Structural equation modeling (SEM) is a widely applied and useful tool for project management scholars. In this Thoughtlet article, we critically reflect on the measurement philosophy underlying the two streams of SEM and their adequacy for estimating relationships among concepts commonly encountered in the field (e.g., team performance). We also discuss considerations to ponder when making the choice between the two types of SEM as well as between SEM and regression analysis.
BalthazardP. A.WaldmanD. A.WarrenJ. E. (2009). Predictors of the emergence of transformational leadership in virtual decision teams. Leadership Quarterly, 20(5), 651–663.
2.
BollenK. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605–634.
3.
ChiocchioF.HobbsB. (2014). The difficult but necessary task of developing a specific project team research agenda. Project Management Journal, 45(6), 7–16.
4.
ColeD. A.PreacherK. J. (2014). Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19(2), 300–315.
5.
FrostP. A. (1979). Proxy variables and specification bias. The Review of Economics and Statistics, 61(2), 323–325.
6.
HairJ. F.BabinB. J.KreyN. (2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations, Journal of Advertising, 46(1), 163–177.
7.
HairJ. F.HultG. T. M.RingleC. M.SarstedtM.ThieleK. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632.
8.
HairJ. F.RisherJ. J.SarstedtM.RingleC. M. (2019a). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
9.
HairJ. F.SarstedtM.RingleC. M. (2019b). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584.
10.
HayesA. F.MontoyaA. K.RockwoodN. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25(1), 76–81.
11.
HenselerJ.RingleC. M.SarstedtM. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.
12.
HultG. T. M.HairJ. F.ProkschD.SarstedtM.PinkwartA.RingleC. M. (2018). Addressing endogeneity in marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1–21.
13.
HwangH.SarstedtM.CheahJ.-H.RingleC. M. (2019). A concept analysis of methodological research on composite-based structural equation modeling: Bridging PLSPM and GSCA. Behaviormetrika, Advance online publication.
JitpaiboonT.SmithS. M.GuQ. (2019). Critical success factors affecting project performance: An analysis of tools, practices, and managerial support. Project Management Journal, 50(3), 271–287.
18.
JoüreskogK. G. (1973). A general method for estimating a linear structural equation system. In GoldbergerA. S.DuncanO. D. (Eds.), Structural equation models in the social sciences (pp. 255–284). New York, NY:Academic Press.
19.
JoüreskogK. G.WoldH. O. A. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In WoldH. O. A.JoüreskogK. G. (Eds.), Systems under indirect observation, part I (pp. 263–270). Amsterdam, the Netherlands: North-Holland.
20.
KhanG.SarstedtM.ShiauW.-L.HairJ. F.RingleC. M.FritzeM. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM): A social network analysis. Internet Research, 29(3), 407–429.
21.
MüllerR.KleinG. (2018). What constitutes a contemporary contribution to Project Management Journal®?Project Management Journal, 49(5), 3–4.
22.
MüllerR.TurnerJ. R.AndersenE. S.ShaoJ.KvalnesØ. (2016). Governance and ethics in temporary organizations: The mediating role of corporate governance. Project Management Journal, 47(6), 7–23.
23.
OjiakoU.AshleighM.WangJ.-K.ChipuluM. (2011). The criticality of transferable skills development and virtual learning environments used in the teaching of project management. Project Management Journal, 47(4), 76–86.
24.
RhemtullaM.van BorkR.BorsboomD. (2019). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, Advance online publication.
25.
RigdonE. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341–358.
26.
RigdonE. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34(6), 598–605.
27.
RigdonE. E.BeckerJ.-M.SarstedtM. (2019a). Factor indeterminacy as metrological uncertainty: Implications for advancing psychological measurement. Multivariate Behavioral Research, 54(3), 429–443.
28.
RigdonE. E.BeckerJ.-M.SarstedtM. (2019b). Parceling cannot reduce factor indeterminacy in factor analysis: A research note. Psychometrika84(3), 772–780.
29.
RigdonE. E.SarstedtM.RingleC. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing ZFP, 39(3), 4–16.
30.
RoünkkoüM.EvermannJ. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425–448.
31.
RoünkkoüM.McIntoshC. N.AntonakisJ. (2015). On the adoption of partial least squares in psychological research: Caveat emptor. Personality and Individual Differences,87, 76–84.
32.
SarstedtM.HairJ. F.RingleC. M.ThieleK. O.GuderganS. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies!Journal of Business Research, 69(10), 3998–4010.
33.
ShmueliG.SarstedtM.HairJ. F.CheahJ.-H.TingH.VaithilingamS.RingleC. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347.
34.
SteigerJ. H. (1979). The relationship between external variables and common factors. Psychometrika, 44(1), 93–97.
TabassiA. A.RoufechaeiK. M.BakarA. H. A.YusofN. (2017). Linking team condition and team performance: A transformational leadership approach. Project Management Journal, 48(2), 22–38.
37.
TeräsvirtaT. (1987). Usefulness of proxy variables in linear models with stochastic regressors. Journal of Econometrics, 36(3), 377–382.
38.
WickensM. R. (1972). A note on the use of proxy variables. Econometrica, 40(4), 759–761.
WoldH. O. A. (1982). Soft modeling: the basic design and some extensions. In JoüreskogK. G.WoldH. O. A. (Eds.), Systems under indirect observations, part II (pp. 1–54). Amsterdam, the Netherlands: North-Holland.