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The purpose of this study is to review the use of partial least squares-structural equation modeling (PLS-SEM) in the field of hospitality and tourism and thereby to assess whether the PLS-SEM-based papers followed the recommended application guidelines and to investigate whether a comparison of journal types (hospitality vs tourism) and journal qualities (top-tier vs other leading) reveal significant differences in PLS-SEM use.

A total of 206 PLS-SEM based papers published between 2000 and April 2017 in the 19 SSCI-indexed hospitality and tourism journals were critically analyzed using a wide range of guidelines for the following aspects of PLS-SEM: the rationale of using the method, the data characteristics, the model characteristics, the model assessment and reporting the technical issues.

The results reveal that some aspects of PLS-SEM are correctly applied by researchers, but there are still some misapplications, especially regarding data characteristics, formative measurement model evaluation and structural model assessment. Furthermore, few significant differences were found on the use of PLS-SEM between the two fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

To enhance the quality of research in hospitality and tourism, the present study provides recommendations for improving the future use of PLS-SEM.

The present study fills a sizeable gap in hospitality and tourism literature and extends the previous assessments on the use of PLS-SEM by providing a wider perspective on the issue (i.e. includes both hospitality and tourism journals rather than the previous reviews that focus on either tourism or hospitality), using a larger sample size of 206 empirical studies, investigating the issue over a longer time period (from 2000 to April, 2017, including the in-press articles), extending the scope of criteria (guidelines) used in the review and comparing the PLS-SEM use between the two allied fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

The purpose of this study is to explore the impact of service quality on the formation of destination brand equity through customer satisfaction at a winery, from the perspective of Chinese wine tourists.

This study utilized a survey research design. A convenience sample of 311 visitors to a major winery located in Yantai, China, was surveyed, and 265 useable questionnaires were analyzed. To analyze the data, the study used partial least squares structural equation modeling (PLS-SEM).

The results of the study reveal that service quality at a winery is a significant determinant of winery satisfaction among Chinese wine tourists, which in turn affects the brand equity of a wine tourism destination.

This study contributes to the growing body of literature focusing on identity-based branding in the context of wine tourism. As such, this study brings together knowledge of a place branding dimension (i.e. destination brand equity), satisfaction and tourism experience at a winery.

The results suggest that the road to favorable assessments of a wine destination brand (macro level) go through a satisfying experience at a winery (micro level). Therefore, the need to co-create the wine experience through various stakeholders' involvement is crucial for the success of wine tourism.

Extant wine studies often highlight western wine tourists' behavior and examine central behavioral constructs such as winery service quality and satisfaction. This study extends previous research by: (1) investigating the issue from Chinese wine tourists' perspective and (2) integrating the destination brand equity of a wine region to current investigations that commonly focus on the service quality of a winery and wine tourists' satisfaction.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.