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Abstract
The use of partial least squares path modelling (PLSPM) has escalated in the areas of marketing, management, information systems, and organizational behaviour. Researchers in tourism and hospitality have to date been reluctant to use this approach, instead, focusing on covariance-based structural equation modelling (CBSEM) techniques conducted in Lisrel or AMOS. This article highlights the main differences between CBSEM and PLSPM and describes the advantages of PLSPM with regard to (1) testing theories and analyzing structural relationships among latent constructs; (2) dealing with sample size limitations and non-normal data; (3) analyzing complex models that have 'formative' and 'reflective' latent constructs; and (4) analyzing models with higher-order molar and molecular constructs. These advantages are put into practice using examples from a tourism context. The paper demonstrates the application of PLSPM in the case of destination competitiveness, and illustrates how this approach could enhance the theoretical and practical usefulness of tourism modelling. This paper also presents a step-by -step guide to PLSPM analysis, providing directions for future research designs in tourism. This presents valuable knowledge for researchers, editors, and reviewers with recommendations, rules of thumb, and corresponding references for appropriately applying and assessing structural models.
Keywords: Quantitative methods, structural equation modelling, partial least squares, tourism, rormative indicators.
Introduction
Structural equation modelling (SEM) is now widely used in business and tourism research (e.g., Babin et al., 2008; Assaker et al., 2010; Hallak et al., 2012). SEM allows for the analysis of latent variable(s) at the observation level (measurement/outer model), and to also test simultaneous relationships between latent variables at the theoretical level (structural/inner model) (Bollen, 1989). It can be used to examine research questions related to causal relationships among a set of latent factors each measured by one or more manifest [observed] variables within a single comprehensive method. There are two main approaches to SEM analysis 1) covariance-based SEM analysis (CBSEM)(Jöreskog, 1978, 1993), and 2) component-based, or partial least squares SEM (also referred as partial least squares path modellingPLSPM) (Wold, 1982, Esposito Vinzi et al., 2010).
The two approaches serve different research purposes. CBSEM typically employs a fall information maximum likelihood estimation process that yields parameter estimates that minimize the discrepancy between the implied and the observed covariance matrices. This approach examines the 'goodness-of-fit' of the...