Common Beliefs and Reality About PLS

Organizational Research Methods - Tập 17 Số 2 - Trang 182-209 - 2014
Jörg Henseler1,2, Theo K. Dijkstra3,4, Marko Sarstedt5,6, Christian M. Ringle7,6, Adamantios Diamantopoulos8, Detmar W. Straub9, David J. Ketchen10, Joseph F. Hair11, G. Tomas M. Hult12, Roger J. Calantone12
1#N# 2ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal
21Institute for Management Research, Radboud University Nijmegen, Nijmegen, the Netherlands
3Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
4SOM EEF
5Otto Von Guericke Univ, Otto von Guericke University, Univ Hosp Magdeburg, Inst Human Genet
6University of Newcastle, Callaghan, Australia
7Tech Univ Hamburg Harburg, Hamburg University of Technology, Dept Technol Assessment
8University of Vienna, Vienna, Austria
9Georgia State Univ, Georgia State University, University System of Georgia, J Mack Robinson Coll Business
10Auburn Univ, Auburn University, Auburn University System, Raymond J Harbert Coll Business
11Kennesaw State Univ, Kennesaw State University, University System of Georgia, Coles Coll Business
12Broad College of Business, Michigan State University, East Lansing, MI, USA

Tóm tắt

This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

Từ khóa


Tài liệu tham khảo

10.1016/j.leaqua.2010.10.010

Areskoug B., 1982, Systems under indirect observation: Causality, structure, prediction

10.1177/1094428110397773

Barclay D., 1995, Technology Studies, 2, 285

Becker J.-M., 2013, Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance

10.25300/MISQ/2013/37.3.01

10.1207/s15327906mbr1101_1

Bentler P. M., Long Range Planning

10.1002/9781118619179

Bollen K. A., 1993, Testing structural equation models

Byrne B. M., 1998, Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming

Canty A., 2013, boot: Bootstrap R (S-Plus) functions

Chin W. W., 2003, PLS graph–version 3.0

10.1287/isre.14.2.189.16018

10.1207/s15327906mbr1801_7

10.1007/BF02289662

10.1017/CBO9780511802843

10.1214/ss/1032280214

10.1016/0304-4076(83)90094-5

10.1007/978-3-540-32827-8_2

Dijkstra T. K., Long Range Planning

Dijkstra T. K., Henseler J. (2012). Consistent and asymptotically normal PLS-estimators for linear structural equations (Working paper). Retrieved from http://www.rug.nl/staff/t.k.dijkstra/research.

Dijkstra T. K., Psychometrika

10.1007/978-3-540-32827-8_3

10.1177/002224378201900406

10.1177/002224378101800104

Fox J., 2013, sem: Structural equation models

10.2307/23044042

10.1080/10705519409539967

10.1109/HICSS.2006.381

10.2307/41703490

10.1037/0033-295X.93.2.216

Hair J. F., 1998, Multivariate analysis

Hair J. F., 2014, A primer on partial least squares structural equation modeling (PLS-SEM)

10.2753/MTP1069-6679190202

10.1016/j.lrp.2013.01.001

10.1016/j.lrp.2012.09.008

10.1007/s11747-011-0261-6

10.1016/j.csda.2003.09.007

10.1177/0956797613480187

10.1080/10705511.2012.634710

10.1007/s00180-009-0164-x

10.1007/s11747-011-0298-6

10.1080/10705510903439003

10.1108/S1474-7979(2009)0000020014

10.1007/s00180-012-0317-1

10.1007/BF02925318

10.1037/1082-989X.3.4.424

10.1080/10705519909540118

Hui B. S., 1982, Systems under indirect observation, part II, 119

10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7

10.1007/978-3-540-32827-8_15

10.1509/jmkr.47.4.699

10.1177/002224378201900405

10.1016/j.csda.2006.12.004

Jöreskog K. G., 1993, Lisrel 8: Structured equation modeling with the Simplis command language

Jöreskog K. G., 1982, Systems under indirect observation, part I, 263

10.2307/2019221

10.1017/S0954579400000523

10.1007/BF02294860

10.1177/109442810032003

10.1016/j.accinf.2011.05.002

Ley P., 1972, Quantitative aspects of psychological assessment

10.1207/s15327906mbr2301_7

10.1007/978-3-642-52512-4

Lohmöller J.-B., 1980, Three-mode path models with latent variables and partial least squares (PLS) parameter estimation

10.1016/j.ijresmar.2011.03.006

10.1080/15366360802035596

10.2307/25148727

10.1207/s15327906mbr3102_5

10.18637/jss.v048.i03

10.4135/9781412985116

Mulaik S. A., 2010, The foundations of factor analysis, 2

Muthén L. K., 2006, Mplus version 4 user’s guide

10.1207/S15328007SEM0802_7

10.1016/j.jom.2012.06.002

R Core Team, 2013, R: A language and environment for statistical computing

Rai A., 2013, To PLS or not to PLS: That is the question

Rego L. L., 1998, Advances in Consumer Research, 25, 132

10.1016/j.ijresmar.2009.08.001

10.1016/j.lrp.2012.09.010

Rigdon E. E., 2013, Structural equation modeling: A second course, 2, 81

Rigdon E. E., Long Range Planning

10.1007/s00291-013-0320-0

10.1016/j.jbusres.2012.02.031

10.2307/41410402

Ringle C. M., Wende S., Will A. (2005). SmartPLS 2.0. Retrieved from www.smartpls.de.

10.1177/1094428112474693

10.18637/jss.v048.i02

Rubinstein R. Y., 2008, Simulation and the Monte Carlo method, 2

10.1007/BF03396886

Sarstedt M., Long Range Planning

10.1007/BF02291413

10.1177/1059601108329198

10.1007/978-3-540-32827-8_32

10.1007/s11336-011-9206-8

10.1080/14783360802159543

10.1016/j.csda.2004.03.005

10.1207/s15327752jpa8701_03

10.1007/978-3-540-32827-8_14

10.1007/978-3-642-34904-1_2

10.1016/0014-2921(74)90008-7

Wold H., 1979, Model construction and evaluation when theoretical knowledge is scarce: An example of the use of partial least squares cahiers du département déconométrie

10.1016/B978-0-12-416550-2.50007-8

Wold H., 1982, Systems under indirect observations: Part II, 1

Wold H., 1985, Encyclopedia of statistical sciences, 6, 581

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Organizational Research Methods

Tập 17 Số 2

182-209

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