Using confirmatory composite analysis to assess emergent variables in business research

Confirmatory composite analysis (CCA) was invented by Jorg Henseler and Theo K. Dijkstra in 2014 and elaborated by Schuberth et al. (2018b) as an innovative set of procedures for specifying and assessing composite models. Composite models consist of two or more interrelated constructs, all of which emerge as linear combinations of extant variables, hence the term ‘emergent variables’. In a recent JBR paper, Hair et al. (2020) mistook CCA for the measurement model evaluation step of partial least squares structural equation modeling. In order to clear up potential confusion among JBR readers, the paper at hand explains CCA as it was originally developed, including its key steps: model specification, identification, estimation, and assessment. Moreover, it illustrates the use of CCA by means of an empirical study on business value of information technology. A final discussion aims to help analysts in business research to decide which type of covariance structure analysis to use.

[1]  H. Wold.  Nonlinear Iterative Partial Least Squares (NIPALS) Modelling: Some Current Developments , 1973 .

[2]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[3]  Florian Schuberth,et al.  How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research , 2020, Inf. Manag..

[4]  Joseph F. Hair,et al.  Estimation issues with PLS and CBSEM: Where the bias lies! ☆ , 2016 .

[5]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[6]  Po-Hsien Huang.  Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling , 2017, Psychometrika.

[7]  Jessica Braojos,et al.  How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence , 2019, Inf. Manag..

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

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

[10]  Denny Borsboom,et al.  Worse than measurement error: Consequences of inappropriate latent variable measurement models. , 2020, Psychological methods.

[11]  L. Sajtos,et al.  Auxiliary theories as translation mechanisms for measurement model specification , 2016 .

[12]  Florian Schuberth,et al.  Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites , 2020, Ind. Manag. Data Syst..

[13]  J. Henseler,et al.  Interplay of relational and contractual governance in public-private partnerships: The mediating role of relational norms, trust and partners' contribution , 2018 .

[14]  K. Yuan.  Fit Indices Versus Test Statistics , 2005, Multivariate behavioral research.

[15]  Alexeis Garcia-Perez,et al.  Turning heterogeneity into improved research outputs in international R&D teams , 2019 .

[16]  William Lewis,et al.  A Multicollinearity and Measurement Error Statistical Blind Spot: Correcting for Excessive False Positives in Regression and PLS , 2017, MIS Q..

[17]  Scott E. Maxwell,et al.  Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. , 1993 .

[18]  George A. Marcoulides,et al.  Assessing Structural Equation Models by Equivalence Testing With Adjusted Fit Indexes , 2016 .

[19]  Theo K. Dijkstra,et al.  A Perfect Match Between a Model and a Mode , 2017 .

[20]  Jörg Henseler,et al.  Partial least squares path modeling using ordinal categorical indicators , 2016, Quality & Quantity.

[21]  Detmar W. Straub,et al.  Specifying Formative Constructs in Information Systems Research , 2007, MIS Q..

[22]  Edgar C. Merkle,et al.  The problem of model selection uncertainty in structural equation modeling. , 2012, Psychological methods.

[23]  J. Henseler,et al.  Linear indices in nonlinear structural equation models: best fitting proper indices and other composites , 2011 .

[24]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[25]  J. Kettenring,et al.  Canonical Analysis of Several Sets of Variables , 2022 .

[26]  CONSISTENCY AND IDENTIFIABILITY REVISITED , 2002 .

[27]  S. Mulaik,et al.  EVALUATION OF GOODNESS-OF-FIT INDICES FOR STRUCTURAL EQUATION MODELS , 1989 .

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

[29]  T. Dijkstra.  Some comments on maximum likelihood and partial least squares methods , 1983 .

[30]  Concha Bielza,et al.  Bayesian Sparse Partial Least Squares , 2013, Neural Computation.

[31]  Daniel Ruiz-Palomo,et al.  Family Management and Firm Performance in Family SMEs: The Mediating Roles of Management Control Systems and Technological Innovation , 2019, Sustainability.

[32]  Y. Rosseel,et al.  Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit , 2018 .

[33]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[34]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[35]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[36]  Florian Schuberth,et al.  Confirmatory composite analysis using partial least squares: setting the record straight , 2020, Review of Managerial Science.

[37]  Y. Takane,et al.  Generalized structured component analysis , 2004 .

[38]  S. Embretson,et al.  Personality Measurement Issues Viewed Through the Eyes of IRT , 1999 .

[39]  K. Jöreskog.  A general approach to confirmatory maximum likelihood factor analysis , 1969 .

[40]  A. McQuarrie,et al.  Regression and Time Series Model Selection , 1998 .

[41]  Jörg Henseler,et al.  Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors , 2017, Ind. Manag. Data Syst..

[42]  R. Beran,et al.  Bootstrap Tests and Confidence Regions for Functions of a Covariance Matrix , 1985 .

[43]  Silvia Martelo-Landroguez,et al.  Uncontrolled counter-knowledge: its effects on knowledge management corridors , 2019, Knowledge Management Research & Practice.

[44]  Jörg Henseler,et al.  Confirmatory Composite Analysis , 2018, Front. Psychol..

[45]  J. Coan.  Emergent Ghosts of the Emotion Machine , 2010 .

[46]  Florian Schuberth,et al.  Investigating the effects of tourist engagement on satisfaction and loyalty , 2019, The Service Industries Journal.

[47]  Juan-Gabriel Cegarra-Navarro,et al.  Overcoming knowledge barriers to health care through continuous learning , 2019, J. Knowl. Manag..

[48]  Florian Schuberth,et al.  cSEM: Composite-Based Structural Equation Modeling , 2020 .

[49]  Antonio L. Leal-Rodríguez,et al.  An explanatory and predictive model for organizational agility , 2016 .

[50]  J. Henseler.  Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling , 2017 .

[51]  Andreas Ritter,et al.  Structural Equations With Latent Variables , 2016 .

[52]  Hefu Liu,et al.  Supply Chain Information Integration and Firm Performance: Are Explorative and Exploitative IT Capabilities Complementary or Substitutive? , 2020, Decis. Sci..

[53]  Marko Sarstedt,et al.  Quantify uncertainty in behavioral research , 2020, Nature Human Behaviour.

[54]  A. García-Pérez,et al.  An open-minded strategy towards eco-innovation: A key to sustainable growth in a global enterprise , 2019, Technological Forecasting and Social Change.

[55]  Nathan S. Hartman,et al.  Method Variance and Marker Variables: A Review and Comprehensive CFA Marker Technique , 2010 .

[56]  R. Nuzzo.  How scientists fool themselves – and how they can stop , 2015, Nature.

[57]  Jörg Henseler,et al.  Is the whole more than the sum of its parts? On the interplay of marketing and design research , 2015 .

[58]  Felipe Hernández-Perlines,et al.  Training and business performance: the mediating role of absorptive capacities , 2016, SpringerPlus.

[59]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[60]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[61]  Scott L. Hershberger,et al.  The New Rules of Measurement : What Every Psychologist and Educator Should Know , 1999 .

[62]  V. Savalei,et al.  When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. , 2012, Psychological methods.

[63]  Manuel J. Sánchez-Franco,et al.  Understanding relationship quality in hospitality services , 2019, Internet Res..

[64]  Wynne W. Chin,et al.  You Write, but Others Read: Common Methodological Misunderstandings in PLS and Related Methods , 2013 .

[65]  Heungsun Hwang,et al.  Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error , 2017, Front. Psychol..

[66]  D. Borsboom.  Latent Variable Theory , 2008 .

[67]  Heungsun Hwang,et al.  Bayesian generalized structured component analysis. , 2020, The British journal of mathematical and statistical psychology.

[68]  Eric W.T. Ngai,et al.  Impact of Service-Dominant Orientation on the Innovation Performance of Technology Firms: Roles of Knowledge Sharing and Relationship Learning , 2020, Decis. Sci..

[69]  F. Chirico,et al.  Psychological Ownership, Knowledge Sharing and Entrepreneurial Orientation in Family Firms: The Moderating Role of Governance Heterogeneity , 2017 .

[70]  Y. Rosseel,et al.  Local fit evaluation of structural equation models using graphical criteria. , 2017, Psychological methods.

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

[72]  Jose Benitez-Amado,et al.  How information technology influences opportunity exploration and exploitation firm's capabilities , 2018, Inf. Manag..

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

[74]  Detmar W. Straub,et al.  Common Beliefs and Reality About PLS , 2014 .

[75]  H. Bozdogan.  Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[76]  Joe F. Hair,et al.  Assessing measurement model quality in PLS-SEM using confirmatory composite analysis , 2020 .

[77]  Laura Rueda,et al.  From traditional education technologies to student satisfaction in Management education: A theory of the role of social media applications , 2017, Inf. Manag..

[78]  Florin Sabin Foltean,et al.  Customer relationship management capabilities and social media technology use: Consequences on firm performance , 2019, Journal of Business Research.

[79]  J. Edwards.  Multidimensional Constructs in Organizational Behavior Research: An Integrative Analytical Framework , 2001 .

[80]  Joseph F. Hair,et al.  When to use and how to report the results of PLS-SEM , 2019, European Business Review.

[81]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[82]  José L. Roldán,et al.  Antecedents and consequences of knowledge management performance: the role of IT infrastructure , 2018, Intangible Capital.

[83]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[84]  M. Miller,et al.  Sample Size Requirements for Structural Equation Models , 2013, Educational and psychological measurement.

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

[86]  Jacob Cohen,et al.  Problems in the Measurement of Latent Variables in Structural Equations Causal Models , 1990 .

[87]  Geoffrey S. Hubona,et al.  Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..

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

[89]  Gautam Ray,et al.  Impact of Information Technology Infrastructure Flexibility on Mergers and Acquisitions , 2018, MIS Q..

[90]  P. Barrett.  Structural equation modelling : Adjudging model fit , 2007 .

[91]  W. G. Brown,et al.  Multicollinearity problems and ridge regression in sociological models , 1975 .

[92]  Christie M. Fuller,et al.  Common methods variance detection in business research , 2016 .

[93]  Jörg Henseler,et al.  Consistent Partial Least Squares Path Modeling , 2015, MIS Q..

[94]  K. Schermelleh-Engel,et al.  Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. , 2003 .

[95]  S. West,et al.  Model fit and model selection in structural equation modeling. , 2012 .