Common Beliefs and Reality about Partial Least Squares : Comments on Rönkkö and Evermann

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. Institute for Management Research, Radboud University Nijmegen, Nijmegen, the Netherlands ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal Faculty of Economics and Business, University of Groningen, Groningen, the Netherlands Otto-von-Guericke University Magdeburg, Magdeburg, Germany University of Newcastle, Callaghan, Australia Hamburg University of Technology, Hamburg, Germany University of Vienna, Vienna, Austria J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, USA Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA Coles College of Business, Kennesaw State University, Kennesaw, GA, USA Broad College of Business, Michigan State University, East Lansing, MI, USA Corresponding Author: Jörg Henseler, Institute for Management Research, Radboud University Nijmegen, Thomas van Aquinostraat 3, 6525 GD Nijmegen, the Netherlands. Email: j.henseler@fm.ru.nl Organizational Research Methods 2014, Vol. 17(2) 182-209 a The Author(s) 2014 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1094428114526928 orm.sagepub.com at KENNESAW STATE UNIV on May 27, 2016 orm.sagepub.com Downloaded from

[1]  Fujun Lai,et al.  Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research , 2012 .

[2]  Joerg Henseler Why generalized structured component analysis is not universally preferable to structural equation modeling , 2012 .

[3]  M. Tenenhaus Component-based Structural Equation Modelling , 2008 .

[4]  P. Coelho,et al.  Likelihood and PLS Estimators for Structural Equation Modeling: An Assessment of Sample Size, Skewness and Model Misspecification Effects , 2013 .

[5]  R. Gill,et al.  Conditions for factor (in)determinacy in factor analysis , 1998 .

[6]  B. Efron,et al.  Bootstrap confidence intervals , 1996 .

[7]  Pedro Simões Coelho,et al.  Comparison of Likelihood and PLS Estimators for Structural Equation Modeling: A Simulation with Customer Satisfaction Data , 2010 .

[8]  Edward E. Rigdon,et al.  Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead , 2014 .

[9]  P M Bentler,et al.  Multistructure Statistical Model Applied To Factor Analysis. , 1976, Multivariate behavioral research.

[10]  Lutz Hildebrandt,et al.  A Comparison of Current PLS Path Modeling Software: Features, Ease-of-Use, and Performance , 2010 .

[11]  N. L. Johnson,et al.  Multivariate Analysis , 1958, Nature.

[12]  M. Sarstedt,et al.  Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments? , 2011 .

[13]  Abraham Kaplan,et al.  Definition and Specification of Meaning , 1946 .

[14]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[15]  John J. Sosik,et al.  Silver Bullet or Voodoo Statistics? , 2009 .

[16]  Marko Sarstedt,et al.  PLS path modeling and evolutionary segmentation , 2013 .

[17]  Jan-Bernd Lohmöller,et al.  Latent Variable Path Modeling with Partial Least Squares , 1989 .

[18]  Marko Sarstedt,et al.  Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance , 2013 .

[19]  Stacie Petter,et al.  On the use of partial least squares path modeling in accounting research , 2011, Int. J. Account. Inf. Syst..

[20]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[21]  Carol Saunders,et al.  PLS: A Silver Bullet? , 2006 .

[22]  N Cliff,et al.  Some Cautions Concerning The Application Of Causal Modeling Methods. , 1983, Multivariate behavioral research.

[23]  H. Wold Causal flows with latent variables: Partings of the ways in the light of NIPALS modelling , 1974 .

[24]  J. Henseler Partial least squares path modeling: Quo vadis? , 2018, Quality & Quantity.

[25]  Christian Homburg,et al.  Covariance structure analysis via specification searches , 1992 .

[26]  Yves Rosseel,et al.  lavaan: An R Package for Structural Equation Modeling , 2012 .

[27]  William Lewis,et al.  Does PLS Have Advantages for Small Sample Size or Non-Normal Data? , 2012, MIS Q..

[28]  Paul E. Tesluk,et al.  A Comparison of Approaches to Forming Composite Measures in Structural Equation Models , 2000 .

[29]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .

[30]  Marko Sarstedt,et al.  Applications of Partial Least Squares Path Modeling in Management Journals: A Review of Past Practices and Recommendations for Future Applications , 2012 .

[31]  A. Greenwald,et al.  Under what conditions does theory obstruct research progress? , 1986, Psychological review.

[32]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and Voice Mail Emotion/Adoption Study , 1996, ICIS.

[33]  Edward E. Rigdon,et al.  Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods , 2012 .

[34]  Fred L. Bookstein,et al.  Partial least squares analysis in developmental psychopathology , 1989, Development and Psychopathology.

[35]  Wynne W. Chin,et al.  A Comparison of Approaches for the Analysis of Interaction Effects Between Latent Variables Using Partial Least Squares Path Modeling , 2010 .

[36]  Christian Derquenne,et al.  A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy , 2007, Comput. Stat. Data Anal..

[37]  Friedrich Leisch,et al.  semPLS: Structural Equation Modeling Using Partial Least Squares , 2012 .

[38]  Michael Scharkow,et al.  The Relative Trustworthiness of Inferential Tests of the Indirect Effect in Statistical Mediation Analysis , 2013, Psychological science.

[39]  A. Tenenhaus,et al.  Regularized Generalized Canonical Correlation Analysis , 2011, Eur. J. Oper. Res..

[40]  Irene R. R. Lu,et al.  Two new methods for estimating structural equation models: An illustration and a comparison with two established methods , 2011 .

[41]  William Lewis,et al.  PLS, Small Sample Size, and Statistical Power in MIS Research , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[42]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[43]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[44]  Arun Rai,et al.  Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats , 2013, MIS Q..

[45]  Ming-Mei Wang,et al.  Some new results on factor indeterminacy , 1972 .

[46]  Joseph F. Hair,et al.  On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012) , 2014 .

[47]  Mikko Rönkkö,et al.  A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling , 2013 .

[48]  P. Bentler,et al.  Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification , 1998 .

[49]  Kenneth A. Bollen,et al.  Monte Carlo Experiments: Design and Implementation , 2001 .

[50]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[51]  Michael J. Ryan,et al.  Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares , 2010 .

[52]  Jan-Bernd Lohmoller,et al.  The PLS Program System: Latent Variables Path Analysis with Partial Least Squares Estimation. , 1988, Multivariate behavioral research.

[53]  Jodie B. Ullman,et al.  Structural Equation Modeling: Reviewing the Basics and Moving Forward , 2006, Journal of personality assessment.

[54]  Philippe Jacquart,et al.  On making causal claims: A review and recommendations , 2010 .

[55]  Peter F. Halpin,et al.  Manifest and Latent Variates , 2008 .

[56]  Lopo L. Rego The Relationship Market Structure-Market Efficiency From a Customer Satisfaction Perspective , 1998 .

[57]  Marko Sarstedt,et al.  Goodness-of-fit indices for partial least squares path modeling , 2013, Comput. Stat..

[58]  Marcia J. Simmering,et al.  Control Variable Use and Reporting in Macro and Micro Management Research , 2012 .

[59]  Arun Rai,et al.  Predictive Validity and Formative Measurement in Structural Equation Modeling: Embracing Practical Relevance , 2013, ICIS.

[60]  Herman Wold,et al.  Model Construction and Evaluation When Theoretical Knowledge Is Scarce , 1980 .

[61]  Herman Wold,et al.  Soft modelling: The Basic Design and Some Extensions , 1982 .

[62]  Marc A. Tomiuk,et al.  A Comparative Study on Parameter Recovery of Three Approaches to Structural Equation Modeling , 2010 .

[63]  Marko Sarstedt,et al.  Genetic algorithm segmentation in partial least squares structural equation modeling , 2013, OR Spectrum.

[64]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[65]  J. Hammersley SIMULATION AND THE MONTE CARLO METHOD , 1982 .

[66]  R. P. McDonald,et al.  Path Analysis with Composite Variables. , 1996, Multivariate behavioral research.

[67]  Karl G. Jöreskog,et al.  Lisrel 8: Structural Equation Modeling With the Simplis Command Language , 1993 .

[68]  D. Straub,et al.  Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly , 2012 .

[69]  Harsharanjeet S. Jagpal Multicollinearity in Structural Equation Models with Unobservable Variables , 1982 .

[70]  Vincenzo Esposito Vinzi,et al.  PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement , 2010 .

[71]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[72]  Moritz Heene,et al.  Sensitivity of SEM Fit Indexes With Respect to Violations of Uncorrelated Errors , 2012 .

[73]  Marko Sarstedt,et al.  An assessment of the use of partial least squares structural equation modeling in marketing research , 2012 .

[74]  F. Bookstein,et al.  Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory , 1982 .

[75]  Jörg Henseler,et al.  On the convergence of the partial least squares path modeling algorithm , 2010, Comput. Stat..

[76]  Mostafa El Qannari,et al.  An alternative algorithm to the PLS B problem , 2005, Comput. Stat. Data Anal..