Use of PLS

7/27/2019 Use of PLS

1/21

The Use of Partial Least SquaresStructural Equation Modelingin Strategic Management

Research: A Review of PastPractices and Recommendationsfor Future Applications

Joseph F. Hair, Marko Sarstedt, Torsten M. Pieper andChristian M. Ringle

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 methods capabilities, sometimes even misapplying it. Our review of PLS-SEM

applications and recommendations on how to improve the use of the method areimportant to disseminate rigorous research and publication practices in the strategic

management discipline.

2012 Elsevier Ltd. All rights reserved.

Long Range Planning -- (2012) ---e--- http://www.elsevier.com/locate/lrp

0024-6301/$ - see front matter 2012 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.lrp.2012.09.008

Please cite this article in press as: Hair J. F., et al., The Use of Partial Least Squares Structural Equation Modeling in Strategic

Management Research: A Review of Past Practices and Recommendations for Future Applications, Long Range Planning (2012),

http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://www.elsevier.com/locate/lrphttp://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://www.elsevier.com/locate/lrphttp://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008http://dx.doi.org/10.1016/j.lrp.2012.09.008


2/21

IntroductionResearch into the strategic management discipline recognized relatively early the potential of struc-tural equation modeling (SEM) to empirically test theories and conceptual models. Indeed, by thelate 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, inmany research situations, advantageous properties of variance-based partial least squares SEM

(PLS-SEM; Lohmoller, 1989; Wold, 1982) in comparison with the alternative covariance-basedSEM (CB-SEM; Joreskog, 1978; Joreskog, 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(Joreskog and Wold, 1982).

PLS-SEM is particularly appealing when the research objective focuses on prediction and explain-ing the variance of key target constructs (e.g., strategic success of firms) by different explanatoryconstructs (e.g., sources of competitive advantage); the sample size is relatively small and/or theavailable 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, forma-

tively measured constructs are particularly useful for explanatory constructs (e.g., sources of com-petitive 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 enablesresearchers to create and estimate such models without imposing additional limiting constraints.PLS-SEM applications in strategic management often address topics such as long-term survivalof 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 inother disciplines such as marketing (Hair et al., 2012) and management information systems(MIS) (Ringle et al., 2012). Researchers in management and especially strategic managementseem to predominantly rely on first-generation multivariate analysis techniques (e.g., factor analy-sis, multiple linear regression, etc.) in their empirical studies, and thus may miss opportunities thatresearchers 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 re-quirements, data distribution, model specification) and the improper use of PLS-SEM in a few earlyapplications (Hulland, 1999). More recent articles, however, conclude that PLS can indeed be a sil-

ver bullet in many research situationse

if correctly applied (Hair et al., 2011).As with other statistical methods, users can only benefit from the unique properties of PLS-SEMif they understand the principles underlying the method, apply it properly, and report the resultscorrectly. Due to the complexities involved in using PLS-SEM, systematic assessments on how thetechnique 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 thisresearch question, corresponding assessments are very limited. Hulland (1999)provided an assess-ment of four studies in the strategic management area, showing the PLS-SEM technique had beenapplied with considerable variability in terms of authors appropriately handling conceptual andmethodological issues.

Many disciplines frequently review the methods used to disseminate rigorous research and pub-lication practices. While reviews of CB-SEM usage have been carried out across many disciplines inbusiness research (e.g.,Babin, Hair and Boles, 2008; Baumgartner and Homburg, 1996; Brannick,1995; Garver and Mentzer, 1999;Shah and Goldstein, 2006; Shook et al., 2004; Steenkamp and vanTrijp, 1991), recent reviews of PLS-SEM usage cover only accounting (Lee et al., 2011),

2 The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research





3/21

management information systems (Ringle et al., 2012), and marketing (Hair et al., 2012). Againstthis background, an update and extension ofHullands (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 readeris referred to recent articles (Chin, 2010; Hair et al., 2011; Henseler et al., 2012; Henseleret 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 strategicmanagement research, and analyze these applications according to several key dimensions, in-cluding reasons for using PLS-SEM, data characteristics, model characteristics, model evalu-ation and reporting. We contrast the findings in strategic management research withstandards applied in other disciplines. Where possible, we indicate best practices as guidelinesfor future applications of PLS-SEM in strategic management and suggest avenues for furtherresearch 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 avail-able analytical potential, and sometimes incorrectly apply methods in top-tier strategic manage-ment journals. For this reason, our review of PLS-SEM applications and guidelines on how toproperly use the method are important to disseminate rigorous research and publication practicesin the strategic management discipline.

Review of PLS-SEM researchOur review includes studies published in the Academy of Management Journal, Administrative

Science Quarterly, Journal of Management, Journal of Management Studies, Long Range Planning,Management Science,Organization Scienceand Strategic Management Journal, which were selectedas representative of the leading journals in management (e.g., Furrer et al., 2008; Raisch andBirkinshaw, 2008). These eight journals were searched for the 30-year period from 1981 through2010, to identify all empirical applications of PLS-SEM. We accessed the ABI/INFORM Com-plete, EBSCO Business Source Complete, ISI WEB OF KNOWLEDGE and JSTOR databases aswell as online versions of the journals, using the keywords partial least squares and PLSto search the full text of articles previously published. To identify studies eligible for inclusionin the review, the list was then examined independently by two professors proficient in the tech-nique. In this process, conceptual papers on methodological aspects (e.g., Echambadi et al., 2006)

and empirical studies that mentioned having used PLS-SEM for validation purposeswithout re-porting concrete results (e.g., Tiwana, 2008; Zott and Amit, 2007) were removed.1 Using thesecriteria, we identified thirty-seven studies (Table 1) containing 112 PLS-SEM model estimations.The number of model estimations was larger than the number of studies reviewed since severalarticles estimated multiple models using different set-ups and/or data originating from differentsources, countries, or years. The Strategic Management Journalpublished the largest number ofPLS-SEM studies of the reviewed journals. In contrast,Long Range Planningdid not include a sin-gle PLS-SEM study in the period of time reviewed.

Figure 1shows the (cumulative) number of studies between 1985 (the first year an applicationwas found) and 2010 (the bars indicate the number of studies per year and the line indicates the

cumulative number of studies). It is apparent that the use of PLS-SEM has significantly increasedover time. Regressing the number of studies on the linear effects of time yields significant model(F 14.25; p < 0.01) and time effects (t 3.76; p < 0.01). A quadratic effect of time, however,is not significant (t 0.35; p > 0.10). Therefore, the use of PLS-SEM in strategic management

1 The coding agreement on the relevant articles was 94 percent, which compares well with the study byHair et al. (2012).

Long Range Planning, vol -- 2012 3





4/21

research has grown linearly as a function of time, which is typical for early introduction stages ofa new research techniques diffusion. In the field of marketing, in contrast, the use of PLS-SEM hasaccelerated substantially over time (Hair et al., 2012).

In light of these results (i.e., a linear growth of the number of studies per year), we applied theBass diffusion model (Bass, 1969) to investigate how the application of the PLS-SEM method instrategic management has developed over time. Fitting the Bass model to the data yielded a co-efficient of innovation (p) of 0.11 and a coefficient of imitation (q) of 0.23. The value ofp q

falls within boundaries commonly encountered in prior research, and the ratio ofp/q suggeststhat the use of PLS-SEM can be regarded as slightly contagious in terms of diffusion(Mahajan et al., 1995).

Critical issues in PLS-SEM researchThe thirty-seven articles included in our review were analyzed according to five key criteriapreviously used to evaluate critical issues and common misapplications in research involvingPLS-SEM (Hair et al., 2012). The criteria used to analyze the studies and model estimationswere: 1) reasons for using PLS-SEM, 2) data characteristics, 3) model characteristics, 4) model

evaluation, and 5) reporting. We also distinguish between two time periods to assess whetherthe use of PLS-SEM has changed between these periods. Using Chins (1998), Chin andNewsteds (1999)andHullands (1999)PLS-SEM articles published in the late 1990s as seminalmilestones, we subsequently differentiate between studies published before 2000 (sixteen stud-ies with sixty-one models) and studies published in 2000 and beyond (twenty-one studies withfifty-one models).2

Reasons for using PLS-SEM

Given that PLS-SEM has only recently attracted increased interest in business research disciplines, itrequires a more detailed explanation of the rationale leading to the selection of this method. Of the

thirty-seven studies, a total of thirty-two (86.5%) provided explicit reasons for using PLS-SEM.Moreover, the proportion of studies providing explicit reasons for using PLS-SEM remained rela-tively consistent before 2000 (15 studies) and from 2000 onward (17 studies).

Table 1. PLS-SEM studies in the top management journals

Academy of Management Journal Management Science Strategic Management Journal

Avolio et al., 1999

Cording et al., 2008

Groth et al., 2009

Mezner and Nigh, 1995

Shamir et al., 1998

Fichman and Kemerer, 1997

Fornell et al., 1985

Fornell et al., 1990

Graham et al., 1994

Gray and Meister, 2004

Im and Rai, 2008

Mitchell and Nault, 2007

Venkatesh and Agarwal, 2006

Xu et al., 2010

Birkinshaw et al., 1995

Birkinshaw et al., 1998

Cool et al., 1989

Delios and Beamish, 1999

Doz et al., 2000

Gruber et al., 2010

Johansson and Yip, 1994

Olk and Young, 1997

Robins et al., 2002

Sarkar et al., 2001

Tsang, 2002

Administrative Science Quarterly

House et al., 1991

Howell and Higgins, 1990

Journal of Management

Ashill and Jobber, 2010

Shea and Howell, 2000

Organization Science

Jarvenpaa and Majchrzak, 2008

Milberg et al., 2000

Nambisan and Baron, 2010

Purvis et al., 2001

Staples et al., 1999

Journal of Management Studies

Barthelemy and Quelin, 2006

Lee and Tsang, 2001

Van Riel et al., 2009

2 In the following, we consistently use the term studies when referring to the thirty-seven journal articles and the termmodels when referring to the 112 PLS-SEM applications in these articles.






5/21

The four most frequently used reasons for using PLS-SEM are, in order of importance, non-normal data (22 studies, 68.8%), small sample size (17 studies, 53.1%), formative measures (10studies, 31.3%), and focus on prediction (10 studies, 31.3%).3 A comparison of studies publishedbefore 2000 with those published in 2000 and onward shows a fairly consistent pattern. Apart fromgradual shifts in the order of importance, the prevalence of the reasons for using PLS-SEM remainsrelatively consistent over time, with non-normal data, formative measures, small sample size, andfocus on prediction being the most prevalent reasons in recent years. This observation is consistent

with patterns observed in marketing research (Hair et al., 2012).Wold (1985, p. 589) originally designed PLS-SEM for research situations that are simulta-

neously data-rich and theory-primitive. He envisioned a discovery-oriented process d a dia-logue between the investigator and the computer (Wold, 1985, p. 590). Rather than commit toa specific model a priori and frame the statistical analysis as a hypothesis test, Wold imagineda researcher estimating numerous models in the course of learning something about the dataand about the phenomena underlying the data (Rigdon, in press). However, only seven studies(21.9%) indicate theory development as a rationale for using PLS-SEM and one study (3.1%)mentions exploratory research purposes. It was surprising, therefore, to find that practically allstudies argue their case in confirmatory terms, which likely reflects an academic bias in favor

of presenting findings in a confirmatory context (Greenwald et al., 1986). While this practice un-derlines an apparent misconception in the appropriate use of PLS-SEM shown also in other fieldssuch as marketing (Hair et al., 2012), one should recognize that CB-SEM, on the other hand, israrely used in a truly confirmatory sense. In fact, research reality is that models estimated withCB-SEM rarely fit initially and modifying models in an effort to yield what reviewers and editorsmight perceive accurate in terms of model fit is typical. Choices in statistical methods often in-volve tradeoffs, andWold (1985) recognized both strengths and limitations in his intentionallyapproximate technique. It is important for modern users to consider these same issues whenmaking the choice between PLS-SEM versus CB-SEM and when applying a particular technique.

Data characteristicsA primary advantage of PLS-SEM over CB-SEM is that it works particularly well with small samplesizes (e.g.,Chin and Newsteds, 1999;Reinartz et al., 2009). It is not surprising, therefore, that theaverage sample size of the studies included in our review (5% trimmed mean 154.9) is

Figure 1. Applications of PLS-SEM in Management Journal Publications over Time

3 The total of the percentages exceeds 100 percent because various studies mentioned multiple reasons for the use of PLS-SEM.






6/21

considerably lower than that reported in previous reviews of CB-SEM studies (mean 246.4) (Shahand Goldstein, 2006). Noteworthy, however, is a significant difference in the sample sizes of studiespublished before 2000 (5% trimmed mean 95.4) and in 2000 and beyond (5% trimmedmean 207.1). The sample size of studies in 2000 and beyond has more than doubled comparedto studies published before 2000, but is still below the average sample size reported in comparablePLS-SEM reviews in marketing (5% trimmed mean 211.3) (Hair et al., 2012) and MIS (5%

trimmed mean 238.1) (Ringle et al., 2012). Similarly, the median sample size across all modelsincluded in our review (median 83) is considerably lower than that reported in theHair et al.(2012)marketing (median 159) andRingle et al. (2012)MIS (median 198) studies. This trendis also apparent for models with less than 100 observations (58 of 112 models in total).

Overall, PLS-SEM studies in the strategic management discipline rely on much smaller samplesizes compared to other fields. Even though from a statistical standpoint PLS-SEM can be usedwith smaller sample sizes, this observation is not without problems for at least two reasons. First,relying on small sample sizes tends to capitalize on the idiosyncrasies of the sample at hand. All elsebeing equal, the more heterogeneous the underlying population, the larger the required sample sizenecessary to adequately reflect the population and to yield accurate estimates. Researchers need to

be aware that no statistical method can offset the fact that smaller sample sizes go hand in handwith higher sampling error, especially when the population and the sample are heterogeneous incomposition. Second, the biasing effects of small sample sizes are likely to be accentuated whendata are extremely non-normal. Even though PLS-SEM is well-known to be robust when usedon highly skewed data (e.g., Cassel et al., 1999;Reinartz et al., 2009), such data inadequacies inflatebootstrapping standard errors, thereby reducing the statistical power of the method. Consideringthe tendency of PLS-SEM to underestimate inner model relationships (Hui and Wold, 1982),non-normal data may represent a concern in combination with small sample sizes. It is thereforeof concern that none of the studies included in our review reported a check of the skewness andkurtosis of the data underlying the analyses.

What might have contributed to the misconception of the universal suitability of PLS-SEM tohandle small sample sizes is the widespread application of the ten times rule of thumb(Barclay et al., 1995;Hair et al., 2013). This rule recommends a minimum sample size of ten timesthe maximum number of independent variables in the outer model and inner model. This approachis equivalent to using a sample size of ten times the largest number of formative indicators used tomeasure any construct in the outer model (in other words, the number of indicators per formativeconstruct) or ten times the largest number of structural paths directed at a particular latent con-struct in the inner model. Most models (93; 83.0%) meet this rule of thumb. The nineteen models(17.0%) that did not meet this criterion are on average 26.7 percent short of the recommendedsample size. Over time, 14 out of 61 models published before 2000 did not meet the ten timesrule of thumb, whereas only five out of 51 studies published in 2000 and beyond did not meetthe ten times rule of thumb, revealing a significant difference (p < 0.10) and indicating that re-searchers have become more aware of sample size issues in PLS-SEM in recent years.

While this rule of thumb may provide a broad estimate of minimum sample size requirementsfor the use of PLS-SEM (Hair et al., 2011), it needs to be pointed out that it does not consider effectsize, reliability, the total number of indicators, and other issues likely affecting the statistical powerof the PLS-SEM method. Since sample size recommendations in PLS-SEM essentially build on theproperties of ordinary least squares regression, researchers can revert to more differentiated rules ofthumb such as those provided byCohen (1992)in his statistical power analyses for multiple regres-sion models. For instance, when the maximum number of independent variables in the outer andinner models is five, one would need ninety-one observations to achieve a statistical power of 80percent, assuming a medium effect size and a 5 percent a-level. Cohens (1992) statistical poweranalyses generally match those fromReinartz et al. (2009) in their comparison of CB-SEM andPLS-SEM, provided that the outer model has an acceptable quality in terms of outer loadings(i.e., loadings should be above the common threshold of 0.70). Likewise,Hair et al. (2013)provideminimum sample size recommendations, based on regression-based power analyses.






7/21

Another benefit of PLS-SEM is its ability to process nominal, ordinal, interval, and ratio scaled vari-ables (Fornell and Bookstein, 1982;Haenlein and Kaplan, 2004;Reinartz et al., 2009). Of the 112models included in our review, only six models (5.4%) included categorical variables with morethan two modalities. A considerably larger number (40 models; 35.7%) used binary variables. Theuse of categorical variables in PLS-SEM should be approached with caution as the number of binaryindicators and the position of the corresponding construct in the path model may restrict the use of

categorical variables. For instance, an issue occurs when a binary single-item is used to measure anendogenous latent variable representing a choice situation. In the final inner approximation of thePLS-SEM algorithm, the endogenous latent variable is regressed on the predecessor variables. Asthe construct becomes its measure, however, and thus has only two values (choice versus no choice),a basic premise of the ordinary least squares regression is violated. Researchers should be acquainted,therefore, with the fundamental steps in the PLS-SEM algorithm (e.g., Henseler et al., 2012;Henseleret al., 2009) to avoid model set-ups that are problematic in this respect.

Model characteristics

Table 2 provides an overview of model characteristics of the PLS-SEM studies included in our

review. On average, the number of latent variables in path models is 7.5, which is similar to the7.9 and 8.1 reported in prior PLS-SEM reviews in marketing (Hair et al., 2012) and MIS (Ringleet al., 2012), but much higher than in comparable studies in a CB-SEM context (e.g.,Baumgartner and Homburg, 1996; Shah and Goldstein, 2006). This increased model complexityis also mirrored in the higher number of inner model relationships being analyzed(mean 10.4), which has increased significantly (p 0.10) over time (9.4 before 2000 and 11.6thereafter). Likewise, models incorporate a relatively large average number of indicators (27 inour review), which is much higher than generally encountered in CB-SEM (e.g., Baumgartnerand Homburg, 1996;Shah and Goldstein, 2006). This finding is not due to a larger average numberof indicators per construct but rather a result of the relatively larger number of constructs used inthe models. Specifically, the average numbers of indicators per reflective construct is 3.4 and 3.6 forformative constructs, both of which have increased significantly over time (p 0.01). The relativelysmall difference in the number of indicators per reflective and formative construct is striking, giventhat formative indicators should capture the entire content domain of the construct under consid-eration (Diamantopoulos et al., 2008). This especially holds for PLS-SEM which is restricted to es-timating formative constructs without an error term (Diamantopoulos, 2011).

Taken jointly, these results suggest that researchers benefit from the ability of PLS-SEM to usefewer data points in estimating complex models with many constructs, inner model relationshipsand indicator variables. In contrast, CB-SEM quickly reaches its limits in similar situations. For ex-ample, a complex model such as that described inStaples et al. (1999)with fifteen constructs, mea-sured by a total seventy indicator variables has 2,383 degrees of freedom. Hence, the statisticalpower for the test of model fit based on root-mean-square error of approximation usinga 0.05, 0 0.05, and a 0.08, would be 1.000 (MacCallum et al., 1996). Therefore, roundingerrors would likely be detected at the 10th place and the model probably would fail even with sim-ulated data (Haenlein and Kaplan, 2004).

An important characteristic of PLS-SEM is that it readily incorporates both reflective and forma-tive measures. Drawing on this characteristic, half of the models used a combination of both reflec-tively and formatively measured latent variables (56 models; 50.0%), and the number increasedsignificantly (p < 0.05) over time. Very few models were composed of solely reflectively measuredlatent variables (12 models; 10.7%) or solely formatively measured latent variables (12 models;10.7%). One quite surprising finding was that thirty-two models (28.6%) did not specify the mea-

surement mode for the constructs at all, despite the ongoing and rather vibrant debate on measure-ment specification (Bagozzi, 2007; Diamantopoulos, 2006; Diamantopoulos et al., 2008;Diamantopoulos and Siguaw, 2006; Diamantopoulos and Winklhofer, 2001; Edwards andBagozzi, 2000;Howell et al., 2007). It is encouraging, however, to see that the number of modelslacking an outer model description decreased significantly (p


8/21

Our review revealed that 76 of 112 PLS path models (67.9%) used single-item measures. WhilePLS-SEM readily incorporates single-item measures, researchers need to be vigilant since PLS-SEMrequires reasonable outer model quality (i.e., a sufficient number of indicators per construct andhigher loadings) for the technique to provide acceptable parameter estimates under a restrictedsample size (Reinartz et al., 2009). Apart from that, in terms of predictive validity, recent researchshows that single-item measures perform as well as multi-item scales (Diamantopoulos et al., 2012)only under very specific conditions. As Diamantopoulos et al. (2012, p. 446) point out, opting forsingle-item measures in most empirical settings is a risky decision as the set of circumstances thatwould favor their use is unlikely to be frequently encountered in practice. Despite their ease ofimplementation in PLS-SEM, researchers should follow Diamantopoulos et al.s (2012) guidelineand only consider single items (rather than a multi-item scale) when 1) small sample sizes are pres-ent (i.e., N 0.90),and4) the itemsare semantically redundant. This especially holds since PLS-SEM focuses on explaining the variancein the endogenous variables, thereby placing emphasis on prediction.

Table 2. Descriptive statistics for model characteristics

Criterion Results

(n [ 112)

Proportion (%) Before 2000

(n [ 61)

2000 onward

(n [ 51)

Number of latent variables

Mean 7.5 e 7.0 8.1

Median 6.0 6.0 6.0

Range (2; 31) (3; 15) (2; 31)

Number of inner model path relations

Mean 10.4 e 9.4 11.6*

Median 9.0 7.0 10.0

Range (2; 39) (3; 39) (2; 31)

Mode of outer models

Only reflective 12 10.7 5 7

Only formative 12 10.7 11*** 1

Reflective and formative 56 50.0 24 32**

Not specified 32 28.6 21* 11Number of indicators per reflective constructa

Mean 3.4 e 2.3 4.3***

Median 3.0 2.0 4.0

Range (1; 10) (1; 5) (1; 10)

Number of indicators per formative constructb

Mean 3.6 e 2.6 4.7***

Median 2.5 2.0 5.0

Range (1; 10) (1; 6) (1; 10)

Total number of indicators in models

Mean 27.0 e 19.7 35.7***

Median 19.0 18.0 23.0

Range (7; 114) (7; 70) (9; 114)

Number of models with single-item constructs 76 67.9 45 31

*** (**, *) indicates a significant difference between before 2000 and 2000 onward at a 1% (5%, 10%) significance level;results based on independent samples t-tests and (one-tailed) Fishers exact tests (no tests for median differences).

a Includes only models that have been marked as including reflective indicators (nbefore 2000 29; n2000 onward 39).b Includes only models that have been marked as including formative indicators (nbefore 2000 35; n2000 onward 33).




http://-/?-http://-/?-http://-/?-http://-/?-


9/21

Model evaluation

Outer model evaluationTo assess the extent to which constructs are appropriately measured by their indicator variables (in-dividually or jointly), researchers need to differentiate between reflective and formative measure-ment approaches (Diamantopoulos et al., 2008), with each approach relying on a different set ofcriteria. Reflective measures are commonly evaluated through criteria of internal consistency,such as Cronbachs alpha and composite reliability (Hair et al., 2011). The internal consistency per-spective that underlies reflective outer model evaluation cannot be universally applied to formativemodels since formative measures do not necessarily covary (e.g., Diamantopoulos and Winklhofer,2001). Thus, any attempt to purify formative indicators based on correlation patterns can have ad-verse consequences for construct measures content validity (e.g., Diamantopoulos and Riefler,2011; Diamantopoulos and Siguaw, 2006). This especially holds for PLS-SEM which assumesthat the formative indicators fully capture the content domain of the construct under consideration(Diamantopoulos, 2011). Therefore, instead of considering measures such as composite reliabilityor AVE, researchers have to revert to other criteria to assess formatively measured constructs. Theseinclude, for example, the indicator weights, their significance and collinearity among the indicators.Our review assesses whether and how authors evaluated reflective (Table 3, Panel A) and formativemeasures (Table 3, Panel B) and whether the standards used for evaluating reflective outer modelswere applied to formative models.

Reflective outer modelsDespite the importance of providing evidence for the reflective measures reliability and validity inorder to adequately interpret inner model estimates (Henseler et al., 2009), authors oftentimes donot engage in these analyses. For instance, of the sixty-eight models reporting the use of reflectivelymeasured constructs, fifty-three models (77.9%) reported outer loadings, thereby indirectly speci-fying indicator reliability. Internal consistency reliability was reported in 38 of 68 models (55.9%)

using reflectively measured constructs and is significantly more prevalent recently (p 0.05). Mostmodels report composite reliability values, either exclusively (17 models; 25.0%) or in conjunctionwith Cronbachs Alpha (14 models; 20.6%). A relatively small number (seven models, 10.3%) re-port only Cronbachs Alpha. Since the PLS-SEM algorithm emphasizes indicators with strong re-liability levels more, composite reliability is generally regarded as the more appropriate criterionto establish internal consistency reliability as compared to Cronbachs Alpha. The latter is generallyperceived as a lower bound of reliability whereas composite reliability is the upper bound.

In a PLS-SEM context, Tenenhaus et al. (2005) suggest using the above-mentioned reliabilitymeasures to assess the unidimensionality of a set of manifest variables hypothesized to reflect anunderlying construct. However,Sahmer et al. (2006) show that Cronbachs Alpha and composite

reliability are both inadequate to assess whether a set of reflective manifest variables is unidimen-sional or not. Instead, researchers should apply the Kaiser-Gutman criterion (seeKarlis et al., 2003for a modified version) or Revelles (1979) coefficient beta.

Convergent validity was assessed in 30 of 68 models (44.1%) with the AVE being by far the mostprevalent measure. Furthermore, a total of twenty-seven models (39.7%) report some measure ofdiscriminant validity. Specifically, thirteen models (19.1%) exclusively relied on the Fornell-Larcker criterion (Fornell and Larcker, 1981), which compares the AVE of each construct withthe squared inter-construct correlations. This criterion has been applied significantly (p 0.05)more frequently in recent years. Similarly, thirteen models (19.1%) reported cross loadings only,a more liberal form of assessing discriminant validity, which has also been reported significantly

(p 0.01) more frequently in recent years.

Formative outer modelsOur analysis shows 68 of 112 models (60.7%) included at least one formatively measured con-struct. Indicator weights, the most common criterion to assess formative measures, were reported






10/21

in 26 of 68 models (38.2%). A formative indicators weight represents the partialized effect of theindicator on its corresponding construct, controlling for the effect of all other indicators of thatconstruct (Cenfetelli and Basselier, 2009). Therefore, weights are generally smaller than loadings(i.e., the zero-order bivariate correlation between the indicator and the associated construct) andthus need to be assessed for their significance through resampling procedures such as bootstrap-ping or jackknifing. However, only three models (4.4%) reported standard errors, significancelevels, t-values, or p-values for indicator weights, all of which were published in more recent

years.Multicollinearity among indicators represents an important concern in assessing formative mea-

sures since it can inflate bootstrap standard errors and therefore trigger type II errors (Cenfetelliet al., 2009). Surprisingly, however, with one exception multicollinearity assessment is almost en-tirely missing in the models included in our review. Since the weights of formative indicators are

Table 3. Evaluation of outer models

Empirical test

criterion in PLS-SEMaNumber of models

reporting (n [ 68)

Proportion

reporting (%)

Before 2000

(n [ 29)

2000 onward

(n [ 39)

Panel A: Reflective outer models

Indicator reliability Indicator loadings 53 77.9 22 31

Internal consistency

reliability

Only composite

reliability

17 25.0 3 14**

Only Cronbachs Alpha 7 10.3 0 7**

Both 14 20.6 0 14***

Convergent validity AVE 29 42.7 2 27

Other 1 1.5 0 1

Discriminant

validity

Only Fornell-Larcker

criterion

13 19.1 2 11**

Only cross-loadings 13 19.1 0 13***

Other 1 1.5 0 1

Empirical test

criterion in PLS-SEMaNumber of models

reporting (n [ 68)

Proportion

reporting (%)

Before 2000

(n [ 35)

2000 onward

(n [ 33)

Panel B: Formative outer models

e Reflective criteria used

to evaluate formative

constructs

17 25.0 11 6

Indicators absolute

contribution

to the construct

Indicator weights 26 38.2 21*** 5

Significance

of weights

Standard errors,

significance levels,

t-values/p-values for

indicator weights

3 4.4 0 3

Multicollinearity Only VIF/tolerance 0 0.0 e e

Only condition index 0 0.0 e e

Both 1 1.5 0 1

*** (**, *) indicates a significant difference between before 2000and 2000 onward respectively, at a 1% (5%, 10%)significance level; results based on (one-tailed) Fishers exact tests.

a

Single-item constructs were excluded from this analysis.




http://-/?-http://-/?-http://-/?-http://-/?-


11/21

usually smaller than those of reflective indicators, multicollinearity can cause misinterpretations ofthe indicators relevance for the construct domain (Diamantopoulos and Winklhofer, 2001). Thelack of multicollinearity assessment is an important oversight and should be a reporting require-ment in future studies that include formative measures.

Overall, formative outer model assessment in the management discipline leaves much to be de-sired. Researchers neglect fundamental principles of outer model evaluation such as significance

testing and multicollinearity assessment, casting measurement quality and thus the studies findingsinto doubt. More severely, a total of seventeen models (25.0%) with formatively measured con-structs inappropriately used reflective criteria to evaluate the corresponding measures. This mistakehas been made consistently over time. In fact, it is surprising that six of 33 models (18.2%) usedreflective criteria to evaluate formative measures in recent years since the discussion about the eval-uation of formative measures has taken place not only in the marketing and MIS disciplines (e.g.,Diamantopoulos and Winklhofer, 2001; Petter et al., 2007), but also in the management domain(e.g.,Podsakoff et al., 2006;Podsakoff et al., 2003).

Researchers should place more emphasis on evaluating formative measures by applying estab-lished as well as more recently proposed guidelines (Hair et al. (2013) provide an overview of

the state-of the-art in formative measurement evaluation). For instance, researchers can examinethe correlation between the formatively measured construct and a reflective measure of thesame construct. This analysis, also known as redundancy analysis (Chin, 1998), indicates the val-idity of the designated set of formative indicators in tapping the construct of interest. Similarly, onan indicator-level,Hair et al. (2011)argue that researchers should also consider a formative indi-cators absolute contribution to its construct; that is, the information an indicator provides with-out considering any other indicators (Cenfetelli and Basselier, 2009). The absolute contribution isgiven by the formative indicators outer loading, which is always provided along with the indicatorweights in PLS-SEM. When an indicators outer weight is insignificant but its outer loading is high(i.e., above 0.50), the indicator should be interpreted as absolutely important but not as relatively

important. In this situation, the indicator would generally be retained. However, when an indicatorhas an insignificant weight and the outer loading is below 0.50, the researcher should decidewhether to retain or delete the indicator by examining its theoretical relevance and potential con-tent overlap with other indicators of the same construct. Only if the theory-driven conceptualiza-tion of the construct strongly supports retaining the indicator (e.g., by means of expertassessment), should it be kept in the formative outer model. Finally, if the outer loading is low,insignificant, and there is no empirical support for the indicators relevance regarding providingcontent to the formative index, it should be considered a strong candidate for removal.

It is important to note that eliminating formative indicators that do not meet threshold levelsin terms of their contribution has, from an empirical perspective, almost no effect on the param-eter estimates when re-estimating the model. Nevertheless, formative indicators should never bediscarded simply on the basis of statistical outcomes. In this context, Einhorns ( 1972, p. 378)conclusion that just as the alchemists were not successful in turning base metal into gold, themodern researcher cannot rely on the computer to turn his data into meaningful and valuablescientific information still holds true today. Therefore, before removing an indicator from theformative outer model, researchers need to carefully check its relevance from a content validitypoint of view.

Inner model evaluationUnlike CB-SEM, PLS-SEM does not optimize a unique global scalar function. The lack of a globalscalar function and the consequent lack of global goodness-of-fit measures is traditionally con-

sidered a major drawback of PLS-SEM. It is important to recognize that the term fit has dif-ferent meanings in the contexts of CB-SEM and PLS-SEM. Fit statistics for CB-SEM are derivedfrom the discrepancy between the empirical and the model-implied (theoretical) covariance ma-trix, whereas PLS-SEM focuses on the discrepancy between the observed (in the case of manifestvariables) or approximated (in the case of latent variables) values of the dependent variables and






12/21

the values predicted by the model in question. As a consequence, researchers using PLS-SEM relyon measures indicating the models predictive capabilities to judge the models quality (Table 4).

The central criterion in this respect is the R2 which 90 of the 112 models (80.4%) report. Onlytwelve models (10.7%) report effect size (f2), which considers the relative impact of a particular ex-ogenous latent variable on an endogenous latent variable by means of changes in the R2 (Cohen,1988). But in recent years significantly more models (p < 0.01) reported the effect size f2. The

cross-validated redundancy measure Q2

, a common sample re-use technique (Geisser, 1974;Stone, 1974), allows for assessing a models predictive validity. More precisely, Q2 represents a syn-thesis of cross-validation and function fitting and is a recommended assessment criterion for PLS-SEM applications (Wold, 1982). It is notable that only three models (2.7%) reported this criterionall of which appeared in recent years. Similar to thef2 value, the Q2 value can also be used to assessthe predictive relevance of an individual construct to the model (labeled q2). But none of themodels reported predictive relevance of individual constructs.

Overall, the results point to an apparent problem in researchers reporting when evaluating PLS-SEM-based path models.Table 4summarizes the review of the inner model evaluations. The factthat reviews of PLS-SEM use in marketing research (Hair et al., 2012) and MIS (Ringle et al., 2012)

showed similarly problematic results is not encouraging. In accordance with PLS-SEMs statisticalproperties, researchers should make broader use of relevant criteria to assess the models predictivecapabilities.

Table 4. Evaluation of inner models

Criterion Empirical test

criterion in PLS-SEM

Number of models

reporting (n [ 112)

Proportion

reporting (%)

Before 2000

(n [ 61)

2000 onward

(n [ 51)

Endogenous constructsexplained variance

R2

90 80.4 45 45*

Effect size f2 12 10.7 0 12***

Predictive relevance Cross-validated

redundancy Q23 2.7 0 3*

Relative predicted

relevance

q2 0 0.0 e e

Overall goodness-of-fit GoF 0 0.0 e e

Path coefficients Absolute values 107 95.5 59 48

Significance of

path coefficients

Standard errors,

significance levels,

t-values, p-values

107 95.5 59 48

Confidence intervals e 0 0.0 e e

Total effects e 12 10.7 9 3

Criterion Empirical test

criterion in PLS-SEM

Number of studies

reporting (n [ 37)

Proportion

reporting (%)

Before 2000

(n [ 16)

2000 onward

(n [ 21)

Observed

heterogeneity

Categorical moderator 8 21.6 5 3

Continuous moderator 2 5.4 0 2

Unobserved

heterogeneity

Response-based

segmentation techniques(e.g., FIMIX-PLS)

0 0.0 e e

*** (**, *) indicates a significant difference between before 2000 and 2000 onward at a 1% (5%, 10%) significance level;results based on (one-tailed) Fishers exact tests.






13/21

As PLS-SEM aims at maximizing the explained variance of the dependent variables, the modelquality criteria described above cannot indicate fit or a lack thereof in a CB-SEM sense. Thisalso holds for the goodness-of-fit index (GoF) whichTenenhaus et al. (2004) originally proposedas a means to validate a path model globally. Henseler and Sarstedt (in press) recently challengedthe usefulness of the GoF both conceptually and empirically. For instance, in a simulation study, theauthors show that the GoF is not able to separate valid models from invalid ones. Since the GoF is

also not applicable to formative outer models and does not penalize overparameterization efforts,researchers are advised to maintain the current practice and not apply this measure.

After assessing the predictive quality of an inner model, researchers examine the standardizedpath coefficients to analyze whether the hypothesized relationships among constructs are reflectedby the data. The significance of these coefficients should be assessed using resampling procedures(Henseler et al., 2009). Of the 112 models in our review, 107 (95.5%) reported path coefficients andtheir significance. Unfortunately, researchers only interpreted the parameter coefficients t-value.But there are other approaches to assess a coefficients stability if researchers consider the completebootstrap output. The more superior techniques involve construction of (bias-corrected) bootstrapconfidence intervals (e.g.,Gudergan et al., 2008;Sarstedt et al., 2011) which d unlike regular con-

fidence intervalsd

may be asymmetrically distributed around the mean estimate from the full data.This is a valuable property as the forced symmetry of regular confidence intervals may have negativeinfluence on estimation accuracy and statistical power (Efron and Tibshirani, 1986).

It is important to note that the examination of inner model estimates, both in terms of valuesand significance, is not restricted to direct relationships. Rather, researchers can also examine totaleffects; that is, the sum of direct and indirect effects. Interpretation of total effects is particularlyuseful in studies with the objective of exploring the differential impact of different driver constructson a criterion construct via several mediating variables (Albers, 2010). In our review, 12 of 112models (10.7%) reported total effects.

Recently, methodological research has devoted considerable interest to the identification and

treatment of heterogeneous data structures within a PLS-SEM framework. As a result of theseefforts, researchers can apply a broad range of procedures to take observed heterogeneity into ac-count. These include approaches to modeling continuous moderating variables (Henseler andFassott, 2010;Henseler and Chin, 2010) as well as categorical moderating variables (i.e., Sarstedtet al., 2011), which only a few studies in our review made use of (Table 4). However, variables caus-ing heterogeneous data structures are not always observed (known). As a result, researchers havedeveloped approaches that facilitate identifying and treating unobserved heterogeneity (Sarstedt,2008), which none of the studies included. Among the various approaches that have been proposed,Hahn et al.s(2002)finite mixture PLS (FIMIX-PLS) procedure is considered a primary approach.By fitting mixture regression models on the data, FIMIX-PLS effectively detects heterogeneity ininner model estimates and enables the derivation of latent classes. More recently, Ringle et al.(in press)proposed a combination of PLS-SEM with genetic algorithms (PLS-GAS), which haveroutinely been used in various business research disciplines to handle complex optimization prob-lems. The authors show that PLS-GAS effectively uncovers heterogeneity and allows for a more pre-cise segmentation of the data compared to prior approaches. Further assessment, especially withregard to (formative) outer models is still pending but preliminary analyses show that the approachholds considerable promise for segmentation tasks in PLS-SEM.

Reporting

A fundamental issue in the use of any statistical technique relates to the reporting of the choice ofcomputational options as these can have a significant bearing on the analysis results. This also holds

for the use of PLS-SEM, which leaves the researcher with several degrees of freedom when runningthe algorithm or using complementary techniques such as jackknifing or bootstrapping. Unfortu-nately, reporting practices in management research are lacking in several respects.

For instance, while practically all studies used resampling techniques such as jackknifing andbootstrapping, only 20 of 37 studies (54.1%) explicitly mentioned their use. Furthermore, only






14/21

10 of the 20 studies (50.0%) reported the concrete parameter settings (on the upside, settings werereported significantly more frequently in recent years; p < 0.05).

Detailed reporting on resampling procedures is critical, however, in PLS-SEM studies. For exam-ple, the different bootstrapping sign change options yield considerably different results when pa-rameter estimates are close to zero. Specifically, when the no sign change option indicatesa nonsignificant relationship, the individual sign changes option is more likely to indicate a relation-

ship as significant. Likewise, a misspecification of bootstrap sample size vis-a-vis the original sample

size and bootstrap cases can significantly bias the results. For instance, using a small number ofbootstrap samples, particularly when the original sample size is much larger, will considerably de-flate standard errors. Considered jointly, bootstrap parameter settings leave the researcher withmany degrees of freedom in their analysis of results, making their concrete reporting a must.

None of the studies provides information on the use of the PLS-SEM algorithm; that is, on theweighting scheme used and the abort criterion. While in practice the choice of weighting schemehas little bearing on the analysis results, researchers have to consider that the schemes are not univer-sally applicable to all kinds of model set-ups. For instance, the centroid scheme must not be used whenestimating higher order models (Hair et al., 2012;Hair et al., 2013). Similarly, although the PLS-SEM

algorithm usually converges (Henseler, 2010), it may not do so when the stop criterion is extremelylow (e.g., 1020). Therefore, researchers should provide the (maximum) number of iterations to assesswhether or not the PLS-SEM algorithm converged before reaching the pre-specified stop criterion.

Reporting of the software used (in accordance with the license agreements) can provide someinformation in this respect as the different programs rely on different default settings. However,only 18 of 37 studies (48.7%) specified which software was used for model estimation. While recentstudies reported software package information significantly more often (p < 0.01) than earlier stud-ies, the number is still relatively small. Of the eighteen studies in our review providing softwarepackage information, ten studies used PLS Graph (Chin, 2003), five studies used LVPLS(Lohmoller, 1987), two studies used SmartPLS (Ringle et al., 2005), and one study used PLS-

GUI (Li, 2005).Surprisingly, and in contrast to previous reviews of PLS-SEM studies in the marketing literature(Hair et al., 2012), twenty-five studies of 37 studies (67.6%) reported the covariance/correlationmatrix for the indicator variables, which enables readers to replicate and validate the analytical find-ings. The number of studies reporting the covariance/correlation matrix is significantly higher(p < 0.01) in recent than in previous years.

In summary, researchers should pay closer attention to reporting technical aspects when usingPLS-SEM. The fact that the PLS-SEM algorithm practically always converges might tempt re-searchers to put less emphasis on the technicalities of the analyses. For the reasons mentionedabove, however, this practice needs to be changed.

Observations and conclusionsToday, our understanding of PLS-SEM is much more developed as a result of recent analyses thatcompare the methods properties with those of CB-SEM (e.g.,Chin and Newsteds, 1999;Reinartzet al., 2009) or newly emerging techniques for estimating structural equation models (Henseler,2012;Hwang et al., 2010;Lu et al., 2011). While the comparative studies approaches and researchaims differ, collectively they show the benefits of PLS-SEM lie in its ability to identify relationshipsamong latent variables in the model when they in fact exist in the population (i.e., its statisticalpower), especially in situations when sample sizes are small. This property makes PLS-SEM partic-ularly useful in exploratory research settings. PLS-SEM also facilitates more flexibility in estimating

complex models and those incorporating formative indicators, situations in which the uses of clas-sical covariance-based techniques often reach their limits (Hair et al., 2011). These characteristicsmake PLS-SEM particularly useful for strategic management research that often deals with smallsample sizes, complex models, and formative measures especially when analyzing the sources ofcompetitive advantage.






15/21

As with any statistical technique, PLS-SEM requires researchers to make several choices that ifmade incorrectly can have substantial consequences on the validity of the results. Based on priormethodological discussions, our own work in the area, and particularly the present review of pre-vious applications of PLS-SEM in the Academy of Management Journal, Administrative ScienceQuarterly, Journal of Management, Journal of Management Studies, Long Range Planning, Manage-ment Science,Organization ScienceandStrategic Management Journal, we offer the following general

guidelines to future users of the technique.First, more careful thought should be given to data characteristics. Even though PLS-SEM per-

forms well with small samples and non-normal data, researchers should not be careless in imple-menting these advantages. Small sample sizes and skewed data easily increase sampling error

yielding inflated bootstrap standard errors. When this occurs, the techniques statistical power isreduced, offsetting one of PLS-SEMs major advantages.

Second, researchers should pay closer attention to model specification issues. For example, usingformatively measured constructs in PLS-SEM implies that the indicators capture the entire con-struct domain (or at least major parts of it). Similarly, using single-item measures is not withoutproblems. Considering that single-item measures significantly lag behind multi-item scales in terms

of predictive validity, their use should be avoided in PLS-SEM in most situations, especially in lightof the techniques predictive focus.Third, researchers should make greater use of model evaluation criteria, especially when assessing

the quality of formatively measured constructs. Our review shows that current practice leaves muchto be desired in this regard, casting doubt on the validity of some of the measures. Similarly, re-searchers should make use of the full range of criteria available to assess the models predictive ca-pabilities, such as the cross-validated redundancy index or the effect size. Researchers need tounderstand that these measures are by definition not indicative of model fit in a covariance-based sense, and any effort to interpret them as such should clearly be rejected.

Lastly, it is important to note that some of the criticism stated in the review might be misplaced

since there is no indication whether some of the admonished reporting elements have been dis-carded in the course of the revision process. However, every study should provide the readerwith sufficient information to fully assess the quality of the research as well as replicate the results(Stewart, 2009). To improve reporting practices of PLS-SEM studies in strategic management andother disciplines, the aspects inTable 5must be clearly addressed by providing details on 1) dataused and their characteristics, 2) model characteristics, 3) PLS-SEM algorithm settings and softwareused, and 4) evaluation criteria results of inner and outer models. Acknowledging the trade-off be-tween page restrictions and number of studies being published, journal editors should neverthelessdevote more journal space to fundamental reporting elements to improve the readers confidence inthe results.

Besides basic PLS-SEM analyses, researchers can take advantage of a much larger set of meth-odological extensions of the PLS-SEM method, ranging from evaluation techniques such as blind-folding (e.g., Chin, 1998) and confirmatory tetrad analysis (e.g., Gudergan et al., 2008) toimportance-performance analyses (e.g.,Rigdon et al., 2011;Volckner et al., 2010), PLS-SEM multigroup analyses (Henseler et al., 2009;Sarstedt et al., 2011) and response-based segmentation ap-proaches such finite mixture-PLS (e.g., Hair et al., 2011;Rigdon et al., 2010). However, PLS-SEMstudies in strategic management very infrequently exploit such potentials and, thus, miss oppor-tunities to further substantiate the appropriateness of findings and to improve their analyses. Forinstance, uncovering unobserved heterogeneity represents a key area of concern every PLS-SEMstudy and should be addressed in the results evaluation (Hair et al., 2012) while theimportance-performance adds a second dimensions (i.e., performance) to the analysis of resultsand thereby leads to richer and further differentiated findings. Hence, PLS-SEM in strategic man-agement must more strongly focus on presenting state of the art applications.

As this and previous reviews of PLS-SEM use (Hair et al., 2012;Lee et al., 2011;Ringle et al.,2012) have shown, PLS-SEM has already had a substantial impact on empirical research in severaldisciplines. However, to further increase its usefulness in empirical research, the problem areas and






16/21

Table 5. Issues, Implications and Recommendations in the Application of PLS-SEM

Issue Implications Recommendation References

Sample size

requirements

Model generally identified but low

sample sizes deflate statistical power,

especially when the outer model

quality is poor and data are

highly skewed

Use ten times rule as rough

estimate of the required

sample size; for a more

thorough assessment,

consider Cohens (1992)

statistical power tables or

carry out distinct power

analyses

Hair et al. (2013);

Reinartz et al. (2009)

Non-normal

data

PLS-SEM is very robust when used

on extremely non-normal data;

however, bootstrapping standard

errors may become inflated,

especially when the sample size

is small, yielding lower levelsof statistical power

Examine the degree to

which data are non-normal

using Q-Q plots,

Kolmogorov-Smirnov

or Shapiro-Wilk tests

Cassel et al. (1999),

Mooi and

Sarstedt (2011);

Reinartz et al. (2009)

Use of formative

measures

PLS-SEM readily accommodates

formative measures but assumes

that the indicators cover the

entire domain of the construct

Establish content validity by

determining how well the

indicators cover the entire

(or at least major aspects)

of the constructs content

domain. Also see formative

outer model assessment

Diamantopoulos (2011)

Use of categorical

variables

PLS-SEM can generally

accommodate categorical variables

but their inclusion depends on their

position in the outer model

and the number of indicators

used per construct

Consider the PLS-SEM

algorithm to assess the

suitability of the model

set-up

Henseler (2010)

Henseler et al. (2009)

Use of single-item

measures

Leads to poor outer model quality

in terms of predictive validity;

aggravates PLS-SEMs tendency

to underestimate inner model

relationships

Avoid using single-item

measures; should only be

considered when practical

considerations

(e.g., population/sample

is limited in size) require

their use

Diamantopoulos

et al. (2012)

Reflective outer

model assessment

Establishing reliability and validity

of reflective measures is a

precondition for interpreting

inner model estimates

Fully make use of popular

criteria to assess the

reflective outer models

Henseler et al. (2009)

Hair et al. (2012,2013)

Formative outer

model assessment

Criteria used for reflective outer

model evaluation are not

universally applicable to

formative measures

Consider established criteria

(weights and their

significance, multicollinearity)

as well as more recently

proposed methods

(e.g., redundancy analysis,

indicator loadings) to evaluateformative measures quality

Hair et al. (2011,2013)

Cenfetelli and

Basselier (2009)






17/21

recommendations about how to improve the practice of using PLS-SEM should be carefully takeninto account in future applications of the technique.

ReferencesAgarwal, R., Sarkar, M.B., Echambadi, R., 2002. The conditioning effect of time on firm survival: an industry

life cycle approach. Academy of Management Journal 45 (5), 971e994.

Albers, S., 2010. PLS and success factor studies in marketing. In: Esposito Vinzi, V., Chin, W.W., Henseler, J.,Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing

and Related Fields. Springer, Berlin et al., pp. 409e425.Babin, B.J., Hair, J.F., Boles, J.S., 2008. Publishing research in marketing journals using structural equations

modeling. Journal of Marketing Theory and Practice 16 (4), 279e285.

Bagozzi, R.P., 2007. On the meaning of formative measurement and how it differs from reflective measure-ment: comment on Howell, Breivik, and Wilcox (2007). Psychological Methods 12 (2), 229e237.

Barclay, D.W., Higgins, C.A., Thompson, R., 1995. The partial least squares approach to causal modeling: per-sonal computer adoption and use as illustration. Technology Studies 2 (2), 285 e309.

Bass, F.M., 1969. A new product growth for model consumer durables. Management Science 15 (5), 215e227.

Baumgartner, H., Homburg, C., 1996. Applications of structural equation modeling in marketing and con-sumer research: a review. International Journal of Research in Marketing 13 (2), 139e161.

Birkinshaw, J., Hood, N., Jonsson, S., 1998. Building firm-specific advantages in multinational corporations:

the role of subsidiary initiative. Strategic Management Journal 19 (3), 221e242.

Birkinshaw, J., Morrison, A., Hulland, J., 1995. Structural and competitive determinants of a global integra-tion strategy. Strategic Management Journal 16 (8), 637e655.

Brannick, M.T., 1995. Critical comments on applying covariance structure modeling. Journal of Organiza-tional Behavior 16 (3), 201e213.

Cassel, C., Hackl, P., Westlund, A.H., 1999. Robustness of partial least-squares method for estimating latentvariable quality structures. Journal of Applied Statistics 26 (4), 435e446.

Cenfetelli, R.T., Bassellier, G., 2009. Interpretation of formative measurement in information systems research.MIS Quarterly 33 (4), 689e708.

Table 5 (continued)

Issue Implications Recommendation References

Evaluation of the

inner model

As PLS-SEM aims at maximizing

the explained variance of the

dependent variables, model

quality criteria cannot indicatefit or a lack thereof in a

CB-SEM sense.

Consider the full range of

criteria to assess the models

predictive capabilities

(i.e., R2

, Q2

). Bootstrappinganalyses should also be used

to construct (bias-corrected)

confidence intervals

Hair et al. (2012,2013)

Consideration of

heterogeneous

data structures

Analyses on the aggregate

data level can seriously bias

the results if the data structure

is heterogeneous

Consider observable

moderating variables or, in

case of unobserved

heterogeneity, apply latent

class approaches such as

FIMIX-PLS or PLS-GAS

Sarstedt et al. (2011)

Rigdon et al. (2010)

Ringle et al. (in press)

Rigdon et al. (2011)

Reporting of

computational

settings

Reporting computational

settings is highly important

because specific

(combinations of) settings

can be considered conservative

or liberal

Fully report the following

elements:

-Standard PLS-SEM algorithm:

weighting scheme, maximum

iterations, abort criterion

-Resampling procedures:

sign change option, number

of bootstrap cases and samples

Hair et al. (2012)

Hair et al. (2013)






18/21

Chin, W.W., 1998. The partial least squares approach to structural equation modeling. In: Marcoulides, G.A.(Ed.), Modern Methods for Business Research. Lawrence Erlbaum, Mahwah, pp. 295e358.

Chin, W.W., 2003. PLS Graph 3.0. Soft Modeling Inc., Houston.Chin, W.W., 2010. How to write up and report PLS analyses. In: Esposito Vinzi, V., Chin, W.W., Henseler, J.,

Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing

and Related Fields. Springer, Berlin et al, pp. 655e690.Chin, W.W., Newsted, P.R., 1999. Structural equation modeling analysis with small samples using partial least

squares. In: Hoyle, R.H. (Ed.), Statistical Strategies for Small Sample Research. Sage, Thousand Oaks, pp.307e341.

Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences, second ed. Lawrence Erlbaum Associ-

ates, Hillsdale, NJ.

Cohen, J., 1992. A power primer. Psychological Bulletin 112 (1), 155e159.Cool, K., Dierickx, I., Jemison, D., 1989. Business strategy, market structure and risk-return relationships:

a structural approach. Strategic Management Journal 10 (6), 507e522.

Devinney, T.M., Midgley, D.F., Venaik, S., 2000. The optimal performance of the global firm: formalizing andextending the integration-responsiveness framework. Organization Science 11 (6), 674e695.

Diamantopoulos, A., 2006. The error term in formative measurement models: interpretation and modeling

implications. Journal of Modelling in Management 1 (1), 7e17.

Diamantopoulos, A., 2011. Incorporating formative measures into covariance-based structural equationmodels. MIS Quarterly 35 (2), 335e358.

Diamantopoulos, A., Riefler, P., 2011. Using formative measures in international marketing models: a cau-tionary tale using consumer animosity as an example. Advances in International Marketing 10 (22),

11e30.Diamantopoulos, A., Riefler, P., Roth, K.P., 2008. Advancing formative measurement models. Journal of Busi-

ness Research 61 (12), 1203e1218.Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., Kaiser, S., 2012. Guidelines for choosing between

multi-item and single-item scales for construct measurement: a predictive validity perspective. Journal of

the Academy of Marketing Science 40 (3), 434e449.Diamantopoulos, A., Siguaw, J.A., 2006. Formative vs. reflective indicators in measure development: does the

choice of indicators matter? British Journal of Management 13 (4), 263e

282.Diamantopoulos, A., Winklhofer, H.M., 2001. Index construction with formative indicators: an alternative to

scale development. Journal of Marketing Research 38 (2), 269e277.

Doz, Y.L., Olk, P.M., Ring, P.S., 2000. Formation processes of R&D consortia: which path to take? Where doesit lead? Strategic Management Journal 21 (3), 239e266.

Echambadi, R., Campbell, B., Agarwal, R., 2006. Encouraging best practice in quantitative managementresearch: an incomplete list of opportunities. Journal of Management Studies 43 (8), 1801e1820.

Edwards, J.R., Bagozzi, R.P., 2000. On the nature and direction of relationships between constructs andmeasures. Psychological Methods 5 (2), 155e174.

Efron, B., Tibshirani, R., 1986. Bootstrap methods for standard errors, confidence intervals, and other

measures of statistical accuracy. Statistical Science 1 (1), 54e75.

Einhorn, H.J., 1972. Alchemy in the behavioral sciences. Public Opinion Quarterly 36 (3), 367e

378.Fornell, C., Lorange, P., Roos, J., 1990. The cooperative venture formation process: a latent variable structural

modeling approach. Management Science 36 (10), 1246e1255.Fornell, C.G., Bookstein, F.L., 1982. Two structural equation models: LISREL and PLS applied to consumer

exit-voice theory. Journal of Marketing Research 19 (4), 440e452.Fornell, C.G., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and

measurement error. Journal of Marketing Research 18 (1), 39e50.Furrer, O., Thomas, H., Goussevskaia, A., 2008. The structure and evolution of the strategic management

field: a content analysis of 26 years of strategic management research. International Journal of Management

Reviews 10 (1), 1e23.Geisser, S., 1974. A predictive approach to the random effects model. Biometrika 61 (1), 101e107.

Govindarajan, V., 1989. Implementing competitive strategies at the business unit level: implications of match-ing managers to strategies. Strategic Management Journal 10 (3), 251e269.

Gray, P.H., Meister, D.B., 2004. Knowledge sourcing effectiveness. Management Science 50 (6), 821 e834.

Greenwald, A.G., Pratkanis, A.R., Leippe, M.R., Baumgardner, M.H., 1986. Under what conditions does the-ory obstruct research progress? Psychological Review 93 (2), 216e229.






19/21

Gudergan, S.P., Ringle, C.M., Wende, S., Will, A., 2008. Confirmatory tetrad analysis in PLS path modeling.Journal of Business Research 61 (12), 1238e1249.

Haenlein, M., Kaplan, A.M., 2004. A beginners guide to partial least squares analysis. Understanding Statistics3 (4), 283e297.

Hahn, C., Johnson, M.D., Herrmann, A., Huber, F., 2002. Capturing customer heterogeneity using a finite

mixture PLS approach. Schmalenbach Business Review 54 (3), 243e269.Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., 2013. A Primer on Partial Least Squares Structural Equa-

tion Modeling. Sage, Thousand Oaks,.Hair, J.F., Ringle, C.M., Sarstedt, M., 2011. PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and

Practice 19 (2), 139e151.

Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A., 2012. An assessment of the use of partial least squares structural

equation modeling in marketing research. Journal of the Academy of Marketing Science 40 (3), 414e433.Henseler, J., 2010. On the convergence of the partial least squares path modeling algorithm. Computational

Statistics 25 (1), 107e120.

Henseler, J., 2012. Why generalized structured component analysis is not universally preferable to structuralequation modeling. Journal of the Academy of Marketing Science 40 (3), 402 e413.

Henseler, J., Chin, W.W., 2010. A comparison of approaches for the analysis of interaction effects between

latent variables using partial least squares path modeling. Structural Equation Modeling 17 (1), 82e109.

Henseler, J., Fassott, G., 2010. Testing moderating effects in PLS path models: an illustration of available pro-cedures. In: Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H. (Eds.), Handbook of Partial Least

Squares: Concepts, Methods and Applications in Marketing and Related Fields. Springer, Berlin et al.,pp. 713e735.

Henseler, J., Ringle, C.M., Sarstedt, M., 2012. Using partial least squares path modeling in international ad-vertising research: basic concepts and recent issues. In: Okazaki, S. (Ed.), Handbook of Research in Inter-

national Advertising. Edward Elgar Publishing, Cheltenham, pp. 252e276.Henseler, J., Ringle, C.M., Sinkovics, R.R., 2009. The use of partial least squares path modeling in interna-

tional marketing. Advances in International Marketing 20, 277e320.

Henseler, J., Sarstedt, M. Goodness-of-fit indices for partial least squares path modeling. Computational Sta-tistics,in press.

Howell, R.D., Breivik, E., Wilcox, J.B., 2007. Is formative measurement really measurement? Reply to Bollen(2007) and Bagozzi (2007). Psychological Methods 12 (2), 238e245.

Hui, B.S., Wold, H., 1982. Consistency and consistency at large of partial least squares estimates. In:

Joreskog, K.G., Wold, H. (Eds.), Systems under Indirect Observation: Part II, North Holland, Amsterdam,pp. 119e130.

Hulland, J., 1999. Use of Partial Least Squares (PLS) in strategic management research: a review of four recentstudies. Strategic Management Journal 20 (2), 195e204.

Hwang, H., Malhotra, N.K., Kim, Y., Tomiuk, M.A., Hong, S.A., 2010. Comparative study on parameter re-covery of three approaches to structural equation modeling. Journal of Marketing Research 47 (4),699e712.

Im, G., Rai, A., 2008. Knowledge sharing ambidexterity in long-term interorganizational relationships. Man-

agement Science 54 (7), 1281e

1296.Jarvenpaa, S.L., Majchrzak, A., 2008. Knowledge collaboration among professionals protecting national secu-

rity: role of transactive memories in ego-centered knowledge networks. Organization Science 19 (2),260e276.

Johansson, J.K., Yip, G.S., 1994. Exploiting globalization potential: U.S. and Japanese strategies. StrategicManagement Journal 15 (8), 579e601.

Joreskog, K.G., 1978. Structural analysis of covariance and correlation matrices. Psychometrika 43 (4),443e477.

Joreskog, K.G., 1982. The LISREL approach to causal model-building in the social sciences. In: Wold, H.,

Joreskog, K.G. (Eds.), Systems Under Indirect Observation: Part I, North-Holland, Amsterdam,pp. 81e100.

Joreskog, K.G., Wold, H., 1982. The ML and PLS techniques for modeling with latent variables: historical andcomparative aspects. In: Joreskog, K.G., Wold, H. (Eds.), Systems Under Indirect Observation: Part I,North-Holland, Amsterdam, pp. 263e270.

Karlis, D., Saporta, G., Spinakis, A., 2003. A simple rule for the selection of principal components. Commu-nications in Statistics e Theory and Methods 32 (3), 643e666.




http://dx.doi.org/10.1007/s00180-012-0317-1http://dx.doi.org/10.1007/s00180-012-0317-1


20/21

Lee, L., Petter, S., Fayard, D., Robinson, S., 2011. On the use of partial least squares path modeling in account-ing research. International Journal of Accounting Information Systems 12 (4), 305e328.

Li, Y., 2005. PLS-GUI e Graphic User Interface for Partial Least Squares (PLS-PC 1.8). University of SouthCarolina, Columbia, SC.

Lohmoller, J.-B., 1987. LVPLS 1.8. Lohmoller, Cologne.

Lohmoller, J.-B., 1989. Latent Variable Path Modeling with Partial Least Squares. Physica, Heidelberg.Lu, I.R.R., Kwan, E., Thomas, D.R., Cedzynski, M., 2011. Two new methods for estimating structural equation

models: An illustration and a comparison with two established methods. International Journal of Researchin Marketing 28 (3), 258e268.

MacCallum, R.C., Browne, M.W., Sugawara, H.M., 1996. Power analysis and determination of sample size for

covariance structure modeling. Psychological Methods 1 (2), 130e149.

Mahajan, V., Muller, E., Bass, F.M., 1995. Diffusion of new products: empirical generalizations and managerialuses. Marketing Science 14 (3), G79eG88.

Mooi, E., Sarstedt, M., 2011. A Concise Guide to Market Research. The Process, Data, and Methods Using

IBM SPSS Statistics. Springer, Berlin et al.Petter, S., Straub, D., Rai, A., 2007. Specifying formative constructs in information systems research. MIS

Quarterly 31 (4), 623e656.

Podsakoff, N.P., Shen, W., Podsakoff, P.M., 2006. The role of formative measurement models in strategic

management research: review, critique, and implications for future research. In: Ketchen, D.J.,Bergh, D.D. (Eds.), Research Methodology in Strategy and Management. Emerald, pp. 197 e252.

Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P., Lee, J.Y., 2003. The mismeasure of man(agement) and itsimplications for leadership research. The Leadership Quarterly 14 (6), 615e656.

Purvis, R.L., Sambamurthy, V., Zmud, R.W., 2001. The assimilation of knowledge platforms in organizations:an empirical investigation. Organization Science 12 (2), 117e135.

Raisch, S., Birkinshaw, J., 2008. Organizational ambidexterity: antecedents, outcomes, and moderators. Jour-nal of Management 34 (3), 375e409.

Reinartz, W.J., Haenlein, M., Henseler, J., 2009. An empirical comparison of the efficacy of covariance-based

and variance-based SEM. International Journal of Market Research 26 (4), 332e344.Revelle, W., 1979. Hierarchical clustering and the internal structure of tests. Multivariate Behavioral Research

14 (1), 57e

74.Rigdon, E.E. Partial least squares path modeling, in: Hancock G., Mueller R.O. (Eds.), Structural Equation

Modeling: A Secondary Course. Information Age Publishing, Charlotte, NC, in press.

Rigdon, E.E., Ringle, C.M., Sarstedt, M., 2010. Structural modeling of heterogeneous data with partial leastsquares. In: Malhotra, N.K. (Ed.), Review of Marketing Research. Sharpe, pp. 255e296. Armonk.

Rigdon, E.E., Ringle, C.M., Sarstedt, M., Gudergan, S.P., 2011. Assessing heterogeneity in customer satisfac-tion studies: across industry similarities and within industry differences. Advances in International Mar-

keting, 169e194.Ringle, C.M., Sarstedt, M., Schlittgen, R., Taylor, C. R. PLS path modeling and evolutionary segmentation.

Journal of Business Research,in press.

Ringle, C.M., Sarstedt, M., Straub, D.W., 2012. A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS

Quarterly 36 (1), iiie

xiv.Ringle, C.M., Wende, S., Will, A., 2005. SmartPLS 2.0 Hamburg.www.smartpls.de.

Robins, J.A., Tallman, S., Fladmoe-Lindquist, K., 2002. Autonomy and dependence of international cooper-ative ventures: an exploration of the strategic performance of U.S. ventures in Mexico. Strategic Manage-

ment Journal 23 (10), 881e901.Sahmer, K., Hanafi, M., Qannari, M., 2006. Assessing unidimensionality within PLS path modeling frame-

work. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nurnberger, A., Gaul, W. (Eds.), From Data and Infor-mation Analysis to Knowledge Engineering. Springer Berlin Heidelberg, pp. 222e229.

Sarkar, M.B., Echambadi, R., Harrison, J.S., 2001. Alliance entrepreneurship and firm market performance.

Strategic Management Journal 22 (6/7), 701e711.Sarstedt, M., 2008. A review of recent approaches for capturing heterogeneity in partial least squares path

modelling. Journal of Modelling in Management 3 (2), 140e

161.Sarstedt, M., Henseler, J., Ringle, C.M., 2011. Multi-group analysis in partial least squares (PLS) path mod-

eling: alternative methods and empirical results. Advances in International Marketing 22, 195 e218.

Shah, R., Goldstein, S.M., 2006. Use of structural equation modeling in operations management research:looking back and forward. Journal of Operations Management 24 (2),

Documents

Use of PLS