The Pathmox approach for PLS path modeling segmentation

Modeling has often failed to meet expectations, mostly because of the difficulty of comprehending relationships within phenomena and expressing them in mathematical models. Reality is frequently too complex to be reflected in a single model. This is often the case of marketing research, where variab...

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Vydáno v:Applied stochastic models in business and industry Ročník 32; číslo 4; s. 453 - 468
Hlavní autoři: Lamberti, Giuseppe, Aluja, Tomas Banet, Sanchez, Gaston
Médium: Journal Article Publikace
Jazyk:angličtina
Vydáno: Blackwell Publishing Ltd 01.07.2016
Témata:
60 Probability theory and stochastic processes
60H Stochastic analysis
62 Statistics
62H Multivariate analysis
Anàlisi multivariable
Anàlisi multivariant
Classificació AMS
Construction
Criteria
Decision trees
Decisions
Estadística matemàtica
Heterogeneity
Least squares method
Matemàtiques i estadística
Mathematical models
model comparison
Modelling
Multivariate analysis
Mètodes estadístics
partial least squares path modeling
Processos estocàstics
segmentation
segmentation trees
Stochastic processes
Àrees temàtiques de la UPC
ISSN:1524-1904, 1526-4025
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Shrnutí:Modeling has often failed to meet expectations, mostly because of the difficulty of comprehending relationships within phenomena and expressing them in mathematical models. Reality is frequently too complex to be reflected in a single model. This is often the case of marketing research, where variables relating to socioeconomics or psychographics constitute potential sources of heterogeneity. In such cases, the assumption of ‘one model fits all’ is unrealistic and may lead to inaccurate decisions. Thus, heterogeneity is a major issue in modeling. Once a model has been fitted to a complete data set that fulfills all validation criteria, it is difficult to establish whether it is valid for the whole population or it is merely an average artifact from several sub‐populations. The purpose of this paper is to present the Pathmox approach to deal with heterogeneity in partial least squares path modeling. The idea behind Pathmox is to build a tree of path models that have look‐alike structure as a binary decision tree, with different models for each of its nodes. The split criterion consists of an F statistic comparing two structural models. In order to ensure the suitability of the split criterion, a simulation study was conducted. Finally, we have applied Pathmox to a survey that measured Satisfaction among Spanish mobile phone operators. Results suggest that the Pathmox approach performs adequately in detecting partial least squares path modeling heterogeneity. Copyright © 2016 John Wiley & Sons, Ltd.
Bibliografie:istex:D1190D4C97F4A940C3DE60D4B606F7B3578E6B35
ark:/67375/WNG-68GZHKJT-H
ArticleID:ASMB2168
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2168