A Finite Mixture Approach to Uncover the Heterogeneity in the Relationship Between Visit Motivation and Activity Preferences
Published 13 September 2012
Catarina Marques, Elizabeth Reis, João Menezes
Citations1
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
This research uses a model-based segmentation approach in partial least squares path modeling—the FInite MIXture Partial Least Squares (FIMIX-PLS) methodology—to capture the unobserved heterogeneity in the inner path model estimates of the influence of motivation to visit the Portuguese protected areas on visitors' preferences for different types of activities. Three distinctive visitor segments were identified. Results confirm the assumption of heterogeneity as the relationships differ across segments.
Keywords
Social SciencesBusiness, Management and Accounting
The Journal of Marketing Theory and PracticePLS-SEM: Indeed a Silver Bullet
20,907 Citations2011Joe F. Hair, Christian M. Ringle +1 more
The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
Latent Variable Path Modeling with Partial Least Squares
2,044 Citations1989Jan-Bernd Lohmöller
This latent variable path modeling with partial least squares%0D will actually offer you the smart idea to be successful and will add even more expertise to life and also work much better.
Medical Entomology and ZoologySoft modelling: The Basic Design and Some Extensions
1,266 Citations1982Herman Wold
Annals of Tourism ResearchTesting the push and pull factors
597 Citations1994Muzaffer Uysal, Claudia Jurowski
Marketing ScienceFinite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity
440 Citations1997Kamel Jedidi, Harsharanjeet S. Jagpal +1 more
Schmalenbach Business ReviewCapturing Customer Heterogeneity using a Finite Mixture PLS Approach
293 Citations2002Carsten Hahn, Michael D. Johnson +2 more
Journal of Applied StatisticsTreating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies
227 Citations2010Marko Sarstedt, Christian M. Ringle
Annals of Tourism ResearchDimensions of the social group role in pleasure vacations
182 Citations1981John L. Crompton
Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples
174 Citations2009Christian M. Ringle, Sven Wende +1 more
Journal of Business ResearchFormative indicators: Introduction to the special issue
164 Citations2008Adamantios Diamantopoulos
SSRN Electronic JournalSegmentation for Path Models and Unobserved Heterogeneity: The Finite Mixture Partial Least Squares Approach
37 Citations2006Christian M. Ringle