Genetic algorithm segmentation in partial least squares structural equation modeling. (English) Zbl 1290.90090
Summary: 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.
MSC:
| 90C59 | Approximation methods and heuristics in mathematical programming |
Keywords:
genetic algorithm; partial least squares; path modeling; PLS-SEM; segmentation; structural equation modelingReferences:
| [1] | 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 · Zbl 1186.62001 |
| [2] | 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 |
| [3] | Anderberg MR (1973) Cluster analysis for applications. Academic Press, New York · Zbl 0299.62029 |
| [4] | Anderson EW, Fornell CG (2000) Foundations of the American customer satisfaction index. Total Quality Manag 11:869-882 · doi:10.1080/09544120050135425 |
| [5] | Aptech (2008) GAUSS for Windows Version 9.0. Aptech Systems, Black Diamond |
| [6] | Becker J-M, Rai A, Ringle CM et al (2013) Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Q (forthcoming) · Zbl 1429.62227 |
| [7] | 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 |
| [8] | Boomsma, A.; Hoogland, JJ; Cudeck, R. (ed.); Toit, S. (ed.); Sörbom, D. (ed.), The robustness of LISREL modeling revisited, 139-168 (2001), Chicago |
| [9] | Cenfetelli RT, Bassellier G (2009) Interpretation of formative measurement in information systems research. MIS Q 33:689-708 |
| [10] | 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 · Zbl 1186.62001 |
| [11] | Chin, WW; Marcoulides, GA (ed.), The partial least squares approach to structural equation modeling, 295-358 (1998), Mahwah |
| [12] | 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 |
| [13] | 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 · doi:10.1287/isre.14.2.189.16018 |
| [14] | 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 · doi:10.1016/j.jbusres.2008.01.013 |
| [15] | Cowgill MC, Harvey RJ, Watson LT (1999) A genetic algorithm approach to cluster analysis. Comput Math Appl 39:99-108 · Zbl 1043.62516 · doi:10.1016/S0898-1221(99)00090-5 |
| [16] | Diamantopoulos A (2011) Incorporating formative measures into covariance-based structural equation models. MIS Q 35:335-A335 |
| [17] | Diamantopoulos A, Riefler P, Roth KP (2008) Advancing formative measurement models. J Bus Res 61:1203-1218 · doi:10.1016/j.jbusres.2008.01.009 |
| [18] | Dijkstra TK (1983) Some comments on maximum likelihood and partial least squares methods. J Econom 22:67-90 · Zbl 0521.62098 · doi:10.1016/0304-4076(83)90094-5 |
| [19] | 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 |
| [20] | 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 · Zbl 1199.90018 · doi:10.1002/asmb.728 |
| [21] | Falk RF, Miller NB (1992) A primer for soft modeling. University of Akron Press, Akron |
| [22] | Fogel DB (2006) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, Piscataway |
| [23] | Fornell CG, Bookstein FL (1982) Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J Mark Res 19:440-452 · doi:10.2307/3151718 |
| [24] | Fornell CG, Johnson MD, Anderson EW et al (1996) The American customer satisfaction index: nature, purpose, and findings. J Mark 60:7-18 · doi:10.2307/1251898 |
| [25] | 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 |
| [26] | Grün B, Leisch F (2008) Flexmix 2: finite mixtures with concomitant variables and varying constant parameters. J Stat Softw 28:1-35 |
| [27] | Gudergan SP, Ringle CM, Wende S et al (2008) Confirmatory tetrad analysis in PLS path modeling. J Bus Res 61:1238-1249 · doi:10.1016/j.jbusres.2008.01.012 |
| [28] | Haenlein M, Kaplan AM (2004) A beginner’s guide to partial least squares analysis. Understand Stat 3:283-297 · doi:10.1207/s15328031us0304_4 |
| [29] | Hahn C, Johnson MD, Herrmann A et al (2002) Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Bus Rev 54:243-269 |
| [30] | Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19:139-151 · doi:10.2753/MTP1069-6679190202 |
| [31] | Hair JF, Ringle CM, Sarstedt M (2012a) Partial least squares: the better approach to structural equation modeling? Long Range Plan 45:312-319 |
| [32] | 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 |
| [33] | 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 |
| [34] | Hair JF, Hult GTM, Ringle CM et al (2013a) A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks · Zbl 1416.62010 |
| [35] | 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 |
| [36] | 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 · doi:10.1080/10705510903439003 |
| [37] | Henseler J, Sarstedt (2013) Goodness of fit indices for partial least squares path modeling. Comput Stat (forth coming) · Zbl 1305.65042 |
| [38] | Henseler, J.; Ringle, CM; Sarstedt, M.; Okazaki, S. (ed.), Using partial least squares path modeling in international advertising research: basic concepts and recent issues, 252-276 (2012), Cheltenham |
| [39] | Henseler J, Ringle CM, Sinkovics RR (2009) The use of partial least squares path modeling in international marketing. Adv Int Mark 20:277-320 |
| [40] | Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor · Zbl 0317.68006 |
| [41] | Hui, BS; Wold, H.; Jöreskog, KG (ed.); Wold, H. (ed.), Consistency and consistency at large of partial least squares estimates, 119-130 (1982), Amsterdam · Zbl 0503.62064 |
| [42] | Jedidi K, Jagpal HS, Desarbo WS (1997) Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Mark Sci 16:39-59 · doi:10.1287/mksc.16.1.39 |
| [43] | Jöreskog, KG; Wold, H. (ed.); Wold, KG (ed.), The LISREL approach to causal model-building in the social sciences, 81-100 (1982), Amsterdam · Zbl 0503.62098 |
| [44] | Jöreskog KG (1978) Structural analysis of covariance and correlation matrices. Psychometrika 43:443-477 · Zbl 0392.62043 · doi:10.1007/BF02293808 |
| [45] | Jöreskog, KG; Wold, H.; Wold, H. (ed.); Jöreskog, KG (ed.), The ML and PLS techniques for modeling with latent variables: historical and comparative aspects, 263-270 (1982), Amsterdam · Zbl 0503.62067 |
| [46] | 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 · doi:10.1016/j.accinf.2011.05.002 |
| [47] | Leisch F, Monecke A (2012) semPLS: structural equation modeling using partial least squares. J Stat Softw 48 |
| [48] | Lohmöller J-B (1989) Latent variable path modeling with partial least squares. Physica, Heidelberg · Zbl 0788.62050 · doi:10.1007/978-3-642-52512-4 |
| [49] | Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33:1455-1465 · doi:10.1016/S0031-3203(99)00137-5 |
| [50] | Micceri T (1989) The unicorn, the normal curve, and other improbable creatures. Psychol Bull 105:156-166 · doi:10.1037/0033-2909.105.1.156 |
| [51] | Michalewicz Z et al (1996) Genetic algorithms + data structures = evolutionary programs. Springer, Berlin · Zbl 0841.68047 · doi:10.1007/978-3-662-03315-9 |
| [52] | 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 · doi:10.1016/j.lrp.2012.09.003 |
| [53] | Mooi EA, Sarstedt M (2011) A concise guide to market research: the process, data, and methods using IBM SPSS statistics. Springer, Berlin |
| [54] | Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recognit 17:825-832 |
| [55] | 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 · doi:10.2753/MTP1069-6679190205 |
| [56] | 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 · doi:10.1016/j.jom.2012.06.002 |
| [57] | R Core Team (2012) R: a language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna |
| [58] | 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 · doi:10.1016/j.ijresmar.2009.08.001 |
| [59] | Rigdon, EE; Ringle, CM; Sarstedt, M.; Malhotra, NK (ed.), Structural modeling of heterogeneous data with partial least squares, 255-296 (2010), Armonk |
| [60] | 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 · doi:10.1108/S1474-7979(2011)0000022011 |
| [61] | 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 |
| [62] | Ringle, CM; Sarstedt, M.; Schlittgen, R.; Fink, A. (ed.); Lausen, B. (ed.); Seidel, W. (ed.); Ultsch, A. (ed.), Finite mixture and genetic algorithm segmentation in partial least squares path modeling: identification of multiple segments in a complex path model, 167-176 (2010), Berlin-Heidelberg |
| [63] | Ringle CM, Sarstedt M, Schlittgen R et al (2013) PLS path modeling and evolutionary segmentation. J Bus Res (forthcoming) · Zbl 1290.90090 |
| [64] | Ringle CM, Sarstedt M, Straub DW (2012) A critical look at the use of PLS-SEM in MIS quarterly. MIS Q 36:iii-xiv |
| [65] | Ringle, CM; Schlittgen, R.; Martens, H. (ed.); Næs, T. (ed.); Martens, M. (ed.), A genetic segmentation approach for uncovering and separating groups of data in PLS path modeling, 75-78 (2007), Åas |
| [66] | 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 |
| [67] | Ringle CM, Wende S, Will A (2005) SmartPLS 2.0. http://www.smartpls.de, Hamburg |
| [68] | Robins JA (2012) Partial-least squares. Long Range Plan 45:309-311 · doi:10.1016/j.lrp.2012.10.002 |
| [69] | 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 |
| [70] | 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/ |
| [71] | Sarstedt M (2008a) A review of recent approaches for capturing heterogeneity in partial least squares path modelling. J Model Manag 3:140-161 |
| [72] | Sarstedt M (2008b) Market segmentation with mixture regression models: understanding measures that guide model selection. J Target Meas Anal Mark 16:228-246 · Zbl 0521.62098 |
| [73] | 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 |
| [74] | 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 |
| [75] | 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 · Zbl 1511.62402 · doi:10.1080/02664760903030213 |
| [76] | 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 · doi:10.1007/s12087-009-0023-7 |
| [77] | Shah R, Goldstein SM (2006) Use of structural equation modeling in operations management research: looking back and forward. J Oper Manag 24:148-169 · doi:10.1016/j.jom.2005.05.001 |
| [78] | 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 · doi:10.1177/1059601108329198 |
| [79] | 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 · Zbl 1186.62001 |
| [80] | 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 |
| [81] | Tenenhaus M, Esposito Vinzi V et al (2005) PLS path modeling. Comput Stat Data Anal 48:159-205 · Zbl 1429.62227 · doi:10.1016/j.csda.2004.03.005 |
| [82] | 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 · doi:10.1177/1094670510370054 |
| [83] | Wedel M, Kamakura W, Arora N et al (1999) Discrete and continuous representations of unobserved heterogeneity in choice modeling. Mark Lett 10:219-232 · doi:10.1023/A:1008054316179 |
| [84] | Wedel M, Kamakura WA (2000) Market segmentation: conceptual and methodological foundations. Kluwer, Boston · doi:10.1007/978-1-4615-4651-1 |
| [85] | Wold, H.; Jöreskog, KG (ed.); Wold, H. (ed.), Soft modeling: the basic design and some extensions, 1-54 (1982), Amsterdam · Zbl 0517.62065 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
