Key issues on Partial Least Squares (PLS) in operations management research: A guide to submissions

Purpose: This work aims to systematise the use of PLS as an analysis tool via a usage guide or recommendation for researchers to help them eliminate errors when using this tool. Design/methodology/approach:  A recent literature review about PLS and discussion with experts in the methodology. Findings: This article considers the current situation of PLS after intense academic debate in recent years, and summarises recommendations to properly conduct and report a research work that uses this methodology in its analyses. We particularly focus on how to: choose the construct type; choose the estimation technique (PLS or CB-SEM); evaluate and report the measurement model; evaluate and report the structure model; analyse statistical power. Research limitations: it was impossible to cover some relevant aspects in considerable detail herein: presenting a guided example that respects all the report recommendations presented herein to act as a practical guide for authors; does the specification or evaluation of the measurement model differ when it deals with first-order or second-order constructs?; how are the outcomes of the constructs interpreted with the indicators being measured with nominal measurement levels?; is the Confirmatory Composite Analysis approach compatible with recent proposals about the Confirmatory Tetrad Analysis (CTA)? These themes will the object of later publications. Originality/value: We provide a check list of the information elements that must contain any article using PLS. Our intention is for the article to act as a guide for the researchers and possible authors who send works to the JIEM (Journal of Industrial and Engineering Management). This guide could be used by both editors and reviewers of JIEM, or other journals in this area, to evaluate and reduce the risk of bias (Losilla et al., 2018) in works using PLS as an analysis procedure.

[1]  Juan A. Marin-Garcia,et al.  Protocol: How to deal with Partial Least Squares (PLS) research in Operations Management. A guide for sending papers to academic journals , 2019, WPOM-Working Papers on Operations Management.

[2]  Shuk Ying Ho,et al.  Partial Least Squares Structural Equation Modeling Approach for Analyzing a Model with a Binary Indicator as an Endogenous Variable , 2016, Commun. Assoc. Inf. Syst..

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

[4]  Edward E. Rigdon,et al.  On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations , 2017 .

[5]  Marko Sarstedt,et al.  Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling , 2018, Journal of International Marketing.

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

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

[8]  Ahmet Usakli,et al.  Using partial least squares structural equation modeling in hospitality and tourism , 2018, International Journal of Contemporary Hospitality Management.

[9]  N. Avkiran An in-depth discussion and illustration of partial least squares structural equation modeling in health care , 2018, Health care management science.

[10]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement , 2009, BMJ.

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

[12]  Jamie L. Callahan The retrospective (im)moralization of self-plagiarism: Power interests in the social construction of new norms for publishing , 2018 .

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

[14]  Juan-Gabriel Cegarra-Navarro,et al.  Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management , 2019, J. Knowl. Manag..

[15]  Stacie Petter,et al.  "Haters Gonna Hate": PLS and Information Systems Research , 2018, Data Base.

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

[17]  Stephane Champely,et al.  Basic Functions for Power Analysis , 2015 .

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

[19]  John Reyes,et al.  Total productive maintenance for the sewing process in footwear , 2018 .

[20]  Marko Sarstedt,et al.  Gain more insight from your PLS-SEM results: The importance-performance map analysis , 2016, Ind. Manag. Data Syst..

[21]  Juan A. Marin-Garcia,et al.  Clustering the mediators between the sales control systems and the sales performance using the AMO model: A narrative systematic literature review , 2018 .

[22]  Kenneth A. Bollen,et al.  Representing general theoretical concepts in structural equation models: the role of composite variables , 2008, Environmental and Ecological Statistics.

[23]  Edward E. Rigdon,et al.  Choosing PLS path modeling as analytical method in European management research: A realist perspective , 2016 .

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

[25]  Jamal Ahmed Hama Kareem,et al.  Ethical and psychological factors in 5S and total productive maintenance , 2015 .

[26]  Keyoor Purani,et al.  Model specification issues in PLS-SEM , 2018, Journal of Hospitality and Tourism Technology.

[27]  P. Shekelle,et al.  Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement , 2015, Systematic Reviews.

[28]  Pratyush Nidhi Sharma,et al.  PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research , 2019, J. Assoc. Inf. Syst..

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

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

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

[32]  David Moher,et al.  PRISMA-Equity 2012 Extension: Reporting Guidelines for Systematic Reviews with a Focus on Health Equity , 2012, PLoS medicine.

[33]  Christian Nitzl,et al.  The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development , 2016 .

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

[35]  H. P. Whitt The sheaf coefficient: A simplified and expanded approach , 1986 .

[36]  Juan A. Marin-Garcia,et al.  Three risk of bias tools lead to opposite conclusions in observational research synthesis. , 2018, Journal of clinical epidemiology.

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

[38]  E. Erdfelder,et al.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses , 2009, Behavior research methods.

[39]  Clay M. Voorhees,et al.  Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies , 2016 .

[40]  Sandra Streukens,et al.  Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results , 2016 .

[41]  Jason Bennett Thatcher,et al.  Conceptualizing and testing formative constructs: tutorial and annotated example , 2009, DATB.

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

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

[44]  Marko Sarstedt,et al.  Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach , 2017 .

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

[46]  Hengky Latan,et al.  Chapter 4 PLS Path Modeling in Hospitality and Tourism Research: The Golden Age and Days of Future Past , 2018, Applying Partial Least Squares in Tourism and Hospitality Research.

[47]  Shawn Bauldry,et al.  Three Cs in measurement models: causal indicators, composite indicators, and covariates. , 2011, Psychological methods.

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

[49]  Marko Sarstedt,et al.  Management of multi-purpose stadiums: importance and performance measurement of service interfaces , 2010, Int. J. Serv. Technol. Manag..

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

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

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

[53]  José L. Roldán,et al.  European management research using partial least squares structural equation modeling (PLS-SEM) , 2015 .

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

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

[56]  Paulo Duarte,et al.  Methods for modelling reflective-formative second order constructs in PLS , 2018, Journal of Hospitality and Tourism Technology.

[57]  Glenn W. Lambie,et al.  An Analysis of the World Health Organization Disability Assessment Schedule 2.0 Measurement Model Using Partial Least Squares–Structural Equation Modeling , 2020, Assessment.

[58]  S. Gudergan,et al.  Partial least squares structural equation modeling in HRM research , 2020 .

[59]  Marko Sarstedt,et al.  Advanced Issues in Partial Least Squares Structural Equation Modeling , 2017 .

[60]  G. V. Seco,et al.  Los efectos de terceras variables en la investigación psicológica , 2011 .

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

[62]  Nils Urbach,et al.  Structural Equation Modeling in Information Systems Research Using Partial Least Squares , 2010 .

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

[64]  Florian Schuberth,et al.  PLS path modeling – a confirmatory approach to study tourism technology and tourist behavior , 2018, Journal of Hospitality and Tourism Technology.

[65]  Manuel J. Sánchez-Franco,et al.  Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research , 2012 .

[66]  Guy Assaker,et al.  Using Partial Least Squares Structural Equation Modeling in Tourism Research , 2016 .

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

[68]  P. Tugwell,et al.  Extending the PRISMA statement to equity-focused systematic reviews (PRISMA-E 2012): explanation and elaboration , 2016, International Journal for Equity in Health.

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

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

[71]  Juan A. Marin-Garcia,et al.  Development and validation of Spanish version of FINCODA: an instrument for self-assessment of innovation competence of workers or candidates for Jobs , 2018, WPOM-Working Papers on Operations Management.

[72]  Galit Shmueli,et al.  The elephant in the room: Predictive performance of PLS models , 2016 .

[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]  X. Bonfill,et al.  [The PRISMA statement: a step in the improvement of the publications of the Revista Española de Salud Pública]. , 2013, Revista espanola de salud publica.

[76]  Juan A. Marin-Garcia,et al.  Triple-A and competitive advantage in supply chains: Empirical research in developed countries , 2018, International Journal of Production Economics.

[77]  Juan A. Marin-Garcia,et al.  Barreras y facilitadores de la implantación del TPM , 2013 .

[78]  Juan A. Marin-Garcia,et al.  Deconstructing AMO framework: a systematic review , 2016 .

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

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

[81]  Glenn W. Lambie,et al.  The Factor Structure of Outcome Questionnaire–45.2 Scores Using Confirmatory Tetrad Analysis–Partial Least Squares , 2020, Journal of Psychoeducational Assessment.