Internet research using partial least squares structural equation modeling (PLS-SEM)

Abstract

Internet research using partial least squares structural equation modeling (PLS-SEM) Innovation and diffusion of PLS-SEM Since Wold (1974) developed the PLS algorithm more than 40 years ago, the method has evolved considerably, particularly in recent years. Indeed, numerous researchers have contributed to expanding awareness and applications of what is now known as PLS-SEM. Today, PLS-SEM belongs to the common portfolio of multivariate analysis methods (Hair, Black, Babin and Anderson, 2018). But the road to its widespread adoption among researchers and practitioners was not always straightforward and sometimes bumpy. Figure 1 visualizes some key publications that contributed to the development and diffusion of PLS-SEM.PLS-SEM was standing in the shadows of the more popular covariance-based SEM (CB-SEM) method for many years. A likely reason was Jöreskog's development of the LISREL software, which led to the early widespread adoption of CB-SEM. In contrast, without software, PLS-SEM remained relatively unknownnotwithstanding early research comparing the methods and offering guidance for their choice ( Jöreskog and Wold, 1982;Dijkstra, 1983). This pattern continued for almost 20 years despite several fundamental advantages of PLS-SEM. The primary advantages of PLS-SEM include the relaxation of "hard" distributional assumptions required by the maximum likelihood method used to estimate models using CB-SEM, and PLS-SEM's ability to easily estimate much more complex models with smaller sample sizes ( Jöreskog and Wold, 1982). Lohmöller (1984) introduced the LVPLS software to estimate causal models and later published the comprehensive PLS-SEM textbook Latent Variable Path Modeling with Partial Least Squares (Lohmöller, 1989). Then in the early 1990s, Falk and Miller (1992) published their Primer on Soft Modeling. Despite these developments, little interest was shown in the method until Chin (1998) introduced the method to business research in his seminal article in Marcoulides's (1998) edited volume Modern Methods for Business Research, followed by his release of PLS-Graph ( 2003)the first software package with a graphical user interface for PLS-SEM analyses. While the availability of PLS-Graph (Chin, 2003) accelerated the use of PLS-SEM, particularly in management information systems (Ringle et al., 2012;Hair, Hollingsworth, Randolph and Chong, 2017), applications grew exponentially following the release of the free SmartPLS 2 software (Ringle et al., 2005) that included many analysis options and quickly became the most popular PLS-SEM software (e.g. Ali et al., 2018;Nitzl, 2016;. About the same time, Tenenhaus et al.'s (2005) seminal article was released, which summarized PLS-SEM's statistical properties and introduced it to a broader audience of methodological researchers. Also, international conferences such as the International Symposium on PLS and Related Methods started gaining momentum. In this regard, the 2005 PLS conference in Barcelona (Spain) certainly shaped the field by forming a strong and collaborat...

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“…The primary advantages of PLS-SEM include the relaxation of normal distributional assumptions required by PLS-SEM's ability to easily estimate much more complex models with smaller sample sizes (Shiau and Chau, 2016;Hair et al, 2019;Khan et al, 2019;Shiau et al, 2019). PLS-SEM is more suitable for this study under the following situations: when the research objective is exploratory research for theory development, when the analysis is for a prediction perspective; when the structural model is complex, when the structural model includes one or more formative constructs; when distribution is lack of normality, and when research requires latent variable scores for consequent analyses (Shiau and Chau, 2016;Hair et al, 2019;Khan et al, 2019;Shiau et al, 2019). The above reasons provide supports to consider the PLS as an appropriate SEM method for a study.…”

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