Essec\Faculty\Model\Contribution {#2233 ▼
#_index: "academ_contributions"
#_id: "7934"
#_source: array:26 [
"id" => "7934"
"slug" => "7934-capturing-and-treating-unobserved-heterogeneity-by-response-based-segmentation-in-pls-path-modeling-a-comparison-of-alternative-methods-by-computational-experiments 
7934-capturing-and-treating-unobserved-heterogeneity-by-response-based-segmentation-in-pls-path-mode "
"yearMonth" => "2007-07"
"year" => "2007"
"title" => "Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. "
"description" => "ESPOSITO VINZI, V., RINGLE, C.M., SQUILLACCIOTTI, S. et TRINCHERA, L. (2007). <i>Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments</i>. ESSEC Business School. ESPOSITO VINZI, V., RINGLE, C.M., SQUILLACCIOTTI, S. et TRINCHERA, L. (2007). <i>Capturing and Treat "
"authors" => array:4 [
0 => array:3 [
"name" => "ESPOSITO VINZI Vincenzo"
"bid" => "B00067049"
"slug" => "esposito-vinzi-vincenzo"
]
1 => array:1 [
"name" => "RINGLE C.M."
]
2 => array:1 [
"name" => "SQUILLACCIOTTI S."
]
3 => array:1 [
"name" => "TRINCHERA L."
]
]
"ouvrage" => ""
"keywords" => array:1 [
0 => "Unobserved Heterogeneity"
]
"updatedAt" => "2020-12-17 21:00:33"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Documents de travail"
"en" => "Working Papers"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "De nos jours, les problématiques liées à la recherche d'hétérogénéité parmi les unités sont devenues critiques dans le cadre des modèles structurels PLS, notamment dans les sciences sociales. L'hypothèse de base de cette méthode, selon laquelle les données proviennent d'une population unique et homogène, s'avère souvent peu réaliste. Les techniques de classification séquentielles sur les variables manifestes sont fréquemment peu efficaces lorsque l'on veut découvrir l'hétérogénéité dans les estimations des paramètres des modèles structurels. Trois approches statistiques ont été développées comme solutions à ce problème dans le cadre des méthodes PLS. L'objectif de ce papier est de présenter une étude sur des jeux de données simulées, ayant différentes caractéristiques permettant une première évaluation des méthodes décrites. Par ces jeux de données, nous allons illustrer l'intérêt de découvrir l'hétérogénéité latente dans les applications des modèles structurels PLS, décrire les caractéristiques de chaque méthode, en comparer les points forts et les points faibles, et découvrir des aspects méthodologiques qui n'ont pas encore été traités. Ces contributions pourront aider chercheurs et praticiens à mieux comprendre les résultats parfois ambigus des modèles PLS, afin de parvenir à des conclusions analytiques plus efficaces. De nos jours, les problématiques liées à la recherche d'hétérogénéité parmi les unités sont devenues "
"en" => "Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies. Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assu "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-01T07:21:44.000Z"
"docTitle" => "Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. "
"docSurtitle" => "Documents de travail"
"authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, RINGLE C.M., SQUILLACCIOTTI S., TRINCHERA L. <a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, RINGLE C.M., SQUILLACCIOTTI S., T "
"docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo, RINGLE C.M., SQUILLACCIOTTI S., TRINCHERA L.</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2007</span> <span class="document-property-authors">ESPOSITO VINZI Vincenzo, RINGLE C.M., SQUILLACCIOTTI S., TRI "
"keywordList" => "<a href="#">Unobserved Heterogeneity</a>"
"docPreview" => "<b>Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments</b><br><span>2007-07 | Documents de travail </span> <b>Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeli "
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments</a> <a href="#" target="_blank">Capturing and Treating Unobserved Heterogeneity by Response Based Segmen "
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.823265
+"parent": null
}
