Why PLS-SEM is suitable for complex modeling? An empirical illustration in Big Data Analytics Quality

Abstract The emergence of multivariate analysis techniques transforms empirical validation of theoretical concepts in social science and business research. In this context, structural equation modelling (SEM) has emerged as a powerful tool to estimate conceptual models linking two or more latent constructs. This paper shows the suitability of the partial least squares (PLS) approach to SEM (PLS-SEM) in estimating a complex model drawing on the philosophy of verisimilitude and the methodology of soft modelling assumptions. The results confirm the utility of PLS-SEM as a promising tool to estimate a complex, hierarchical model in the domain of big data analytics quality.

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

[2]  Detmar W. Straub,et al.  An Update and Extension to SEM Guidelines for Admnistrative and Social Science Research , 2011 .

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

[4]  Peter M Bentler,et al.  On Components, Latent Variables, PLS and Simple Methods: Reactions to Rigdon's Rethinking of PLS. , 2014, Long range planning.

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

[6]  David Kiron,et al.  The analytics mandate , 2014 .

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

[8]  J. Jacoby.  Consumer Research: A State of the Art Review , 1978 .

[9]  Dominic Barton,et al.  Making advanced analytics work for you. , 2012, Harvard business review.

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

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

[12]  Gerald C. Kane.  The American Red Cross: : Adding digital volunteers to its ranks , 2014 .

[13]  Shahriar Akter,et al.  Big data analytics in E-commerce: a systematic review and agenda for future research , 2016, Electronic Markets.

[14]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[15]  Cheryl Burke Jarvis,et al.  The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. , 2005, The Journal of applied psychology.

[16]  Jeanne G. Harris,et al.  Competing on Analytics: The New Science of Winning , 2007 .

[17]  D. Iacobucci,et al.  Modeling Dyadic Interactions and Networks in Marketing , 1992 .

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

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

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

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

[22]  Marko Sarstedt,et al.  Editorial - Partial Least Squares: The Better Approach to Structural Equation Modeling? , 2012 .

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

[24]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[25]  Frans J. Oort,et al.  Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study , 2010 .

[26]  Shahriar Akter,et al.  Trustworthiness in mHealth information services: An assessment of a hierarchical model with mediating and moderating effects using partial least squares (PLS) , 2011, J. Assoc. Inf. Sci. Technol..

[27]  Marko Sarstedt,et al.  Corrigendum to “Editorial Partial Least Squares: The Better Approach to Structural Equation Modeling?” [LRP 45/5-6 (2012) 312–319] , 2014 .

[28]  E. Ngai,et al.  An empirical analysis of inter-organisational value co-creation in a supply chain: a process perspective , 2015 .

[29]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

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

[31]  P. Meehl.  Appraising and Amending Theories: The Strategy of Lakatosian Defense and Two Principles that Warrant It , 1990 .

[32]  H. Wold.  Models for Knowledge , 1982 .

[33]  Richard G. Netemeyer,et al.  Scaling Procedures: Issues and Applications , 2003 .

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

[35]  Qiuping Xu.  Canonical correlation Analysis , 2014 .

[36]  R. Cudeck,et al.  A realistic perspective on pattern representation in growth data: comment on Bauer and Curran (2003). , 2003, Psychological methods.

[37]  Gaby Odekerken-Schröder,et al.  Using PLS path modeling for assessing hierarchial construct models: guidelines and impirical illustration , 2009 .

[38]  Jörg Henseler,et al.  Consistent and asymptotically normal PLS estimators for linear structural equations , 2014 .

[39]  T. Davenport.  Competing on analytics. , 2006, Harvard business review.

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

[41]  Rashid Mehmood,et al.  Enterprise systems and performance of future city logistics , 2016 .

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

[43]  Marko Sarstedt,et al.  PLS-SEM: Looking Back and Moving Forward , 2014 .

[44]  Kenneth S. Law,et al.  Multidimensional Constructs M Structural Equation Analysis: An Illustration Using the Job Perception and Job Satisfaction Constructs , 1999 .

[45]  Thomas F. Stafford,et al.  Special Research Commentary Series on Advanced Methodological Thinking for Quantitative Research , 2011 .

[46]  Barbara Wixom,et al.  Antecedents of Information and System Quality: An Empirical Examination Within the Context of Data Warehousing , 2005, J. Manag. Inf. Syst..

[47]  V. Daniel R. Guide,et al.  Notes from the Editors: Redefining some methodological criteria for the journal ☆ , 2015 .

[48]  Jörg Henseler,et al.  Handbook of Partial Least Squares: Concepts, Methods and Applications , 2010 .

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

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

[51]  H. Wold.  Soft Modelling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS) Approach , 1975, Journal of Applied Probability.

[52]  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 an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[53]  D. Iacobucci.  Everything You Always Wanted to Know About SEM (Structural Equations Modeling) But Were Afraid to Ask , 2009 .

[54]  W ChinWynne,et al.  Adoption intention in GSS , 1995 .

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

[56]  R. MacCallum,et al.  Applications of structural equation modeling in psychological research. , 2000, Annual review of psychology.

[57]  Wynne W. Chin,et al.  Structural Equation Modeling in Marketing: Some Practical Reminders , 2008 .

[58]  Barbara H Wixom,et al.  A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..

[59]  H. Marsh,et al.  In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings , 2004 .

[60]  Shahriar Akter,et al.  Modelling quality dynamics, business value and firm performance in a big data analytics environment , 2017, Int. J. Prod. Res..

[61]  Wynne W. Chin,et al.  Handbook of Partial Least Squares , 2010 .

[62]  W. Rozeboom,et al.  Meehl on metatheory. , 2005, Journal of clinical psychology.

[63]  Alan G. Sawyer,et al.  The Significance of Statistical Significance Tests in Marketing Research , 1983 .

[64]  J. Edwards,et al.  Partial least squares path modeling: Time for some serious second thoughts , 2016 .

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

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

[67]  Ben Clegg,et al.  Quality management and performance: a comparison between the UK and Turkey , 2013 .

[68]  Wynne W. Chin,et al.  A critical look at partial least squares modeling , 2009 .

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

[70]  Joel Huber,et al.  The Impact of Inferential Beliefs on Product Evaluations , 1982 .

[71]  Sam Ransbotham,et al.  Beyond the hype: The hard work behind analytics success , 2016 .

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

[73]  Jörg Henseler,et al.  Testing moderating effects in PLS path models with composite variables , 2016, Ind. Manag. Data Syst..

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

[75]  Richard A. Spreng,et al.  A Reexamination of the Determinants of Consumer Satisfaction , 1996 .

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

[77]  Wynne W. Chin,et al.  Adoption intention in GSS: relative importance of beliefs , 1995, DATB.

[78]  Alex Pentland,et al.  Big Data and Management , 2014 .

[79]  K DijkstraTheo,et al.  Consistent partial least squares path modeling , 2015 .

[80]  Gautam Ray,et al.  Information Technology and the Performance of the Customer Service Process: A Resource-Based Analysis , 2005, MIS Q..

[81]  Shirley Gregor,et al.  The transformational dimension in the realization of business value from information technology , 2006, J. Strateg. Inf. Syst..

[82]  E. Bulut,et al.  The Use of Partial Least Squares Path Modeling in Investigating the Relationship between Leadership, Motivation and Rewarding , 2015 .

[83]  William R. Darden,et al.  Causal Models in Marketing , 1980 .

[84]  Sam Ransbotham.  Enough health care data for an army: The million veteran program , 2016 .

[85]  Alain Yee-Loong Chong,et al.  A structural analysis of greening the supplier, environmental performance and competitive advantage , 2015 .

[86]  Shahriar Akter,et al.  Service quality of mHealth platforms: development and validation of a hierarchical model using PLS , 2010, Electron. Mark..

[87]  John Stuart Mill,et al.  Auguste Comte and Positivism , 1865 .

[88]  Herbert Kotzab,et al.  Supply chain management resources, capabilities and execution , 2015 .

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

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

[91]  Selim Zaim,et al.  Handbook of Partial Least Squares Concepts Methods and Applications , 2010 .

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

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

[94]  W. Reinartz,et al.  An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM , 2009 .

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

[96]  Barbara Wixom,et al.  Maximizing Value from Business Analytics , 2013, MIS Q. Executive.

[97]  N. Sanders.  How to Use Big Data to Drive Your Supply Chain , 2016 .

[98]  Joseph F. Hair,et al.  Partial Least Squares : The Better Approach to Structural Equation Modeling ? , 2012 .

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

[100]  Martin Wetzels,et al.  Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models , 2012 .

[101]  C. Moorman,et al.  What is Quality? An Integrative Framework of Processes and States , 2012 .

[102]  Shahriar Akter,et al.  How to improve firm performance using big data analytics capability and business strategy alignment , 2016 .

[103]  H. Blalock.  Causal Inferences in Nonexperimental Research , 1966 .

[104]  V. Zeithaml,et al.  E-S-QUAL A Multiple-Item Scale for Assessing Electronic Service Quality , 2004 .

[105]  V. E. Vinzi,et al.  REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling , 2008 .

[106]  Kerstin Liehr-Gobbers,et al.  Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach , 2010 .

[107]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

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

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

[110]  H. Blalock.  The Presidential Address: Measurement and Conceptualization Problems: The Major Obstacle to Integrating Theory and Research , 1979 .