Cutting Edge Research Methods in Hospitality and Tourism

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.

This chapter reviews the methods available to hospitality and tourism researchers to perform moderation analysis with continuous variables in partial least squares structural equation modeling (PLS-SEM), with the objective of enhancing understanding and encouraging the use of these techniques in future papers. The product term method is presented first, followed by an empirical example/application in the context of hospitality and tourism. Two extensions, namely the two-stage approach that can help cope with formative and higher-order constructs, and the orthogonalizing approach that can help generate more accurate results and overcome multicollinearity among tourism variables in the presence of a continuous moderator variable, are then presented and discussed. The chapter concludes by presenting guidelines and recommendations for improving the use of interaction effects in analyses of tourism variables, as well as highlighting ongoing developments in both the product term method and PLS-SEM software.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

This chapter examines the nexus between the between tourism growth and income inequality in the top 10 tourist destinations in the world by using the advanced econometric technique namely quantile-on-quantile (QnQ). This approach combines the two approaches, that is, the nonparametric estimation and quantile regression and regresses the quantile of the tourism growth onto income inequality quantiles, thus enabling the effect of the income inequality on across different conditional tourism growth distribution. It also allows to explain a comprehensive picture of the overall interdependence and nonlinear relationship between the examined variables. The result from QnQ approach shows a negative association between income inequality and tourism growth, however, the country-specific analysis shows wide variations within and across different quantiles of variables. Notably, on the one hand, a strong negative association between the variables is found in China, France, Spain, Italy, Russia and the USA implying that tourism expansion minimizes the income inequality. On the other hand, a strong positive association is noted in Germany, Turkey, Mexico and the UK, which means that growth in tourism widens the income inequality. These outcomes provide important policy direction for tourism management in the respective countries.

The application of network theory and social network analysis (SNA) to tourism and hospitality is recent. Nonetheless, several authors have been applying the method contributing to regional planning, local-level tourism networks, tourism policy and governance, innovation, entrepreneurship, knowledge transfer, and learning. This chapter aims to characterize the use of SNA in tourism and hospitality research. Specifically, it intends to: (i) present the framework of SNA in a methodological perspective; (ii) perform a bibliometric analysis of SNA use in tourism and hospitality research; (iii) systematize the dimensions and metrics that researchers can use to apply SNA, namely the relevance for tourism; and (iv) present a case study analyzing tourism innovation networks. This chapter brings important contributions to tourism and hospitality research and practice, by focusing on the theoretical framework and practical application of SNA, providing relevant conceptual and practical knowledge that will empower researchers to use this method in tourism and hospitality studies.

This chapter aims to encourage tourism researchers to widen their scope of method when studying tourist behavior. It does so by debating tracking mobility and planning exercises as meaningful complements in researchers’ method repertoire. Both methods have been found to reflect tourist behavior in a more accurate way than do, for example, surveys and interviews, which rather mirror what tourists say they do, instead of reflecting what they actually do. This chapter presents studies in which tracking mobility and planning exercises have been used, and elaborates on their importance for theory building in future tourism research. Findings show that both methods score high in ecological validity. The chapter gives concrete assistance for future research studies that would like to include methods like these in their examination of tourist behavior. The contribution of this chapter lies in its recommendations to using varied methods, and in its support in the theoretical understanding of tourism.

The aim of this chapter is to review and illustrate a step-by-step guideline in conducting fuzzy-set Qualitative Comparative Analysis (fsQCA) in tourism and hospitality studies. As an emerging method, fsQCA is simultaneously quantitative and qualitative in nature which makes it an appropriate method for social science disciplines including tourism and hospitality area because of complex nature of relationships between multiple variables where theories and models are underdeveloped. Unlike conventional statistical techniques, fsQCA is an asymmetrical analysis technique that provides a holistic view and interrelationships among several conditions using Boolean algebra. The fsQCA analyses produce comprehensive assessment by revealing causal combinations of antecedents to predict an outcome; and identify sufficient configurations (i.e., causal combinations and recipes) and necessary condition/s. By utilizing this method, researchers would be able to produce complex, comprehensive, and robust results.

Necessary conditions represent the factors that cannot be compensated but must be present to aim the desired outcome; if a necessary condition is absent, the outcome will not exist. This logic of necessity causality differs from the conventional logic that has been evaluated by the methods drawing the lines “through the middle of the data” (e.g., regression and SEM). The authors argue that the empirical investigation of necessity causality has been largely ignored in hospitality and tourism literature although the notion of necessary causes for achieving certain outcomes is widespread throughout the studies. Thus, the authors introduce “necessary condition analysis” (NCA) as a suitable analytical method to identify necessary conditions in hospitality and tourism research. This chapter provides details on the underlying logic, key advantages, and an illustrative example of NCA. The chapter concludes by offering a few recommendations for future NCA applications in hospitality and tourism research.

While quantitative survey design represents a default research method in the field of hospitality and tourism, qualitative approaches remain largely sidelined. This is particularly true for netnography, a novel method of scientific enquiry that targets the online interactions of various actors. The present chapter seeks to introduce the netnographic approach, outline its implementation in hospitality and tourism, as well as demarcate it from other methods, such as survey, text mining and content analysis. By giving an overview of recent studies employing netnography, the chapter demonstrates applied examples of ethnographic research online, presents a cross-cultural study on disappointing travel experiences and suggests further research avenues, such as cross-cultural investigation. It concludes by discussing strengths and weaknesses of the netnographic approach. The value of this chapter lies in its reflection of state-of-the-art research in hospitality and tourism based on netnography and the proposition of further directions of research.