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Applications of Partial Least Squares Structural Equation Modeling in Tourism Research: A Methodological Review

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Partial least squares structural equation modeling (PLS-SEM), as an alternative technique to traditional covariance-based structural equation modeling (CB-SEM), offers greater flexibility with regard to data assumptions and could be better harnessed by tourism researchers as a research tool. This article reviews four selected tourism articles using PLS-SEM to highlight the key methodological issues of applying the technique. In so doing, the article provides guidelines for researchers adopting PLS-SEM as a data analysis tool in tourism research, especially when data are multivariate nonnormal and the model includes formative and reflective constructs.


Document Type: Research Article

Publication date: November 1, 2012

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  • Established in 1996, Tourism Analysis is an interdisciplinary journal that provides a platform for exchanging ideas and research in tourism and related fields. The journal aims to publish articles that explore a broad range of research subjects, including, but not limited to, the social, economic, cultural, environmental, and psychological aspects of tourism, consumer behavior in tourism, sustainable and responsible tourism, and effective operations, marketing, and management.

    Tourism Analysis focuses on both theoretical and applied research and strives to promote innovative approaches to understanding the complex and dynamic nature of tourism, its stakeholders, businesses, and its effects on society. The journal welcomes articles on innovative research topics and methodologies beyond the traditional theory-testing sciences, such as robotics, computational sciences, and data analytics.

    Our primary goal is to contribute to the development and advancement of new knowledge in tourism while fostering critical reflections and debates on the radical changes and evolution in tourism among scholars, practitioners, policymakers, and other stakeholders.
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