Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)
Open accessAbstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The model fit indices (SRMR = 0.076; NFI = 0.866) indicate that the data fit the model and the model predicts and tests the hypotheses.
“Finally, the goodness of fit was determined using SmartPLS by calculating the standardized root mean square residual (SRMR; 0.076) and the normed fit index (NFI; 0.866). SRMR values below 0.08 mean that the data fit the model (Hu and Bentler, 1999; Henseler et al., 2014), whereas for NFI values (which are between 0 and 1), the closer the value is to 1, the better the fit (Lohm ¨ oller, 1989).”
From Mastering digital transformation: The nexus between leadership, agility, and digital strategy (2022) · cites this paper as Henseler et al., 2014
PLS-SEM does not require data to meet certain distributional assumptions and can work with non-normal data.
“PLS-SEM does not require the data to meet certain distributional assumptions and can work with non-normal data (Henseler et al., 2014);”
From A consumer behavioural approach to food waste (2018) · cites this paper as Henseler et al., 2014
The bootstrap confidence interval for SRMR is [0.043, 0.053], lower than the suggested threshold of 0.08.
“The bootstrap confidence interval for the Squared Root Mean Residual is [0.043, 0.053], lower than the suggested threshold of 0.08 (Henseler et al., 2014).”
From A consumer behavioural approach to food waste (2018) · cites this paper as Henseler et al., 2014
The model fit should be read as adequate rather than strong.
“so the model fit should be read as adequate rather than strong.”
From Seeing Is Believing, Experiencing Is Deciding: How Augmented Reality Attributes Drive Confidence in Choice and Willingness to Pay (2026) · cites this paper as Henseler et al. (2014)
The SRMR value was 0.037, which is lower than the threshold of 0.080.
“The value of the SRMR was 0.037, which is lower than the threshold of 0.080 [138].”
A confirmatory composite analysis helps determine whether it makes sense to create the proposed formative construct and identifies any model misspecifications.
“In other words, a confirmatory composite analysis helps determine whether it makes sense to create the proposed formative construct and identifies any model misspecifications [138,139].”
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314 · see citing works
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190 · see citing works
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788 · see citing works
<b>lavaan</b> : An <i>R</i> Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · see citing works
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631 · see citing works
Self-citationA primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921 · see citing works
Structural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,512 · see citing works
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2072165 · Cited by 17,471 · see citing works
Testing Structural Equation Models.
Mueller, C. W., Bollen, K. A., & Long, J. S. (1994) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2074955 · Cited by 14,211 · see citing works
(2025) · doi:10.53555/acr.v2i2 · Cited by 13,486 · see citing works
(2014) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 12,785 · see citing works
Self-citationHu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298 · see citing works
\nThe use of partial least squares path modeling in international marketing
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) · Radboud Repository (Radboud University) · doi:10.1108/s1474-7979(2009)0000020014 · Cited by 11,790 · see citing works
Self-citationTesting Structural Equation Models.
Clogg, C. C., Bollen, K. A., & Long, J. S. (1995) · Social Forces · doi:10.2307/2580595 · Cited by 8,244 · see citing works
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088 · see citing works
Self-citationBootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350 · see citing works
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003) · Information Systems Research · doi:10.1287/isre.14.2.189.16018 · Cited by 6,479 · see citing works
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.03.005 · Cited by 6,312 · see citing works
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478 · see citing works
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013) · Long Range Planning · doi:10.1016/j.lrp.2013.01.001 · Cited by 5,310 · see citing works
Self-citationTwo Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory
Fornell, C., & Bookstein, F. L. (1982) · Journal of Marketing Research · doi:10.2307/3151718 · Cited by 4,516 · see citing works
Structural Equation Modeling With Lisrel, Prelis, and Simplis
Byrne, B. M. (2013) · Psychology Press eBooks · doi:10.4324/9780203774762 · Cited by 4,307 · see citing works
Handbook of Partial Least Squares
Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. (2010) · Springer eBooks · doi:10.1007/978-3-540-32827-8 · Cited by 3,850 · see citing works
Self-citationAn empirical comparison of the efficacy of covariance-based and variance-based SEM
Reinartz, W., Haenlein, M., & Henseler, J. (2009) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2009.08.001 · Cited by 2,906 · see citing works
Self-citationStructural Equation Modeling: A Second Course
(2007) · Personnel Psychology · doi:10.1111/j.1744-6570.2007.00081_13.x · Cited by 2,851 · see citing works
Simulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2007) · Wiley series in probability and statistics · doi:10.1002/9780470230381 · Cited by 2,482 · see citing works
Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012) · Long Range Planning · doi:10.1016/j.lrp.2012.09.008 · Cited by 2,454 · see citing works
Self-citationOn making causal claims: A review and recommendations
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010) · The Leadership Quarterly · doi:10.1016/j.leaqua.2010.10.010 · Cited by 2,441 · see citing works
Hayes, A. F., & Scharkow, M. (2013) · Psychological Science · doi:10.1177/0956797613480187 · Cited by 2,438 · see citing works
Bootstrap confidence intervals
DiCiccio, T. J., & Efron, B. (1996) · Statistical Science · doi:10.1214/ss/1032280214 · Cited by 2,286 · see citing works
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012) · MIS Quarterly · doi:10.2307/41410402 · Cited by 2,184 · see citing works
Self-citationLatent Variable Path Modeling with Partial Least Squares
Lohmöller, J.-B. (1989) · Physica-Verlag eBooks · doi:10.1007/978-3-642-52512-4 · Cited by 2,149 · see citing works
Goodness-of-fit indices for partial least squares path modeling
Henseler, J., & Sarstedt, M. (2012) · Computational Statistics · doi:10.1007/s00180-012-0317-1 · Cited by 1,984 · see citing works
Self-citationGefen (2011) · doi:10.2307/23044042 · Cited by 1,596 · see citing works
Self-citationWold, H. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1914.pub2 · Cited by 1,535 · see citing works
Peng, D. X., & Lai, F. (2012) · Journal of Operations Management · doi:10.1016/j.jom.2012.06.002 · Cited by 1,532 · see citing works
Encyclopedia of Statistical Sciences 2.
Dawid, A. P., Kotz, S., Johnson, N. L., & Read, C. B. (1984) · Biometrics · doi:10.2307/2530789 · Cited by 1,494 · see citing works
Consistent and asymptotically normal PLS estimators for linear structural equations
Dijkstra, T. K., & Henseler, J. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.07.008 · Cited by 1,268 · see citing works
Self-citationSoft modelling: The Basic Design and Some Extensions
Wold, H. (1982) · Medical Entomology and Zoology · Cited by 1,262 · see citing works
Henseler, J., & Chin, W. W. (2010) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705510903439003 · Cited by 1,240 · see citing works
Self-citationVinzi, V. E., Trinchera, L., & Amato, S. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_3 · Cited by 1,159 · see citing works
A Comparison of Approaches to Forming Composite Measures in Structural Equation Models
Landis, R. S., Beal, D. J., & Tesluk, P. E. (2000) · Organizational Research Methods · doi:10.1177/109442810032003 · Cited by 1,155 · see citing works
Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods
Rigdon, E. E. (2012) · Long Range Planning · doi:10.1016/j.lrp.2012.09.010 · Cited by 915 · see citing works
Structural Equation Modeling: Reviewing the Basics and Moving Forward
Ullman, J. B. (2006) · Journal of Personality Assessment · doi:10.1207/s15327752jpa8701_03 · Cited by 861 · see citing works
(1982) · Cited by 842 · see citing works
On the emancipation of PLS-SEM: a commentary on Rigdon (2012)
Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014) · Long Range Planning · doi:10.1016/j.lrp.2014.02.007 · Cited by 758 · see citing works
Self-citationDoes PLS Have Advantages for Small Sample Size or Non-Normal Data?1
Goodhue, D. L., ', L., & Thompson (2012) · MIS Quarterly · doi:10.2307/41703490 · Cited by 750 · see citing works
Mooney, C. (1997) · doi:10.4135/9781412985116 · Cited by 677 · see citing works
semPLS: Structural Equation Modeling Using Partial Least Squares
Monecke, A., & Leisch, F. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i03 · Cited by 658 · see citing works
Foundations of Factor Analysis
Mulaik, S. A. (2009) · Chapman & Hall/CRC eBooks · doi:10.1201/b15851 · Cited by 654 · see citing works
Marcoulides, G. A., & Saunders, C. (2006) · MIS Quarterly · doi:10.2307/25148727 · Cited by 580 · see citing works
Under what conditions does theory obstruct research progress?
Greenwald, A. G., Pratkanis, A. R., Leippe, M. R., & Baumgardner, M. H. (1986) · Psychological Review · doi:10.1037/0033-295x.93.2.216 · Cited by 574 · see citing works
Some Cautions Concerning The Application Of Causal Modeling Methods
Cliff, N. (1983) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr1801_7 · Cited by 553 · see citing works
Silver Bullet or Voodoo Statistics?
Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009) · Group & Organization Management · doi:10.1177/1059601108329198 · Cited by 519 · see citing works
Model Construction and Evaluation When Theoretical Knowledge Is Scarce
Wold, H. (1980) · Elsevier eBooks · doi:10.1016/b978-0-12-416550-2.50007-8 · Cited by 474 · see citing works
The ML and PLS techniques for modeling with latent variables : Historical and comparative aspects
Jöreskog, K. G. (1982) · Cited by 472 · see citing works
Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats1
Becker, J.-M., Rai, A., Ringle, C. M., & Völckner, F. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.3.01 · Cited by 464 · see citing works
Self-citationA Critical Examination of Common Beliefs About Partial Least Squares Path Modeling
Rönkkö, M., & Evermann, J. (2013) · Organizational Research Methods · doi:10.1177/1094428112474693 · Cited by 442 · see citing works
On the use of partial least squares path modeling in accounting research
Lee, L. S., Petter, S., Fayard, D., & Robinson, S. N. (2011) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2011.05.002 · Cited by 395 · see citing works
On the convergence of the partial least squares path modeling algorithm
Henseler, J. (2009) · Computational Statistics · doi:10.1007/s00180-009-0164-x · Cited by 387 · see citing works
Self-citationRegularized Generalized Canonical Correlation Analysis
Tenenhaus, A., & Tenenhaus, M. (2011) · Psychometrika · doi:10.1007/s11336-011-9206-8 · Cited by 367 · see citing works
Monte Carlo Experiments: Design and Implementation
Paxton, P. M., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem0802_7 · Cited by 347 · see citing works
Path Analysis with Composite Variables
McDonald, R. P. (1996) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3102_5 · Cited by 333 · see citing works
Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011) · Schmalenbach Business Review · doi:10.1007/bf03396886 · Cited by 308 · see citing works
Self-citationPLS, Small Sample Size, and Statistical Power in MIS Research
Goodhue, D. L., Lewis, W., & Thompson, R. L. (2006) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2006.381 · Cited by 305 · see citing works
Does PLS have advantages for small sample size or non-normal data?
Goodhue, D. L., Lewis, W. W., & Thompson, R. L. (2012) · MIS Quarterly · doi:10.5555/2481655.2481672 · Cited by 298 · see citing works
(2024) · Springer eBooks · doi:10.1007/978-981-99-9283-6_1776 · Cited by 279 · see citing works
Some comments on maximum likelihood and partial least squares methods
Dijkstra, T. K. (1983) · Journal of Econometrics · doi:10.1016/0304-4076(83)90094-5 · Cited by 277 · see citing works
Self-citationComponent-based Structural Equation Modelling
Tenenhaus, M. (2008) · Total Quality Management & Business Excellence · doi:10.1080/14783360802159543 · Cited by 276 · see citing works
Causal flows with latent variables
Wold, H. (1974) · European Economic Review · doi:10.1016/0014-2921(74)90008-7 · Cited by 269 · see citing works
Control Variable Use and Reporting in Macro and Micro Management Research
Atinc, G. M., Simmering, M. J., & Kroll, M. J. (2011) · Organizational Research Methods · doi:10.1177/1094428110397773 · Cited by 267 · see citing works
The PLS Program System: Latent Variables Path Analysis with Partial Least Squares Estimation
Lohmöller, J.-B. (1988) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr2301_7 · Cited by 261 · see citing works
A Comparative Study on Parameter Recovery of Three Approaches to Structural Equation Modeling
Hwang, H., Malhotra, N. K., Kim, Y.-C., Tomiuk, M. A., & Hong, S. (2010) · Journal of Marketing Research · doi:10.1509/jmkr.47.4.699 · Cited by 246 · see citing works
Latent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares
Dijkstra, T. K. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_2 · Cited by 238 · see citing works
Self-citationRethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead
Rigdon, E. E. (2014) · Long Range Planning · doi:10.1016/j.lrp.2014.02.003 · Cited by 229 · see citing works
Genetic algorithm segmentation in partial least squares structural equation modeling
Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2013) · OR Spectrum · doi:10.1007/s00291-013-0320-0 · Cited by 179 · see citing works
Self-citationBecker, J.-M., Rai, A., & Rigdon, E. E. (2013) · Digital Archive @ GSU · Cited by 172 · see citing works
Dijkstra, T. K. (2014) · Long Range Planning · doi:10.1016/j.lrp.2014.02.004 · Cited by 152 · see citing works
Self-citationHair, J. F., Ringle, C. M., & Sarstedt, M. (2013) · Long Range Planning · doi:10.1016/j.lrp.2013.08.016 · Cited by 144 · see citing works
Self-citationSome New Results on Factor Indeterminacy
Schönemann, P. H., & Wang, M. (1972) · Psychometrika · doi:10.1007/bf02291413 · Cited by 144 · see citing works
Henseler, J. (2011) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0298-6 · Cited by 144 · see citing works
Self-citation(1998) · Taylor & Francis eBooks · doi:10.1201/noe0750305136.ch18 · Cited by 143 · see citing works
Consistent Partial Least Squares for Nonlinear Structural Equation Models
Dijkstra, T. K., & Schermelleh-Engel, K. (2013) · Psychometrika · doi:10.1007/s11336-013-9370-0 · Cited by 126 · see citing works
Self-citationPLS path modeling and evolutionary segmentation
Ringle, C. M., Sarstedt, M., Schlittgen, R., & Taylor, C. R. (2012) · Journal of Business Research · doi:10.1016/j.jbusres.2012.02.031 · Cited by 118 · see citing works
Self-citationLu, I. R. R., Kwan, E., Thomas, D. R., & Cedzynski, M. (2011) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2011.03.006 · Cited by 109 · see citing works
Structural equation modeling: A second course, 2nd ed.
Hancock, G. R., & Mueller, R. O. (2013) · Cited by 103 · see citing works
Hulland, J. S., Ryan, M. J., & Rayner, R. K. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_15 · Cited by 88 · see citing works
Sensitivity of SEM Fit Indexes With Respect to Violations of Uncorrelated Errors
Heene, M., Hilbert, S., Freudenthaler, H. H., & Bühner, M. (2012) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2012.634710 · Cited by 86 · see citing works
Vilares, M. J., Almeida, M. H., & Coelho, P. S. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_14 · Cited by 85 · see citing works
Multicollinearity in Structural Equation Models with Unobservable Variables
Jagpal, H. S. (1982) · Journal of Marketing Research · doi:10.1177/002224378201900405 · Cited by 80 · see citing works
Multistructure Statistical Model Applied To Factor Analysis
Bentler, P. M. (1976) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr1101_1 · Cited by 73 · see citing works
sem: Structural Equation Models
Fox, J., Nie, Z., & Byrnes, J. E. K. (2001) · doi:10.32614/cran.package.sem · Cited by 69 · see citing works
Jakobowicz, E., & Derquenne, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.12.004 · Cited by 65 · see citing works
A Comparison of Current PLS Path Modeling Software: Features, Ease-of-Use, and Performance
Temme, D., Kreis, H., & Hildebrandt, L. (2009) · Springer eBooks · doi:10.1007/978-3-540-32827-8_32 · Cited by 64 · see citing works
Definition and Specification of Meaning
Kaplan, A. (1946) · The Journal of Philosophy · doi:10.2307/2019221 · Cited by 45 · see citing works
Conditions for Factor (in)Determinacy in Factor Analysis
Krijnen, W. P., Dijkstra, T. K., & Gill, R. D. (1998) · Psychometrika · doi:10.1007/bf02294860 · Cited by 43 · see citing works
Self-citationHair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012) · SSRN Electronic Journal · Cited by 35 · see citing works
Self-citationPartial least squares analysis in developmental psychopathology
Ketterlinus, R. D., Bookstein, F. L., Sampson, P. D., & Lamb, M. E. (1989) · Development and Psychopathology · doi:10.1017/s0954579400000523 · Cited by 31 · see citing works
Maraun, M. D., & Halpin, P. F. (2008) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366360802035596 · Cited by 27 · see citing works
Covariance structure analysis via specification searches
Homburg, C., & Dobratz, A. (1992) · Statistical Papers · doi:10.1007/bf02925318 · Cited by 18 · see citing works
Gerbing, D. W., & Hamilton, J. G. (1994) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519409539967 · Cited by 18 · see citing works
Regression Analysis of Linear Composite Variance
Creager, J. A., & Valentine, L. D. (1962) · Psychometrika · doi:10.1007/bf02289662 · Cited by 14 · see citing works
Vilares, M. J., & Coelho, P. S. (2013) · Studies in theoretical and applied statistics. Selected papers of the statistical societies · doi:10.1007/978-3-642-34904-1_2 · Cited by 9 · see citing works
An alternative algorithm to the PLS B problem
Hanafi, M. M., & Qannari, E. M. (2003) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2003.09.007 · Cited by 9 · see citing works
To PLS or Not to PLS: That is the Question
Rai, A., Goodhue, D. L., Henseler, J., & Thompson, R. L. (2013) · Cited by 7 · see citing works
Self-citationThe Relationship Market Structure-Market Efficiency From a Customer Satisfaction Perspective
Rego, L. L. (1998) · ACR North American Advances · Cited by 4 · see citing works
Use of partial least squares (PLS) in strategic management research: a review of four recent studies
Hulland, J. (1999) · Strategic Management Journal · doi:10.1002/(sici)1097-0266(199902)20:2
Flagged first, then by citations
3 of the 111 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · see citing works
Self-citationWhen to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541 · see citing works
Self-citationUsing pls path modeling in new technology research: updated guidelines
Henseler, J., Hubona, G., & Ray, P. A. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-09-2015-0382 · Cited by 7,173 · see citing works
Self-citationAssessing measurement model quality in PLS-SEM using confirmatory composite analysis
Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446 · see citing works
Self-citationAn updated and expanded assessment of PLS-SEM in information systems research
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017) · Industrial Management & Data Systems · doi:10.1108/imds-04-2016-0130 · Cited by 4,043 · see citing works
Self-citationTesting measurement invariance of composites using partial least squares
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016) · International Marketing Review · doi:10.1108/imr-09-2014-0304 · Cited by 3,460 · see citing works
Self-citationPartial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285 · see citing works
Self-citationConsistent Partial Least Squares Path Modeling1
Dijkstra, T. K., & Henseler, J. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.2.02 · Cited by 2,898 · see citing works
Self-citationHair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0517-x · Cited by 2,712 · see citing works
Self-citationMediation analysis in partial least squares path modeling
Nitzl, C., Roldán, J. L., & Cepeda‐Carrión, G. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-07-2015-0302 · Cited by 2,618 · see citing works
Partial least squares structural equation modeling in HRM research
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020) · The International Journal of Human Resource Management · doi:10.1080/09585192.2017.1416655 · Cited by 1,943 · see citing works
Self-citationEstimation issues with PLS and CBSEM: Where the bias lies!
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.06.007 · Cited by 1,889 · see citing works
Self-citationBenitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020) · Information & Management · doi:10.1016/j.im.2019.05.003 · Cited by 1,788 · see citing works
Self-citationMikalef, P., & Gupta, M. (2021) · Information & Management · doi:10.1016/j.im.2021.103434 · Cited by 1,700 · see citing works
An Introduction to Structural Equation Modeling
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom companion: business · doi:10.1007/978-3-030-80519-7_1 · Cited by 1,682 · see citing works
Self-citationSarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022) · Psychology and Marketing · doi:10.1002/mar.21640 · Cited by 1,633 · see citing works
Self-citationAli, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2017) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2016-0568 · Cited by 1,620 · see citing works
Self-citationPartial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022) · Handbook of Market Research · doi:10.1007/978-3-319-57413-4_15 · Cited by 1,483 · see citing works
Self-citationRethinking some of the rethinking of partial least squares
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-10-2018-0665 · Cited by 1,839 · see citing works
Self-citationPartial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021) · Springer eBooks · doi:10.1007/978-3-319-05542-8_15-2 · Cited by 1,408 · see citing works
Self-citationEvaluating E-learning systems success: An empirical study
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350 · see citing works
Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018) · Journal of International Marketing · doi:10.1509/jim.17.0151 · Cited by 1,302 · see citing works
Self-citationAshfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020) · Telematics and Informatics · doi:10.1016/j.tele.2020.101473 · Cited by 1,100 · see citing works
Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling
Henseler, J. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1281780 · Cited by 1,059 · see citing works
Self-citationMikalef, P., & Pateli, A. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.09.004 · Cited by 1,038 · see citing works
Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!
Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020) · International Journal of Market Research · doi:10.1177/1470785320915686 · Cited by 1,034 · see citing works
Self-citationPartial least squares structural equation modeling using SmartPLS: a software review
Sarstedt, M., & Cheah, J. (2019) · Journal of Marketing Analytics · doi:10.1057/s41270-019-00058-3 · Cited by 975 · see citing works
Self-citationDubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021) · International Journal of Production Research · doi:10.1080/00207543.2019.1582820 · Cited by 958 · see citing works
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019) · British Journal of Management · doi:10.1111/1467-8551.12355 · Cited by 881 · see citing works
Executing and interpreting applications of PLS-SEM: updates for family business researchers
Hair, J. F., Astrachan, C. B., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2021) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2020.100392 · Cited by 860 · see citing works
Self-citationCan big data and predictive analytics improve social and environmental sustainability?
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.06.020 · Cited by 860 · see citing works
Mastering digital transformation: The nexus between leadership, agility, and digital strategy
AlNuaimi, B. K., Kumar Singh, S., Ren, S., Budhwar, P., & Vorobyev, D. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.038 · Cited by 820 · see citing works
“SRMR values below 0.08 mean that the data fit the model (Hu and Bentler, 1999; Henseler et al., 2014), whereas for NFI values (which are between 0 and 1), the closer the value is to 1, the better the fit (Lohm ¨ oller, 1989).”
BackgroundInfluentialChin, W. W., Cheah, J., Liu, Y., Ting, H., Lim, X., & Cham, T. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-10-2019-0529 · Cited by 802 · see citing works
Improving PLS-SEM Use for Business Marketing Research
Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.03.010 · Cited by 800 · see citing works
Self-citationWamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.09.019 · Cited by 799 · see citing works
On the emancipation of PLS-SEM: a commentary on Rigdon (2012)
Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014) · Long Range Planning · doi:10.1016/j.lrp.2014.02.007 · Cited by 758 · see citing works
Self-citationCiampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.023 · Cited by 710 · see citing works
“PLS-SEM: indeed a silver bullet” – retrospective observations and recent advances
Sarstedt, M., Hair, J. F., & Ringle, C. M. (2022) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2022.2056488 · Cited by 705 · see citing works
Self-citationTips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management
Cepeda‐Carrión, G., Cegarra‐Navarro, J., & Cillo, V. (2018) · Journal of Knowledge Management · doi:10.1108/jkm-05-2018-0322 · Cited by 702 · see citing works
Job satisfaction and organizational commitment
Naser, V., & Rezaei, S. (2016) · Management Research Review · doi:10.1108/mrr-09-2015-0216 · Cited by 699 · see citing works
When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA
Richter, N., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-11-2019-0638 · Cited by 667 · see citing works
Self-citationPartial Least Squares Path Modeling
Henseler, J. (2017) · International series in quantitative marketing · doi:10.1007/978-3-319-53469-5_12 · Cited by 661 · see citing works
Self-citationBouwman, H., Nikou, S., & de Reuver, M. (2019) · Telecommunications Policy · doi:10.1016/j.telpol.2019.101828 · Cited by 653 · see citing works
de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2019) · Tourism Management · doi:10.1016/j.tourman.2019.104042 · Cited by 646 · see citing works
A critical look at the use of SEM in international business research
Richter, N., Sinkovics, R. R., Ringle, C. M., & Schlaegel, C. (2016) · International Marketing Review · doi:10.1108/imr-04-2014-0148 · Cited by 643 · see citing works
Self-citationUnderstanding the attitude and intention to use smartphone chatbots for shopping
Kasilingam, D. L. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101280 · Cited by 636 · see citing works
Measuring business model innovation: conceptualization, scale development, and proof of performance
Clauß, T. (2016) · R and D Management · doi:10.1111/radm.12186 · Cited by 635 · see citing works
HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling
Roemer, E., Schuberth, F., & Henseler, J. (2021) · Industrial Management & Data Systems · doi:10.1108/imds-02-2021-0082 · Cited by 630 · see citing works
Self-citationEuropean management research using partial least squares structural equation modeling (PLS-SEM)
Richter, N., Cepeda‐Carrión, G., Roldán, J. L., & Ringle, C. M. (2016) · European Management Journal · doi:10.1016/j.emj.2016.08.001 · Cited by 585 · see citing works
Self-citationNitzl, C. (2016) · Journal of Accounting Literature · doi:10.1016/j.acclit.2016.09.003 · Cited by 580 · see citing works
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020) · Decision Sciences · doi:10.1111/deci.12445 · Cited by 580 · see citing works
Self-citationPartial least squares path modeling: Quo vadis?
Henseler, J. (2018) · Quality & Quantity · doi:10.1007/s11135-018-0689-6 · Cited by 578 · see citing works
Self-citationAwan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120766 · Cited by 571 · see citing works
“(4) PLS can be a valuable tool for exploratory research (Henseler et al., 2014).”
BackgroundInternet banking service quality, e-customer satisfaction and loyalty: the modified e-SERVQUAL model
Raza, S. A., Umer, A., Qureshi, M. A., & Dahri, A. S. (2020) · The TQM Journal · doi:10.1108/tqm-02-2020-0019 · Cited by 559 · see citing works
Benitez, J., Arenas, A., Castillo, A., & Esteves, J. (2022) · Information & Management · doi:10.1016/j.im.2022.103590 · Cited by 537 · see citing works
Akour, I. A., Al-Maroof, R. S., Alfaisal, R., & Salloum, S. A. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100052 · Cited by 499 · see citing works
Lokuge, S., Sedera, D., Grover, V., & Dongming Xu, A. (2018) · Information & Management · doi:10.1016/j.im.2018.09.001 · Cited by 482 · see citing works
Dubey, R., Gunasekaran, A., & Childe, S. J. (2019) · Management Decision · doi:10.1108/md-01-2018-0119 · Cited by 480 · see citing works
Authenticity and place attachment of major visitor attractions
Ram, Y., Björk, P., & Weidenfeld, A. (2015) · Tourism Management · doi:10.1016/j.tourman.2015.06.010 · Cited by 479 · see citing works
Awan, U., Arnold, M. G., & Gölgeci̇, İ. (2020) · Business Strategy and the Environment · doi:10.1002/bse.2684 · Cited by 476 · see citing works
Cantele, S., & Zardini, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.02.016 · Cited by 472 · see citing works
Strategic Agility, Business Model Innovation, and Firm Performance: An Empirical Investigation
Clauß, T., Abebe, M. A., Tangpong, C., & Hock, M. (2019) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2910381 · Cited by 456 · see citing works
Nezih Altay, A., Gunasekaran, A., Dubey, R., & Childe, S. J. (2018) · Production Planning & Control · doi:10.1080/09537287.2018.1542174 · Cited by 446 · see citing works
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. R. H. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.01.023 · Cited by 447 · see citing works
Methodological research on partial least squares structural equation modeling (PLS-SEM)
Khan, G. F., Sarstedt, M., Shiau, W., Hair, J. F., Ringle, C. M., & Fritze, M. P. (2019) · Internet Research · doi:10.1108/intr-12-2017-0509 · Cited by 445 · see citing works
Self-citationZollo, L., Filieri, R., Rialti, R., & Yoon, S. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.05.001 · Cited by 445 · see citing works
Ventre, I., & Kolbe, D. (2020) · Journal of International Consumer Marketing · doi:10.1080/08961530.2020.1712293 · Cited by 434 · see citing works
Kristoffersen, E., Mikalef, P., Blomsma, F., & Li, J. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2021.108205 · Cited by 432 · see citing works
Understanding the user satisfaction and loyalty of customer service chatbots
Hsu, C., & Lin, J. C. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103211 · Cited by 420 · see citing works
Albort-Morant, G., Leal‐Rodríguez, A. L., & Marchi, V. D. (2018) · Journal of Knowledge Management · doi:10.1108/jkm-07-2017-0310 · Cited by 418 · see citing works
Internet research using partial least squares structural equation modeling (PLS-SEM)
Shiau, W., Sarstedt, M., & Hair, J. F. (2019) · Internet Research · doi:10.1108/intr-10-2018-0447 · Cited by 395 · see citing works
Self-citationThe role of business analytics capabilities in bolstering firms’ agility and performance
Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.12.005 · Cited by 386 · see citing works
Choi, S. L., Goh, C. F., Adam, M. B. H., & Kowang, T. O. (2016) · Human Resources for Health · doi:10.1186/s12960-016-0171-2 · Cited by 384 · see citing works
Digital workplace and organization performance: Moderating role of digital leadership capability
Chatterjee, S., Chaudhuri, R., Vrontis, D., & Giovando, G. (2023) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2023.100334 · Cited by 379 · see citing works
Going beyond the untold facts in PLS–SEM and moving forward
Hair, J. F., Sarstedt, M., Ringle, C. M., Sharma, P. N., & Liengaard, B. D. (2024) · European Journal of Marketing · doi:10.1108/ejm-08-2023-0645 · Cited by 369 · see citing works
Self-citationTarafdar, M., Maier, C., Laumer, S., & Weitzel, T. (2019) · Information Systems Journal · doi:10.1111/isj.12253 · Cited by 355 · see citing works
Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.11.009 · Cited by 347 · see citing works
Lu, K., Yang, H. H., Shi, Y., & Wang, X. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00238-7 · Cited by 346 · see citing works
This fast car can move faster: a review of PLS-SEM application in higher education research
Ghasemy, M., Teeroovengadum, V., Becker, J.-M., & Ringle, C. M. (2020) · Higher Education · doi:10.1007/s10734-020-00534-1 · Cited by 345 · see citing works
Self-citationBreaking barriers in deployment of renewable energy
Seetharaman, Moorthy, K., Patwa, N., Saravanan, & Gupta, Y. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e01166 · Cited by 341 · see citing works
Using partial least squares structural equation modeling in hospitality and tourism
Uşaklı, A., & Küçükergi̇n, K. G. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-11-2017-0753 · Cited by 337 · see citing works
Vrontis, D., Chaudhuri, R., & Chatterjee, S. (2022) · Sustainability · doi:10.3390/su14137949 · Cited by 335 · see citing works
E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation
Papa, A., Mital, M., Pisano, P., & Giudice, M. D. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.02.018 · Cited by 334 · see citing works
Delić, M., & Eyers, D. R. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107689 · Cited by 324 · see citing works
Benitez, J., Castillo, A., Llorens, J., & Braojos, J. (2018) · Information & Management · doi:10.1016/j.im.2017.09.004 · Cited by 318 · see citing works
Grit in the path to e-learning success
Aparício, M., Bação, F., & Oliveira, T. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.10.009 · Cited by 318 · see citing works
Limaj, E., & Bernroider, E. W. N. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2017.10.052 · Cited by 313 · see citing works
Partial least squares path modeling: Time for some serious second thoughts
Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016) · Journal of Operations Management · doi:10.1016/j.jom.2016.05.002 · Cited by 312 · see citing works
Nguyen‐Viet, B., Dang, H. P., & Nguyen, H. H. (2020) · Cogent Business & Management · doi:10.1080/23311975.2020.1796249 · Cited by 312 · see citing works
Statistical Inference with PLSC Using Bootstrap Confidence Intervals1
Aguirre-Urreta, M. I., & Rönkkö, M. (2018) · MIS Quarterly · doi:10.25300/misq/2018/13587 · Cited by 304 · see citing works
Hajiali, I., Kessi, A. M. F., Budiandriani, B., Prihatin, E., Sufri, M., & Sudirman, A. (2022) · Golden Ratio of Human Resource Management · doi:10.52970/grhrm.v2i1.160 · Cited by 304 · see citing works
Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Vallespín-Arán, M. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.04.012 · Cited by 303 · see citing works
Roh, M., & Park, K. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.09.017 · Cited by 303 · see citing works
Social media interaction, the university brand and recruitment performance
Rutter, R. N., Roper, S., & Lettice, F. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.01.025 · Cited by 296 · see citing works
Prediction‐Oriented Model Selection in Partial Least Squares Path Modeling
Sharma, P. N., Galit Shmueli, A., Sarstedt, M., Danks, N. P., & Ray, S. (2018) · Decision Sciences · doi:10.1111/deci.12329 · Cited by 294 · see citing works
Self-citationImpact of Organizational Culture Values on Organizational Agility
Felipe, C. M., Roldán, J. L., & Leal‐Rodríguez, A. L. (2017) · Sustainability · doi:10.3390/su9122354 · Cited by 293 · see citing works
Reverte, C., Gómez-Melero, E., & Cegarra‐Navarro, J. (2015) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.09.128 · Cited by 292 · see citing works
Brill, T. M., Munoz, L., & Miller, R. J. (2019) · Journal of Marketing Management · doi:10.1080/0267257x.2019.1687571 · Cited by 291 · see citing works
Svensson, G., Ferro, C., Høgevold, N. M., Padín, C., Varela, J. C. S., & Sarstedt, M. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.06.226 · Cited by 288 · see citing works
Self-citationReviewing the SmartPLS 4 software: the latest features and enhancements
Cheah, J., Magno, F., & Cassia, F. (2023) · Journal of Marketing Analytics · doi:10.1057/s41270-023-00266-y · Cited by 288 · see citing works
- Authors
- Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J.
- Affiliations
- Radboud University Nijmegen (NL)
- University of Groningen (NL)
- University of Newcastle Australia (AU)
- University of Vienna (AT)
- University System of Georgia (US)
- Auburn University (US)
- Kennesaw State University (US)
- Michigan State University (US)
- Corresponding author
- Henseler, Jörg
- ORCID
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2014-04
- Volume
- 17
- Issue
- 2
- Pages
- 182-209
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2100739170
- Cited by
- 3,332
- References
- 111
- Citations by year
- 2026: 327 · 2025: 396 · 2024: 433 · 2023: 458 · 2022: 424 · 2021: 349 · 2020: 261 · 2019: 230 · 2018: 177 · 2017: 116 · 2016: 106 · 2015: 40 · 2014: 14 · 2012: 1
- FWCI
- 115.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Organizational Research Methods (publishedVersion, cc-by-nc)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by-nc)
- University of Twente Research Information (publishedVersion, cc-by)
- DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion, cc-by-nc)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by-nc)
- Radboud Repository (Radboud University) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc)
- University of Twente Research Information (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- partial least squares, structural equation modeling, common factor model, simulation study, exploratory research, methodological considerations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
