2003
DOI: 10.2139/ssrn.556358
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Country and Consumer Segmentation: Multi-Level Latent Class Analysis of Financial Product Ownership

Abstract: 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 internatio… Show more

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Cited by 39 publications
(78 citation statements)
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“…MLCA is mostly preferred when people"s multi-item responses or repeated measures are nested (Bijmolt, Paas, and Vermunt, 2004;Vermunt, 2003Vermunt, , 2008.…”
Section: Discussionmentioning
confidence: 99%
“…MLCA is mostly preferred when people"s multi-item responses or repeated measures are nested (Bijmolt, Paas, and Vermunt, 2004;Vermunt, 2003Vermunt, , 2008.…”
Section: Discussionmentioning
confidence: 99%
“…In general, the lower the value of the indicators, the better the model is, because it is more parsimonious and adapts better to the data. Nonetheless, when analysing large samples, the BIC and other information criteria often do not reach a minimum value with increasing number of clusters (Bijmolt et al, 2004). In that case, the percentage of reduction in BIC between competing models must be analysed, and additional criteria, such as entropy, should be used to select the optimal number of clusters.…”
Section: Number Of Clusters Selectionmentioning
confidence: 99%
“…In the non-parametric approach, the specification of the random means is different than in the parametric approach. As described by Bijmolt, Paas and Vermunt (2004), these random means vary across the Level 2, between communities latent classes (labeled CB in the figure). This variation of Level 1 parameters across Level 2 units is the key feature of any multilevel model, and in a multilevel latent class analysis it is this variation that defines the between-community latent classes.…”
Section: Random Effects In a Latent Class Modelmentioning
confidence: 99%