Partial least squares path modeling (PLS) was developed in the 1960s and 1970s as a method for predictive modeling. In the succeeding years, applied disciplines, including organizational and management research, have developed beliefs about the capabilities of PLS and its suitability for different applications. On close examination, some of these beliefs prove to be unfounded and to bear little correspondence to the actual capabilities of PLS. In this article, we critically examine several of these commonly held beliefs. We describe their origins, and, using simple examples, we demonstrate that many of these beliefs are not true. We conclude that the method is widely misunderstood, and our results cast strong doubts on its effectiveness for building and testing theory in organizational research.
Aguirre-UrretaM. I.MarakasG. M.EllisM. E. (in press). Measurement of composite reliability in research using partial least squares: Some issues and an alternative approach. DATA BASE for Advances in Information Systems.
2.
AntonakisJ.BendahanS.JacquartP.LaliveR. (2010). On making causal claims: A review and recommendations. Leadership Quarterly, 21, 1086–1120.
3.
AshillN. J.JobberD. (2010). Measuring state, effect, and response uncertainty: Theoretical construct development and empirical validation. Journal of Management, 36(5), 1278–1308.
AtincG.SimmeringM. J.KrollM. J. (2011). Control variable use and reporting in macro and micro management research. Organizational Research Methods, 15(1), 57–74.
6.
AvolioB. J.HowellJ. M.SosikJ. J. (1999). A funny thing happened on the way to the bottom line: Humor as a moderator of leadership style effects. Academy of Management Journal, 42(2), 219–227. doi:10.2307/257094
7.
BassB. M.AvolioB. J.JungD. I.BersonY. (2003). Predicting unit performance by assessing transformational and transactional leadership. Journal of Applied Psychology, 88(2), 207–218.
8.
BirkinshawJ.HoodN.JonssonS. (1998). Building firm-specific advantages in multinational corporations: The role of subsidiary initiative. Strategic Management Journal, 19(3), 221–242.
9.
BirkinshawJ.MorrisonA.HullandJ. (1995). Structural and competitive determinants of a global integration strategy. Strategic Management Journal, 16(8), 637–655.
10.
BollenK. A. (1989). Structural equations with latent variables. New York, NY: John Wiley.
11.
BollenK. A.KirbyJ. B.CurranP. J.PaxtonP. M.ChenF. (2007). Latent variable models under misspecification: Two-stage least squares (2SLS) and maximum likelihood (ML) estimators. Sociological Methods & Research, 36(1), 48–86. doi:10.1177/0049124107301947
12.
BollenK. A.StineR. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205–229. doi:10.1177/0049124192021002004
13.
BurtR. S. (1976). Interpretational confounding of unobserved variables in structural equation models. Sociological Methods & Research, 5(1), 3–52. doi:10.1177/004912417600500101
14.
CheungM.-S.MyersM. B.MentzerJ. T. (2011). The value of relational learning in global buyer-supplier exchanges: A dyadic perspective and test of the pie-sharing premise. Strategic Management Journal, 32(10), 1061–1082. doi:10.1002/smj.926
15.
ChinW. W. (1998). The partial least squares approach to structural equation modeling. In MarcoulidesG. A. (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum.
16.
ChinW. W.NewstedP. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical Strategies for Small Sample Research, 2, 307–342.
17.
CohenJ.CohenP.WestS. G.AikenL. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. London, England: Lawrence Erlbaum.
18.
CoolK.DierickxI.JemisonD. (1989). Business strategy, market structure and risk-return relationships: A structural approach. Strategic Management Journal, 10(6), 507–522.
19.
CordingM.ChristmannP.KingD. R. (2008). Reducing causal ambiguity in acquisition integration: Intermediate goals as mediators of integration decisions and acquisition performance. Academy of Management Journal, 51(4), 744–767.
20.
CrosslandC.HambrickD. C. (2011). Differences in managerial discretion across countries: How nation-level institutions affect the degree to which CEOs matter. Strategic Management Journal, 32(8), 797–819. doi:10.1002/smj.913
21.
DeliosA.BeamishP. W. (1999). Geographic scope, product diversification and the corporate performance of Japanese firms. Strategic Management Journal, 20(8), 711–727.
22.
DijkstraT. K. (1983). Some comments on maximum likelihood and partial least squares methods. Journal of Econometrics, 22(1-2), 67–90.
23.
DuxburyL. E.HigginsC. A. (1991). Gender differences in work-family conflict. Journal of Applied Psychology, 76(1), 60–73. doi:10.1037/0021-9010.76.1.60
24.
EchambadiR.CampbellB.AgarwalR. (2006). Encouraging best practice in quantitative management research: An incomplete list of opportunities. Journal of Management Studies, 43(8), 1801–1820. doi: 10.1111/j.1467-6486.2006.00660.x
25.
EdwardsJ. R. (2011). The fallacy of formative measurement. Organizational Research Methods, 14(2), 370–388. doi:10.1177/1094428110378369
26.
EvermannJ.TateM. (2010). Testing models or fitting models? Identifying model misspecification in PLS. In ICIS 2010 proceedings. Retrieved fromhttp://aisel.aisnet.org/icis2010_submissions/21
27.
FornellC.BooksteinF. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.
28.
FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
29.
GefenD.RigdonE. E.StraubD. W. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv.
30.
GoodhueD. L.LewisW.ThompsonR. (2012). Comparing PLS to regression and LISREL: A response to Marcoulides, Chin, and Saunders. MIS Quarterly, 36(3), 703–716.
31.
GrothM.Hennig-ThurauT.WalshG. (2009). Customer reactions to emotional labor: The roles of employee acting strategies and customer detection accuracy. Academy of Management Journal, 52(5), 958–974.
32.
GruberM.HeinemannF.BrettelM.HungelingS. (2010). Configurations of resources and capabilities and their performance implications: An exploratory study on technology ventures. Strategic Management Journal, 31(12), 1337–1356.
33.
HairJ. F.RingleC. M.SarstedtM. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory & Practice, 19(2), 139–152. doi:10.2753/MTP1069-6679190202
34.
HairJ. F.SarstedtM.PieperT. M.RingleC. M. (2012). 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, 45(5–6), 320–340. doi:10.1016/j.lrp.2012.09.008
35.
HenselerJ.ChinW. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17(1), 82–109. doi:10.1080/10705510903439003
36.
HenselerJ.SarstedtM. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics. doi:10.1007/s00180-012-0317-1
37.
HerzogW.BoomsmaA. (2009). Small-sample robust estimators of noncentrality-based and incremental model fit. Structural Equation Modeling: A Multidisciplinary Journal, 16(1), 1–27. doi:10.1080/10705510802561279
38.
HowellJ. M.AvolioB. J. (1993). Transformational leadership, transactional leadership, locus of control, and support for innovation: Key predictors of consolidated-business-unit performance. Journal of Applied Psychology, 78(6), 891–902. doi:10.1037/0021-9010.78.6.891
39.
HowellJ. M.Hall-MerendaK. E. (1999). The ties that bind: The impact of leader-member exchange, transformational and transactional leadership, and distance on predicting follower performance. Journal of Applied Psychology, 84(5), 680–694.
40.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
41.
HullandJ. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.
42.
JohanssonJ. K.YipG. S. (1994). Exploiting globalization potential: U.S. and Japanese strategies. Strategic Management Journal, 15(8), 579–601.
43.
JöreskogK. G.WoldH. (1982). The ML and PLS techniques for modeling with latent variables. In JöreskogK. G.WoldH. (Eds.), Systems under indirect observation: Causality, structure, prediction (pp. 263–270). Amsterdam, the Netherlands: North-Holland.
44.
LaiK.KelleyK. (2011). Accuracy in parameter estimation for targeted effects in structural equation modeling: Sample size planning for narrow confidence intervals. Psychological Methods, 16(2), 127–148. doi:10.1037/a0021764
45.
LandisR. S.BealD. J.TeslukP. E. (2000). A comparison of approaches to forming composite measures in structural equation models. Organizational Research Methods, 3(2), 186–207. doi:10.1177/109442810032003
46.
LehmannE.CasellaG. (1998). Theory of point estimation. New York, NY: Springer.
47.
LittleT. D.CunninghamW. A.ShaharG.WidamanK. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9(2), 151–173.
48.
LohmöllerJ. B. (1989). Latent variable path modeling with partial least squares. Heidelberg, Germany: Physica-Verlag.
49.
McDonaldR. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270.
50.
MeznarM. B.NighD. (1995). Buffer or bridge? Environmental and organizational determinants of public affairs activities in American firms. Academy of Management Journal, 38, 975–996.
51.
MulaikS. A. (1985). Exploratory statistics and empiricism. Philosophy of Science, 52(3), 410–430.
52.
NickersonR. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5(2), 241–301. doi:10.1037//1082-989X.5.2.241
53.
NunnallyJ. (1978). Psychometric theory. New York, NY: McGraw-Hill.
54.
OlkP.YoungC. (1997). Why members stay in or leave an R&D consortium: Performance and conditions of membership as determinants of continuity. Strategic Management Journal, 18(11), 855–877.
55.
PengD. X.LaiF. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480. doi:10.1016/j.jom.2012.06.002
56.
RaykovT. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. doi:10.1177/01466216970212006
57.
ReinartzW. J.HaenleinM.HenselerJ. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. doi:10.1016/j.ijresmar.2009.08.001
58.
RobinsJ. A.TallmanS.Fladmoe-LindquistK. (2002). Autonomy and dependence of international cooperative ventures: An exploration of the strategic performance of U.S. ventures in Mexico. Strategic Management Journal, 23(10), 881–901. doi:10.1002/smj.260
59.
RosseelY. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
SarkarM. B.EchambadiR. A. J.HarrisonJ. S. (2001). Alliance entrepreneurship and firm market performance. Strategic Management Journal, 22(6/7), 701–711. doi:10.1002/smj.179
62.
ShamirB.ZakayE.BreininE.PopperM. (1998). Correlates of charismatic leader behavior in military units: Subordinates’ attitudes, unit characteristics, and superiors’ appraisals of leader performance. Academy of Management Journal, 41, 387–409.
63.
SheaC. M.HowellJ. M. (2000). Efficacy-performance spirals: An empirical test. Journal of Management, 26(4), 791–812. doi:10.1177/014920630002600409
64.
SosikJ. J.AvolioB. J.KahaiS. S. (1997). Effects of leadership style and anonymity on group potency and effectiveness in a group decision support system environment. Journal of Applied Psychology, 82(1), 89–103.
65.
SosikJ. J.KahaiS. S.PiovosoM. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 34(1), 5–36. doi:10.1177/1059601108329198
66.
TiwanaA. (2008). Do bridging ties complement strong ties? An empirical examination of alliance ambidexterity. Strategic Management Journal, 29(3), 251–272.
67.
TsangE. W. K. (2002). Acquiring knowledge by foreign partners from international joint ventures in a transition economy: Learning-by-doing and learning myopia. Strategic Management Journal, 23(9), 835–854. doi:10.1002/smj.251
68.
VandenbergR. J. (2006). Introduction: Statistical and methodological myths and urban legends. Organizational Research Methods, 9(2), 194–201. doi:10.1177/1094428105285506
69.
VinziV. E.ChinW. W.HenselerJ.WangH. (2010). Editorial: Perspectives on partial least squares. In VinziV. E.ChinW. W.HenselerJ.WangH. (Eds.), Handbook of partial least squares (pp. 1–22). Berlin, Germany: Springer.
70.
WangT.BansalP. (2012). Social responsibility in new ventures: Profiting from a long-term orientation. Strategic Management Journal, 33, 1135–1153. doi:10.1002/smj.1962
71.
WoldH. (1985a). Factors influencing the outcome of economic sanctions. Trabajos de Estadistica y de Investigacion Operativa, 36(3), 325–338. doi:10.1007/BF02888567
72.
WoldH. (1985b). Systems analysis by partial least squares. In NijkampP.LeitnerL.WrigleyN. (Eds.), Measuring the unmeasurable (pp. 221–252). Dordrecht, Germany: Marinus Nijhoff.
73.
WoodM. (2005). Bootstrapped confidence intervals as an approach to statistical inference. Organizational Research Methods, 8(4), 454–470. doi:10.1177/1094428105280059
74.
ZimmermanD. W.WilliamsR. H. (1977). The theory of test validity and correlated errors of measurement. Journal of Mathematical Psychology, 16(2), 135–152.
75.
ZottC.AmitR. (2008). The fit between product market strategy and business model: Implications for firm performance. Strategic Management Journal, 29(1), 1–26.
