Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method

Purpose – The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat unobserved heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat unobserved heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...

[1]  Marko Sarstedt,et al.  Do We Fully Understand the Critical Success Factors of Customer Satisfaction with Industrial Goods? - Extending Festge and Schwaiger’s Model to Account for Unobserved Heterogeneity , 2009 .

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

[3]  Marko Sarstedt,et al.  How collinearity affects mixture regression results , 2015 .

[4]  Douglas Steinley,et al.  Local optima in K-means clustering: what you don't know may hurt you. , 2003, Psychological methods.

[5]  M. Sarstedt.  A review of recent approaches for capturing heterogeneity in partial least squares path modelling , 2008 .

[6]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

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

[8]  Ralf Wilden,et al.  The impact of dynamic capabilities on operational marketing and technological capabilities: investigating the role of environmental turbulence , 2015 .

[9]  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.

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

[11]  W. DeSarbo,et al.  An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data , 1993 .

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

[13]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers , 2014 .

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

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

[16]  Lutz Kaufmann,et al.  A structured review of partial least squares in supply chain management research , 2015 .

[17]  Kevin J. Trainor,et al.  Performance implications of customer-linking capabilities: Examining the complementary role of customer orientation and CRM technology , 2010 .

[18]  Marko Sarstedt,et al.  Market segmentation with mixture regression models: Understanding measures that guide model selection , 2008 .

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

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

[21]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[22]  Wynne W. Chin,et al.  An Introduction to a Permutation Based Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on Simulated Data and a Cross Cultural Analysis of the Sourcing of Information System Services Between Germany and the USA , 2010 .

[23]  Marko Sarstedt,et al.  Testing measurement invariance of composites using partial least squares , 2016 .

[24]  R. Jaszczak,et al.  Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing , 1992 .

[25]  Marko Sarstedt,et al.  Guidelines for treating unobserved heterogeneity in tourism research: A comment on Marques and Reis (2015) , 2016 .

[26]  Marko Sarstedt,et al.  Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – A case study , 2016 .

[27]  Marko Sarstedt,et al.  The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications , 2012 .

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

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

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

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

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

[33]  Jörg Henseler,et al.  Exploring Unanticipated Consequences of Strategy Amongst Stakeholder Segments: The Case of a European Revenue Service , 2012 .

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

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

[36]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .

[37]  Marko Sarstedt,et al.  A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics , 2011 .

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

[39]  Joseph F. Hair,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[40]  Marko Sarstedt,et al.  Assessing Heterogeneity in Customer Satisfaction Studies: Across Industry Similarities and within Industry Differences , 2011 .