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oai:recercat.cat:2117/4485782026-01-29T04:43:55Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Aluja Banet, Tomàs author Lamberti, Giuseppe author Sanchez Trujillo, Gaston author 2013 In most cases, path modeling data come from surveys or researches that contain more information (i.e., observed heterogeneity) than is used for the path models definition. For instance, in many marketing studies like those of consumer satisfaction, it is usual to collect socio-demographic variables and psycho-demographic variables such as age, gender, social-status, or consumers’ habits that take no part in the path model but that can be extremely useful for segmentation purposes. In 2009, Gastón Sánchez introduced the PATHMOX methodology to incorporate the available external variables to identify different segments. The algorithm solves this problem by building a binary tree to detect those segments present in the population that cause the heterogeneity. The F-global test, based on the Fisher’s F for testing the equality of two regression models, is adapted and used, as a splitting criterion, to discover whether two structural models calibrated from two different segments (i.e., two successors of a node), can be considered to be different. However PATHMOX does not identify which of the block or variables indicators are responsible for the heterogeneity. In this article we propose to extend the PATHMOX methodology to test the equality of every endogenous equation of the structural model in order to compare all path coefficients of the structural model estimated in two segments. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica Fisher's F Heterogeneity Models comparison PATHMOX PLSPM Segmentation Extending the PATHMOX approach to detect which constructs differentiate segments