appropriateness of tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate tree and spatial models, as well as with mixtures of pure trees and mixtures of pure spaces, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments at the individual level.Multidimensional scaling;tree models;mixture models;dissimilarity judgments

The effect of duration, serialposition and competitive clutter on consumer memory for television advertising: An extension and a test of generalizability

Advertising;Consumer Behaviour;Television;marketing

Rating versus ranking of values in cross-national research

rating;ranking

Extending dynamic segmentation with lead generation: A latent class Markov analysis of financial product portfolios

A recent development in marketing research concerns the incorporation of dynamics in consumer segmentation.This paper extends the latent class Markov model, a suitable technique for conducting dynamic segmentation, in order to facilitate lead generation.We demonstrate the application of the latent Markov model for these purposes using a database containing information on the ownership of twelve financial products and demographics for explaining (changes in) consumer product portfolios.Data were collected in four bi-yearly measurement waves in which a total of 7676 households participated.The proposed latent class Markov model defines dynamic segments on the basis of consumer product portfolios and shows the relationship between the dynamic segments and demographics.The paper demonstrates that the dynamic segmentation resulting from the latent class Markov model is applicable for lead generation.market segmentation;Markov chains;marketing;demography;measurement

Country and Consumer Segmentation: Multi-Level Latent Class Analysis of Financial Product Ownership

The financial services sector has internationalized over the last few decades.Important differences and similarities in financial behavior can be anticipated between both consumers within a particular country and those living in different countries.For companies in this market, the appropriate choice between strategic options and the resulting international performance may critically depend on the cross-national demand structure for the various financial products.Insight into country segments and international consumer segments based on domain-specific behavioral variables will therefore be of key strategic importance.We present a multi-level latent class framework for obtaining simultaneously such country and consumer segments.In an empirical study we apply this methodology to data on ownership of eight financial products.Information is available for fifteen European countries, with a sample size of about 1000 consumers per country.We find that both country segments and consumer segments are highly interpretable.Furthermore, consumer segmentation is related to demographic variables such as age and income.Our conclusions feature implications, both academic and managerial, and directions for future research.market segmentation;finance

A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods

We compare three alternative Maximum Likelihood Multidimensional Scaling methods for pairwise dissimilarity ratings, namely MULTISCALE, MAXSCAL, and PROSCAL in a Monte Carlo study.The three MLMDS methods recover the true con gurations very well.The recovery of the true dimensionality depends on the test criterion (likelihood ratio test, AIC, or CAIC), as well as on the MLMDS method. The three MLMDS methods t the dissimilarity data equally well.The methods are relatively robust against violations of their distributional assumptions. MULTISCALE outperforms PROSCAL and MAXSCAL with respect to computation time.In a separate Monte Carlo study, it is shown that the MLMDS methods frequently converge to local optima, especially if a random start is used.Rational starts, however, turn out to provide a satisfactory solution for the local optima problem.Implications for researchers intending to apply MLMDS are provided