PLS path modeling and evolutionary segmentation

Christian Ringle, Marko Sarstedt, Rainer Schlittgen and Charles R. Taylor

Journal of Business Research, 2013, vol. 66, issue 9, 1318-1324

Abstract: Applications of the partial least squares (PLS) path modeling approach—which have gained increasing dissemination in business research—usually build on the assumption that the data stem from a single population. However, in empirical applications, this assumption of homogeneity is unrealistic. Analyses on the aggregate data level ignore the existence of groups with substantial differences and more often than not result in misleading interpretations and false conclusions. This study introduces a genetic algorithm segmentation method for PLS path modeling (PLS-GAS) that accounts for the critical issue of unobserved heterogeneity in the path model's estimates of relations. The results from computational experiments allow a primary assessment to substantiate that PLS-GAS effectively uncovers unobserved heterogeneity. Significantly distinctive segment-specific path model estimates further foster the development of differentiated results that render more effective recommendations.

Keywords: Partial least squares; Path modeling; Genetic algorithm; Segmentation; Heterogeneity (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:66:y:2013:i:9:p:1318-1324

DOI: 10.1016/j.jbusres.2012.02.031

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