The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications

Every discipline needs to frequently review the use of multivariate analysis methods to ensure rigorous research and publications. Even though partial least squares structural equation modeling (PLS-SEM) is frequently used for studies in strategic management, this kind of assessment has only been conducted by Hulland (1999) for four studies and a limited number of criteria. This article analyzes the use of PLS-SEM in thirty-seven studies that have been published in eight leading management journals for dozens of relevant criteria, including reasons for using PLS-SEM, data characteristics, model characteristics, model evaluation and reporting. Our results reveal several problematic aspects of PLS-SEM use in strategic management research, but also substantiate some improvement over time. We find that researchers still often do not fully make use of the method's capabilities, sometimes even misapplying it. Our review of PLS-SEM applications and recommendations on how to improve the use of the method are important to disseminate rigorous research and publication practices in the strategic management discipline.

Introduction

Research into the strategic management discipline recognized relatively early the potential of structural equation modeling (SEM) to empirically test theories and conceptual models. Indeed, by the late 1980s (e.g., Birkinshaw et al., 1995; Cool et al., 1989; Fornell et al., 1990; Govindarajan, 1989; Johansson and Yip, 1994), the strategic management discipline acknowledged the different and, in many research situations, advantageous properties of variance-based partial least squares SEM (PLS-SEM; Lohmöller, 1989; Wold, 1982) in comparison with the alternative covariance-based SEM (CB-SEM; Jöreskog, 1978; Jöreskog, 1982) method to estimate structural equation models. In short, CB-SEM and PLS-SEM are different but complementary statistical methods for SEM, whereby the advantages of the one method are the disadvantages of the other, and vice versa (Jöreskog and Wold, 1982).

PLS-SEM is particularly appealing when the research objective focuses on prediction and explaining the variance of key target constructs (e.g., strategic success of firms) by different explanatory constructs (e.g., sources of competitive advantage); the sample size is relatively small and/or the available data is non-normal; and, when CB-SEM provides no, or at best questionable, results (Hair et al., 2011; Hair et al., 2012; Henseler et al., 2009; Reinartz et al., 2009). Moreover, formatively measured constructs are particularly useful for explanatory constructs (e.g., sources of competitive advantage) of key target constructs, such as success (i.e., success factor studies; Albers, 2010). PLS-SEM is the preferred alternative over CB-SEM in these situations, since it enables researchers to create and estimate such models without imposing additional limiting constraints. PLS-SEM applications in strategic management often address topics such as long-term survival of firms (Agarwal et al., 2002; Cool et al., 1989); performance of global firms (Birkinshaw et al., 1998; Birkinshaw et al., 1995; Devinney et al., 2000; Johansson and Yip, 1994; Robins et al., 2002); knowledge sourcing and collaborations (Gray and Meister, 2004, Im and Rai, 2008; Jarvenpaa and Majchrzak, 2008; Purvis et al., 2001); and, cooperation of firms (Doz et al., 2000; Fornell et al., 1990; Sarkar et al., 2001).

Despite recognizing the SEM method and, more specifically, the advantageous features of PLS-SEM in existing studies, their number, as we show in this study, is considerably smaller than in other disciplines such as marketing (Hair et al., 2012) and management information systems (MIS) (Ringle et al., 2012). Researchers in management and especially strategic management seem to predominantly rely on first-generation multivariate analysis techniques (e.g., factor analysis, multiple linear regression, etc.) in their empirical studies, and thus may miss opportunities that researchers in other disciplines frequently exploit by using the second-generation SEM technique. Potential reasons may be the restrictive assumptions of the CB-SEM method (e.g., sample size requirements, data distribution, model specification) and the improper use of PLS-SEM in a few early applications (Hulland, 1999). More recent articles, however, conclude that PLS can indeed be a “silver bullet” in many research situations–if correctly applied (Hair et al., 2011).

As with other statistical methods, users can only benefit from the unique properties of PLS-SEM if they understand the principles underlying the method, apply it properly, and report the results correctly. Due to the complexities involved in using PLS-SEM, systematic assessments on how the technique has been applied in prior research can provide important guidance and, if necessary, opportunities for course correction in future applications. But despite the importance of this research question, corresponding assessments are very limited. Hulland (1999) provided an assessment of four studies in the strategic management area, showing the PLS-SEM technique had been applied with considerable variability in terms of authors appropriately handling conceptual and methodological issues.

Many disciplines frequently review the methods used to disseminate rigorous research and publication practices. While reviews of CB-SEM usage have been carried out across many disciplines in business research (e.g., Babin et al., 2008, Baumgartner and Homburg, 1996, Brannick, 1995; Garver and Mentzer, 1999; Shah and Goldstein, 2006, Shook et al., 2004, Steenkamp and Trijp, 1991), recent reviews of PLS-SEM usage cover only accounting (Lee et al., 2011), management information systems (Ringle et al., 2012), and marketing (Hair et al., 2012). Against this background, an update and extension of Hulland's (1999) case study-based assessment of PLS-SEM specific to the strategic management discipline seems timely and warranted.

Like all statistical methods, “PLS-SEM requires several choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.” (Hair et al., 2012, p. 415). The objective of this article is to provide recommendations for the use of PLS-SEM in strategic management research. For explanations of the PLS-SEM method itself, the reader is referred to recent articles (Chin, 2010; Hair et al., 2011; Henseler et al., 2012; Henseler et al., 2009) and a forthcoming text (Hair et al., 2013). Toward this aim, we review thirty-seven empirical applications of PLS-SEM in eight leading journals publishing strategic management research, and analyze these applications according to several key dimensions, including reasons for using PLS-SEM, data characteristics, model characteristics, model evaluation and reporting. We contrast the findings in strategic management research with standards applied in other disciplines. Where possible, we indicate best practices as guidelines for future applications of PLS-SEM in strategic management and suggest avenues for further research involving the technique.

Our results reveal several problematic aspects of PLS-SEM use in strategic management research, but also substantiate some improvement over time. Researchers still often do not fully utilize available analytical potential, and sometimes incorrectly apply methods in top-tier strategic management journals. For this reason, our review of PLS-SEM applications and guidelines on how to properly use the method are important to disseminate rigorous research and publication practices in the strategic management discipline.