PLS path modeling: a confirmatory approach to study tourism technology and tourist behavior

Tobias Müller, Florian Schuberth, Jörg Henseler

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Purpose: As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites. Design/methodology/approach: The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used. Findings: PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful. Originality/value: To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results.

Original languageEnglish
Pages (from-to)249-266
Number of pages18
JournalJournal of Hospitality and Tourism Technology
Volume9
Issue number3
DOIs
Publication statusPublished - 1 Oct 2018

Keywords

  • Artifact
  • Construct modeling
  • Guidelines
  • Partial least squares path modelling
  • Test for overall model fit

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