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Introduction
For many years, covariance-based structural equation modeling (CB-SEM) was the dominant method for analyzing complex interrelationships between observed and latent variables. In fact, until around 2010, there were far more articles published in social science journals that used CB-SEM instead of partial least squares structural equation modeling (PLS-SEM). In recent years, the number of published articles using PLS-SEM increased significantly relative to CB-SEM (Hair et al., 2017b). In fact, PLS-SEM is now widely applied in many social science disciplines, including organizational management (Sosik et al., 2009), international management (Richter et al., 2015), human resource management (Ringle et al., 2019), management information systems (Ringle et al., 2012), operations management (Peng and Lai, 2012), marketing management (Hair et al., 2012b), management accounting (Nitzl, 2016), strategic management (Hair et al., 2012a), hospitality management (Ali et al., 2018b) and supply chain management (Kaufmann and Gaeckler, 2015). Several textbooks (e.g., Garson, 2016; Ramayah et al., 2016), edited volumes (e.g., Avkiran and Ringle, 2018; Ali et al., 2018a), and special issues of scholarly journals (e.g., Rasoolimanesh and Ali, 2018; Shiau et al., 2019) illustrate PLS-SEM or propose methodological extensions.
The PLS-SEM method is very appealing to many researchers as it enables them to estimate complex models with many constructs, indicator variables and structural paths without imposing distributional assumptions on the data. More importantly, however, PLS-SEM is a causal-predictive approach to SEM that emphasizes prediction in estimating statistical models, whose structures are designed to provide causal explanations (Wold, 1982; Sarstedt et al., 2017a). The technique thereby overcomes the apparent dichotomy between explanation – as typically emphasized in academic research – and prediction, which is the basis for developing managerial implications (Hair et al., 2019). Additionally, user-friendly software packages are available that generally require little technical knowledge about the method, such as PLS-Graph (Chin, 2003) and SmartPLS (Ringle et al., 2015; Ringle et al., 2005), while more complex packages for statistical computing software environments, such as R, can also execute PLS-SEM (e.g. semPLS; Monecke and Leisch, 2012). Authors such as Richter et al. (2016), Rigdon (2016) and Sarstedt et al. (2017a) provide more detailed arguments and discussions on when to use and not to use PLS-SEM.
The...