An assessment of the use of partial least squares structural equation modeling in marketing research

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.

[1]  A. Diamantopoulos,et al.  Using Formative Measures in International Marketing Models: A Cautionary Tale Using Consumer Animosity as an Example , 2011 .

[2]  Donald G. Gardner,et al.  Focus of attention at work: Construct definition and empirical validation , 1989 .

[3]  D. Wittink,et al.  A model of consumer perceptions and store loyalty intentions for a supermarket retailer , 1998 .

[4]  Don Y. Lee,et al.  The impact of firms' risk-taking attitudes on advertising budgets , 1994 .

[5]  Martin Wetzels,et al.  On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies , 2009 .

[6]  J. R. Larson,et al.  Research strategies and tactics in industrial and organizational psychology. , 1990 .

[7]  Marko Sarstedt,et al.  Response-Based Segmentation Using Finite Mixture Partial Least Squares - Theoretical Foundations and an Application to American Customer Satisfaction Index Data , 2010, Data Mining.

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

[9]  Hans Baumgartner,et al.  On the use of structural equation models for marketing modeling , 2000 .

[10]  M. Reimann,et al.  Worldwide Faculty Perceptions of Marketing Journals: Rankings, Trends, Comparisons, and Segmentations , 2009 .

[11]  Tenko Raykov,et al.  Reliability if deleted, not 'alpha if deleted': evaluation of scale reliability following component deletion. , 2007, The British journal of mathematical and statistical psychology.

[12]  J. Rossiter,et al.  Tailor-made single-item measures of doubly concrete constructs , 2009 .

[13]  Henry E. Kyburg,et al.  Comparison of Approaches , 1974 .

[14]  Nigel Slack,et al.  The Importance‐Performance Matrix as a Determinant of Improvement Priority , 1994 .

[15]  H. Winklhofer,et al.  Index Construction with Formative Indicators: An Alternative to Scale Development , 2001 .

[16]  Rachna Shah,et al.  Use of structural equation modeling in operations management research: Looking back and forward ☆ , 2006 .

[17]  A. Herrmann,et al.  The Evolution of Loyalty Intentions , 2006 .

[18]  V. Theoharakis,et al.  Perceptual Differences of Marketing Journals: A Worldwide Perspective , 2002 .

[19]  M. D. Dunnette Handbook of Industrial and Organizational Psychology , 2005 .

[20]  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 .

[21]  Michael T. Brannick,et al.  Critical comments on applying covariance structure modeling , 1995 .

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

[23]  H. Kwon,et al.  The Feasibility of Single-Item Measures in Sport Loyalty Research , 2005 .

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

[25]  Scott B. MacKenzie,et al.  Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques , 2011, MIS Q..

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

[27]  W. DeSarbo,et al.  Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity , 1997 .

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

[29]  T. Hennig-Thurau,et al.  The Role of Parent Brand Quality for Service Brand Extension Success , 2010 .

[30]  Theo K. Dijkstra,et al.  Handbook of partial least squares. Concepts, methods and applications in marketing and related fields , 2008 .

[31]  N. Malhotra,et al.  Marketing research an applied orientation / Oleh Naresh K Malhotra , 1999 .

[32]  George A. Marcoulides,et al.  Modern methods for business research , 1998 .

[33]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[34]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[35]  E. Abt Understanding statistics 3 , 2010, Evidence-Based Dentistry.

[36]  Kenneth A. Bollen,et al.  Causal Indicator Models: Identification, Estimation, and Testing , 2009 .

[37]  J. R. Larson,et al.  Research strategies and tactics in I/O psychology , 1990 .

[38]  C. Saunders,et al.  Editor's comments: PLS: a silver bullet? , 2006 .

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

[40]  J. Hair Multivariate data analysis , 1972 .

[41]  Adamantios Diamantopoulos,et al.  Using single-item measures for construct measurement in management research Conceptual issues and application guidelines , 2009 .

[42]  P. Hackl,et al.  Robustness of partial least-squares method for estimating latent variable quality structures , 1999 .

[43]  S. Geisser A predictive approach to the random effect model , 1974 .

[44]  R. Bagozzi,et al.  On the evaluation of structural equation models , 1988 .

[45]  G. Hult,et al.  Faculty Perceptions of Marketing Journals , 1997 .

[46]  Siegfried P. Gudergan,et al.  Confirmatory Tetrad Analysis in PLS Path Modeling , 2008 .

[47]  Jan-Benedict E. M. Steenkamp,et al.  The use of LISREL in validating marketing constructs. , 1991 .

[48]  Viswanath Venkatesh,et al.  Model of Migration and Use of Platforms: Role of Hierarchy, Current Generation, and Complementarities in Consumer Settings , 2010, Manag. Sci..

[49]  Judy A. Siguaw,et al.  Formative versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration , 2006 .

[50]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[51]  P. Bentler,et al.  Evaluating model fit. , 1995 .

[52]  Wynne W. Chin,et al.  Structural equation modeling analysis with small samples using partial least squares , 1999 .

[53]  David J. Ketchen,et al.  AN ASSESSMENT OF THE USE OF STRUCTURAL EQUATION MODELING IN STRATEGIC MANAGEMENT RESEARCH , 2004 .

[54]  M. C. Hill,et al.  Evaluating Model Fit , 2005 .

[55]  Rolph E. Anderson,et al.  Multivariate Data Analysis (7th ed. , 2009 .

[56]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[57]  Emin Babakus,et al.  The Sensitivity of Confirmatory Maximum Likelihood Factor Analysis to Violations of Measurement Scale and Distributional Assumptions , 1987 .

[58]  J. Rossiter,et al.  The Predictive Validity of Multiple-Item versus Single-Item Measures of the Same Constructs , 2007 .

[59]  Michael R Chernick,et al.  Bootstrap Methods: A Guide for Practitioners and Researchers , 2007 .

[60]  R. P. McDonald,et al.  Structural Equations with Latent Variables , 1989 .

[61]  B. Efron Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods , 1981 .

[62]  Gina J. Medsker,et al.  A Review of Current Practices for Evaluating Causal Models in Organizational Behavior and Human Resources Management Research , 1994 .

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

[64]  A. Kaplan,et al.  A Beginner's Guide to Partial Least Squares Analysis , 2004 .

[65]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[66]  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..

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

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

[69]  R. Hoyle Structural equation modeling: concepts, issues, and applications , 1997 .

[70]  R. Hoyle Statistical Strategies for Small Sample Research , 1999 .

[71]  Adamantios Diamantopoulos,et al.  Advancing formative measurement models , 2008 .

[72]  Claudia van Oppen,et al.  USING PLS PATH MODELING FOR ASSESSING HIERARCHICAL CONSTRUCT MODELS : GUIDELINES AND EMPIRICAL , 2022 .

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

[74]  C. Mann,et al.  A Practical Treatise on Diseases of the Skin , 1889, Atlanta Medical and Surgical Journal (1884).

[75]  Fred L. Bookstein,et al.  Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling. , 1977 .

[76]  Bruce Slutsky,et al.  Chemometrics: A Practical Guide By Kenneth R. Beebe, Randy J. Pell, and Mary Beth Seasholtz. Wiley-Interscience Series on Laboratory Automation. John Wiley & Sons: New York, 1998, xi + 348 pp, ISBN 0-471-12451-6 , 1998, Journal of chemical information and computer sciences.

[77]  K. Bollen,et al.  A tetrad test for causal indicators. , 2000, Psychological methods.

[78]  V. E. Vinzi,et al.  A global Goodness – of – Fit index for PLS structural equation modelling 1 , 2004 .

[79]  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).

[80]  D. Stewart,et al.  The role of method: some parting thoughts from a departing editor , 2009 .

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

[82]  Ronald T. Cenfetelli,et al.  Interpretation of Formative Measurement in Information Systems Research , 2009, MIS Q..

[83]  Barry J. Babin,et al.  Publishing Research in Marketing Journals Using Structural Equation Modeling , 2008 .

[84]  Marko Sarstedt,et al.  Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results , 2011 .

[85]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[86]  Christian Homburg,et al.  Applications of structural equation modeling in marketing and consumer research: A review , 1996 .

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

[88]  A. Boomsma,et al.  The robustness of LISREL modeling revisted. , 2001 .

[89]  M. Sarstedt,et al.  Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies , 2010 .

[90]  H. Wold Path Models with Latent Variables: The NIPALS Approach , 1975 .

[91]  C. Stein,et al.  Structural equation modeling. , 2012, Methods in molecular biology.

[92]  Jörg Henseler,et al.  Testing Moderating Effects in PLS Path Models. An Illustration of Available Procedures , 2005 .

[93]  R. Pieters,et al.  The Structural Influence of Marketing Journals: A Citation Analysis of the Discipline and its Subareas over Time , 2003 .

[94]  David F. Midgley,et al.  Formative versus reflective measurement models: two applications of formative measurement | NOVA. The University of Newcastle's Digital Repository , 2008 .

[95]  Marko Sarstedt,et al.  Structural modeling of heterogeneous data with partial least squares , 2010 .

[96]  T. Dijkstra Latent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares , 2010 .

[97]  Wynne W. Chin How to Write Up and Report PLS Analyses , 2010 .

[98]  Frank Huber,et al.  Capturing Customer Heterogeneity using a Finite Mixture PLS Approach , 2002 .

[99]  R. Bagozzi Principles of marketing research , 1994 .

[100]  K. Jöreskog Structural analysis of covariance and correlation matrices , 1978 .

[101]  R. Fisher,et al.  The effects of relationship quality on customer retaliation , 2006 .

[102]  Cheryl Burke Jarvis,et al.  A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research , 2003 .

[103]  Kenneth A. Bollen,et al.  Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models , 2011, MIS Q..

[104]  Christian M. Ringle,et al.  Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples , 2010 .

[105]  Adamantios Diamantopoulos,et al.  The error term in formative measurement models: interpretation and modeling implications , 2006 .

[106]  Gilbert A. Churchill,et al.  Marketing Research: Methodological Foundations , 1976 .