Edited by Robin Nunkoo
Chapter 29: Application of partial least squares based structural equation modelling (PLS-SEM) in hospitality research
Theory building in business research requires analytical accuracy and sophistication in research methods (Sarstedt et al., 2014). Nonetheless, the significance of newer analytical methods depends on the researcher’s willingness to learn, adopt, and apply them within the research process (Zahra and Sharma, 2004). A review of the literature shows that traditionally empirical studies in hospitality research used only basic statistical techniques. For instance, Line and Runyan (2012) reviewed the hospitality marketing research published in four top hospitality journals from 2008 to 2010. They stated that among 274 articles published, 103 (37.5 per cent) used some type of descriptive and multivariate analysis (for example, descriptive statistics, analysis of variance, regression, or factor analysis). However, Line and Runyan (2012) also stated that more recently an increase in the usage of advanced statistical tools including structural equation modelling (SEM) can be observed. These findings are confirmed by Yoo et al. (2011) in their assessment of empirical articles published in four top hospitality journals during 2000 and 2009. They revealed that out of a total of 570 empirical studies, 254 (44.5 per cent) of the articles used descriptive analytical methods such as descriptive statistics, t-tests, and cross-tabulation. Not only hospitality, but also other important academic fields such as marketing (Babin et al., 2008), family business research (Sarstedt et al., 2014), operations management (Peng and Lai, 2012), and tourism (Nunkoo et al., 2013) have observed a recent rise in the usage of sophisticated and rigorous quantitative methodologies. Amongst these methodologies, SEM is the most commonly applied method across a variety of academic disciplines such as strategic management, marketing, and psychology over the last few years (Astrachan et al., 2014; Chin et al., 2008; Hair et al., 2011). Lei and Wu (2007) stated that SEM characterizes an advanced version of general linear modelling procedures and is applied to examine whether ‘a hypothesized model is consistent with the data collected to reflect [the] theory’ (ibid., p. 34). In simple terms, SEM is a multivariate analytical tool that is used to test and estimate causal and/or hypothetical relationships among the variables concurrently (Astrachan et al., 2014). Its ability to allow statistical inspection of the relationships among theory-based variables and simultaneously employing confirmatory factor analysis (CFA) and linear regression models has contributed to its widespread application (Hair et al., 2014a). However, this argument holds true for covariance-based SEM (CB-SEM) and not for the partial least squares-based SEM (PLSSEM). CB-SEM is the most extensively applied approach of SEM and therefore many scholars refer to it as SEM (Astrachan et al., 2014). However, Hair et al. (2014b) refer to this argument as naive because PLS-SEM is an advantageous and increasingly applied method to assess structural equation models in different disciplines, including marketing, information systems, strategic management, tourism, and so on (Hair et al., 2012b; Hair et al., 2012a; Ringle et al., 2012). Yet, its use in hospitality research remains at an early stage of development (Ali et al., 2018) where its application is much lower as compared to its application in other disciplines including marketing, management information systems (MIS) and strategic management (see Figure 29.1).
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