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Genetic algorithm segmentation in partial least squares structural equation modeling

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Abstract

When applying the partial least squares structural equation modeling (PLS-SEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an assessment of this novel approach. PLS-GAS allows for uncovering unobserved heterogeneity and identifying different groups within a data set. In an application on customer satisfaction data and the American customer satisfaction index model, the method identifies distinctive group-specific PLS path model estimates which allow for a further differentiated interpretation of the results.

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Notes

  1. Wold (1982) differentiates between Mode A and Mode B measurement models. In Mode A, the weight relations are from the latent variable to the associated manifest variables; in Mode B, the weight relations are from the manifest variables to the associated latent variable. In line with Wold (1982), we refer to Mode C path model constellations if each of Modes A and B was chosen at least once in the model.

  2. Note that Becker et al. (2013) recently presented another segmentation approach for PLS-SEM: PLS prediction-oriented segmentation (PLS-POS). The comparison of PLS-GAS and PLS-POS remains an issue for future research.

  3. To determine the number of individuals and generations, we examined these parameters’ effects on the fitness. The results revealed only marginal effects of the population size for higher numbers of generations.

  4. In this study, we generate highly non-normal data with a skewness of 6.183 and an excess (resulting from the difference between two log-normal distributions) of 33.067. See the Online Appendix II (http://www.pls-sem.com/orsp/oa.pdf) for further information on the data generation procedure.

  5. Although such changes in Mode B measurement models do not change the error variances, the pre-specified weights of these measurement models are changed similarly—as in Mode A measurement models—in the computational experiments. When the weights are low (high), they have a pre-specified value of 0.20 (0.40). The mixed factor level assigns the high pre-specified weight of 0.40 to the first manifest variable and the low weight of 0.20 to the last manifest variable per Mode B measurement model; in the mixed factor level constellation, weights linearly increase from 0.20 to 0.40.

  6. See Equation 16 in Online Appendix II (http://www.pls-sem.com/orsp/oa.pdf).

  7. For two segments, relative segment sizes are 50 %/50 % (balanced) and 75 %/25 % (unbalanced); for three segments, relative segment sizes are 33 %/33 %/33 % (balanced) and 60 %/20 %/20 % (unbalanced).

  8. The data sets that have been used as input for the analyses are available at http://www.pls-sem.com/orsp/data.zip.

  9. The GAUSS program to run PLS-GAS is available at http://www.pls-sem.com/orsp/program.zip.

  10. Note that PLS-GAS always meets the global optimum solution when no error is present.

  11. See Equation 16 in Online Appendix II (http://www.pls-sem.com/orsp/oa.pdf).

  12. See the Online Appendix IV (http://www.pls-sem.com/orsp/oa.pdf) for the results of these additional analyses.

  13. The data were provided by Fornell, Claes. American Customer Satisfaction Index, 1999 [Computer file]. ICPSR04436-v1. Ann Arbor, MI: University of Michigan. Ross School of Business, National Quality Research Center/Reston, VA: Wirthlin Worldwide [producers], 1999. Ann Arbor, MI: Inter-University Consortium for Political and Social Research [distributor], 2006-06-09. We would like to thank Claes Fornell and the ICPSR for making the data available.

  14. In addition to these recommendations, one finds various recommendations on the use of the PLS-SEM method and the evaluation of results in prior literature; examples include: Chin (1998); Chin (2010), Falk and Miller (1992), Götz et al. (2010), Haenlein and Kaplan (2004), Henseler et al. (2009); Henseler et al. (2012), Lohmöller (1989), Roldán and Sánchez-Franco (2012), Sosik et al. (2009), Tenenhaus et al. (2005).

References

  • Albers S (2010) PLS and success factor studies in marketing. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H (eds) Handbook of partial least squares: concepts, methods and applications. Springer Handbooks of Computational Statistics Series, vol II. Springer, Heidelberg/ Dordrecht/London/New York, pp 409–425

  • Albers S, Hildebrandt L (2006) Methodische Probleme bei der Erfolgsfaktorenforschung—Messfehler, formative versus reflektive Indikatoren und die Wahl des Strukturgleichungs-Modells. Z für betriebswirtschaftliche Forsch 58:2–33

    Google Scholar 

  • Anderberg MR (1973) Cluster analysis for applications. Academic Press, New York

    Google Scholar 

  • Anderson EW, Fornell CG (2000) Foundations of the American customer satisfaction index. Total Quality Manag 11:869–882

    Article  Google Scholar 

  • Aptech (2008) GAUSS for Windows Version 9.0. Aptech Systems, Black Diamond

  • Becker J-M, Rai A, Ringle CM et al (2013) Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Q (forthcoming)

  • Becker J-M, Ringle CM, Völckner F (2009) Prediction-oriented segmentation: a Aew methodology to uncover unobserved heterogeneity in PLS path models. In: Proceedings of the 38th Conference of the European Marketing Academy (EMAC), Nantes

  • Boomsma A, Hoogland JJ (2001) The robustness of LISREL modeling revisited. In: Cudeck R, du Toit S, Sörbom D (eds) Structural equation modeling: present and future. Scientific Software International, Chicago, pp 139–168

    Google Scholar 

  • Cenfetelli RT, Bassellier G (2009) Interpretation of formative measurement in information systems research. MIS Q 33:689–708

    Google Scholar 

  • Chin WW (2010) How to write up and report PLS analyses. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H (eds) Handbook of partial least squares: concepts, methods and applications. Springer Handbooks of Computational Statistics Series, vol II. Springer, Heidelberg/Dordrecht/ London/New York, pp 655–690

  • Chin WW (1998) The partial least squares approach to structural equation modeling. In: Marcoulides GA (ed) Mod Methods Bus Res. Erlbaum, Mahwah, pp 295–358

    Google Scholar 

  • Chin WW (2003) A permutation procedure for multi-group comparison of PLS models. In: Vilares M, Tenenhaus M, Coelho PS, Esposito Vinzi V, Morineau A (eds) Focus on customers: Proceedings of the 3rd International Symposium on PLS and Related Methods (PLS’03). Decisia, Paris, pp 33–43

  • Chin WW, Marcolin BL, Newsted PN (2003) A partial least squares approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic mail emotion/adoption study. Inf Syst Res 14:189–217

    Article  Google Scholar 

  • Coltman T, Devinney TM, Midgley DF et al (2008) Formative versus reflective measurement models: two applications of formative measurement. J Bus Res 61:1250–1262

    Article  Google Scholar 

  • Cowgill MC, Harvey RJ, Watson LT (1999) A genetic algorithm approach to cluster analysis. Comput Math Appl 39:99–108

    Article  Google Scholar 

  • Diamantopoulos A (2011) Incorporating formative measures into covariance-based structural equation models. MIS Q 35:335–A335

    Google Scholar 

  • Diamantopoulos A, Riefler P, Roth KP (2008) Advancing formative measurement models. J Bus Res 61:1203–1218

    Article  Google Scholar 

  • Dijkstra TK (1983) Some comments on maximum likelihood and partial least squares methods. J Econom 22:67–90

    Article  Google Scholar 

  • Esposito Vinzi V, Ringle CM, Squillacciotti S et al (2007) Capturing and treating unobserved heterogeneity by response based segmentation in PLS path modeling: a comparison of alternative methods by computational experiments. In:ESSEC Research Center, Working Paper No. 07019, Cergy Pontoise Cedex

  • Esposito Vinzi V, Trinchera L, Squillacciotti S et al (2008) REBUS-PLS: a response-based procedure for detecting unit segments in PLS path modelling. Appl Stoch Models Bus Ind 24:439–458

    Article  Google Scholar 

  • Falk RF, Miller NB (1992) A primer for soft modeling. University of Akron Press, Akron

    Google Scholar 

  • Fogel DB (2006) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, Piscataway

    Google Scholar 

  • Fornell CG, Bookstein FL (1982) Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J Mark Res 19:440–452

    Article  Google Scholar 

  • Fornell CG, Johnson MD, Anderson EW et al (1996) The American customer satisfaction index: nature, purpose, and findings. J Mark 60:7–18

    Article  Google Scholar 

  • Götz O, Liehr-Gobbers K, Krafft M (2010) Evaluation of structural equation models using the partial least squares (PLS) approach. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H (eds) Handbook of partial least squares: concepts, methods and applications. Springer Handbooks of Computational Statistics Series, vol II. Springer, Heidelberg/Dordrecht/London/New York, pp 691–711

  • Grün B, Leisch F (2008) Flexmix 2: finite mixtures with concomitant variables and varying constant parameters. J Stat Softw 28:1–35

    Google Scholar 

  • Gudergan SP, Ringle CM, Wende S et al (2008) Confirmatory tetrad analysis in PLS path modeling. J Bus Res 61:1238–1249

    Article  Google Scholar 

  • Haenlein M, Kaplan AM (2004) A beginner’s guide to partial least squares analysis. Understand Stat 3:283–297

    Article  Google Scholar 

  • Hahn C, Johnson MD, Herrmann A et al (2002) Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Bus Rev 54:243–269

    Google Scholar 

  • Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19:139–151

    Article  Google Scholar 

  • Hair JF, Ringle CM, Sarstedt M (2012a) Partial least squares: the better approach to structural equation modeling? Long Range Plan 45:312–319

    Google Scholar 

  • Hair JF, Sarstedt M, Pieper TM et al (2012b) Applications of partial least squares path modeling in management journals: a review of past practices and recommendations for future applications. Long Range Plan 45:320–340

    Google Scholar 

  • Hair JF, Sarstedt M, Ringle CM et al (2012c) An assessment of the use of partial least squares structural equation modeling in marketing research. J Acad Mark Sci 40:414–433

    Google Scholar 

  • Hair JF, Hult GTM, Ringle CM et al (2013a) A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks

  • Hair JF, Ringle CM, Sarstedt M (2013b) Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Planning 46:1–12

    Google Scholar 

  • Henseler J, Chin WW (2010) A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Struct Equ Model 17:82–109

    Article  Google Scholar 

  • Henseler J, Sarstedt (2013) Goodness of fit indices for partial least squares path modeling. Comput Stat (forth coming)

  • Henseler J, Ringle CM, Sarstedt M (2012) Using partial least squares path modeling in international advertising research: basic concepts and recent issues. In: Okazaki S (ed) Handbook research in international advertising. Edward Elgar Publishing, Cheltenham, pp 252–276

    Google Scholar 

  • Henseler J, Ringle CM, Sinkovics RR (2009) The use of partial least squares path modeling in international marketing. Adv Int Mark 20:277–320

    Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hui BS, Wold H (1982) Consistency and consistency at large of partial least squares estimates. In: Jöreskog KG, Wold H (eds) Systems under indirect observation, Part II. North Holland, Amsterdam, pp 119–130

    Google Scholar 

  • Jedidi K, Jagpal HS, Desarbo WS (1997) Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Mark Sci 16:39–59

    Article  Google Scholar 

  • Jöreskog KG (1982) The LISREL approach to causal model-building in the social sciences. In: Wold H, Wold KG (eds) Systems under indirect observation, Part I. North-Holland, Amsterdam, pp 81–100

    Google Scholar 

  • Jöreskog KG (1978) Structural analysis of covariance and correlation matrices. Psychometrika 43:443–477

    Article  Google Scholar 

  • Jöreskog KG, Wold H (1982) The ML and PLS techniques for modeling with latent variables: historical and comparative aspects. In: Wold H, Jöreskog KG (eds) Systems under indirect observation, Part I. North-Holland, Amsterdam, pp 263–270

    Google Scholar 

  • Lee L, Petter S, Fayard D et al (2011) On the use of partial least squares path modeling in accounting research. Int J Acc Inf Syst 12:305–328

    Article  Google Scholar 

  • Leisch F, Monecke A (2012) semPLS: structural equation modeling using partial least squares. J Stat Softw 48

  • Lohmöller J-B (1989) Latent variable path modeling with partial least squares. Physica, Heidelberg

    Book  Google Scholar 

  • Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33:1455–1465

    Article  Google Scholar 

  • Micceri T (1989) The unicorn, the normal curve, and other improbable creatures. Psychol Bull 105:156–166

    Article  Google Scholar 

  • Michalewicz Z et al (1996) Genetic algorithms + data structures = evolutionary programs. Springer, Berlin

    Book  Google Scholar 

  • Money KG, Hillenbrand C, Henseler J et al (2012) Exploring unanticipated consequences of strategy amongst stakeholder segments: the case of a European revenue service. Long Range Plan 45:395–423

    Article  Google Scholar 

  • Mooi EA, Sarstedt M (2011) A concise guide to market research: the process, data, and methods using IBM SPSS statistics. Springer, Berlin

  • Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recognit 17:825–832

    Google Scholar 

  • Navarro A, Acedo FJ, Losada F et al (2011) Integrated model of export activity: analysis of heterogeneity in managers’ orientations and perceptions on strategic marketing management in foreign markets. J Mark Theory Pract 19:187–204

    Article  Google Scholar 

  • Peng DX, Lai F (2012) Using partial least squares in operations management research: a practical guideline and summary of past research. J Oper Manag 30:467–480

    Article  Google Scholar 

  • R Core Team (2012) R: a language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna

  • Reinartz WJ, Haenlein M, Henseler J (2009) An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int J Mark Res 26:332–344

    Article  Google Scholar 

  • Rigdon EE, Ringle CM, Sarstedt M (2010) Structural modeling of heterogeneous data with partial least squares. In: Malhotra NK (ed) Rev Mark Res. Sharpe, Armonk, pp 255–296

    Google Scholar 

  • Rigdon EE, Ringle CM, Sarstedt M et al (2011) Assessing heterogeneity in customer satisfaction studies: across industry similarities and within industry differences. Adv Int Mark 22:169–194

    Article  Google Scholar 

  • Ringle CM, Sarstedt M, Mooi EA (2010) Response-based segmentation using finite mixture partial least squares: theoretical foundations and an application to American customer satisfaction index data. Ann Inf Syst 8:19–49

    Google Scholar 

  • Ringle CM, Sarstedt M, Schlittgen R (2010) Finite mixture and genetic algorithm segmentation in partial least squares path modeling: identification of multiple segments in a complex path model. In: Fink A, Lausen B, Seidel W, Ultsch A (eds) Advances in data analysis, data handling and business intelligence. Springer, Berlin-Heidelberg, pp 167–176

    Google Scholar 

  • Ringle CM, Sarstedt M, Schlittgen R et al (2013) PLS path modeling and evolutionary segmentation. J Bus Res (forthcoming)

  • Ringle CM, Sarstedt M, Straub DW (2012) A critical look at the use of PLS-SEM in MIS quarterly. MIS Q 36:iii-xiv

  • Ringle CM, Schlittgen R (2007) A genetic segmentation approach for uncovering and separating groups of data in PLS path modeling. In: Martens H, Næs T, Martens M (eds) Causalities explored by Iindirect observation: proceedings of the 5th international symposium on PLS and related methods (PLS’07). MATFORSK, Åas, pp 75–78

    Google Scholar 

  • Ringle CM, Wende S, Will A (2010) Finite mixture partial least squares analysis: methodology and numerical examples. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H (eds) Handbook of partial least squares: concepts, methods and applications. Springer Handbooks of Computational Statistics Series, vol II. Springer, Heidelberg, pp 195–218

  • Ringle CM, Wende S, Will A (2005) SmartPLS 2.0. http://www.smartpls.de, Hamburg

  • Robins JA (2012) Partial-least squares. Long Range Plan 45:309–311

    Article  Google Scholar 

  • Roldán JL, Sánchez-Franco MJ (2012) Variance-based structural equation modeling: guidelines for using partial least squares in information systems research. In: Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems. IGI Global, pp 193–221

  • Sánchez G, Trinchera L (2013) R Package plspm: tools for partial least squares path modeling (version 0.3.5). http://cran.r-project.org/web/packages/plspm/

  • Sarstedt M (2008a) A review of recent approaches for capturing heterogeneity in partial least squares path modelling. J Model Manag 3:140–161

    Google Scholar 

  • Sarstedt M (2008b) Market segmentation with mixture regression models: understanding measures that guide model selection. J Target Meas Anal Mark 16:228–246

    Google Scholar 

  • Sarstedt M, Becker J-M, Ringle CM et al (2011a) Uncovering and treating unobserved heterogeneity with FIMIX-PLS: which model selection criterion provides an appropriate number of segments? Schmalenbach Bus Rev 63:34–62

    Google Scholar 

  • Sarstedt M, Henseler J, Ringle CM (2011b) Multi-group analysis in partial least squares (PLS) path modeling: alternative methods and empirical results. Adv Int Mark 22:195–218

    Google Scholar 

  • Sarstedt M, Ringle CM (2010) Treating unobserved heterogeneity in PLS path modelling: a comparison of FIMIX-PLS with different data analysis strategies. J Appl Stat 37:1299–1318

    Article  Google Scholar 

  • Sarstedt M, Schwaiger M, Ringle CM (2009) 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. J Bus Mark Manag 3:185–206

    Article  Google Scholar 

  • Shah R, Goldstein SM (2006) Use of structural equation modeling in operations management research: looking back and forward. J Oper Manag 24:148–169

    Article  Google Scholar 

  • Sosik JJ, Kahai SS, Piovoso MJ (2009) Silver Bullet or Voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group Organ Manag 34:5–36

    Article  Google Scholar 

  • Squillacciotti S (2010) Prediction-oriented classification in PLS path modeling. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H (eds) Handbook of partial least squares: concepts, methods and applications. Springer Handbooks of Computational Statistics Series, vol II. Springer, Heidelberg, pp 219–233

  • Squillacciotti S (2005) Prediction oriented classification in PLS path modeling. In: Aluja T, Casanovas J, Esposito Vinzi V, Tenenhaus M (eds) PLS & marketing: Proceedings of the 4th International Symposium on PLS and Related Methods (PLS’05). DECISIA, Paris, pp 499–506

  • Tenenhaus M, Esposito Vinzi V et al (2005) PLS path modeling. Comput Stat Data Anal 48:159–205

    Article  Google Scholar 

  • Völckner F, Sattler H, Hennig-Thurau T et al (2010) The role of parent brand quality for service brand extension success. J Serv Res 13:359–361

    Article  Google Scholar 

  • Wedel M, Kamakura W, Arora N et al (1999) Discrete and continuous representations of unobserved heterogeneity in choice modeling. Mark Lett 10:219–232

    Article  Google Scholar 

  • Wedel M, Kamakura WA (2000) Market segmentation: conceptual and methodological foundations. Kluwer, Boston

    Book  Google Scholar 

  • Wold H (1982) Soft modeling: the basic design and some extensions. In: Jöreskog KG, Wold H (eds) Systems under indirect observations: Part II. North-Holland, Amsterdam, pp 1–54

    Google Scholar 

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Acknowledgments

The authors thank the area editor and four anonymous reviewers for their helpful comments as well as the time and care devoted to our research.

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Authors and Affiliations

  1. Institute for Human Resource Management and Organizations (HRMO), Hamburg University of Technology (TUHH), Hamburg, Germany

    Christian M. Ringle

  2. Faculty of Law and Business, University of Newcastle, Newcastle, Australia

    Christian M. Ringle & Marko Sarstedt

  3. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Marko Sarstedt

  4. Institute for Statistics and Econometrics, University of Hamburg, Hamburg, Germany

    Rainer Schlittgen

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  1. Christian M. Ringle
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  2. Marko Sarstedt
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Correspondence to Marko Sarstedt.

Additional information

The authors presented the PLS-GAS method at the following conferences: 5th International Symposium of PLS and Related Methods, Ås, Norway, 2007; the 32th Annual Conference of the German Classification Society—Gesellschaft für Klassifikation (GfKl), Hamburg, Germany, 2008; the Australian & New Zealand Marketing Academy (ANZMAC) Annual Conference, Melbourne, Australia, 2009; and the Korean Academy of Marketing Science (KAMS) Global Marketing Conference, Tokyo, Japan, 2010.

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Ringle, C.M., Sarstedt, M. & Schlittgen, R. Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum 36, 251–276 (2014). https://doi.org/10.1007/s00291-013-0320-0

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