An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models
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Anderson EW, Fornell C (2002) Foundations of the American customer satisfaction index. Total Qual Manag 11(7):869–882. https://doi.org/10.1080/09544120050135425
Becker J-M, Rai A, Rigdon E (2013) Predictive validity and formative measurement in structural equation modeling: embracing practical relevance. In: Proceedings of the 34th international conference on information systems (ICIS), Milan, Italy
Bollen KA (1989) Structural equations with latent variables. Wiley, Hoboken. https://doi.org/10.1002/9781118619179
Bollen KA (1996) An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika 61(1):109–121. https://doi.org/10.1007/BF02296961
Bollen KA (2011) Evaluating effect, composite, and causal indicators in structural equation models. MIS Q 35(2):359. https://doi.org/10.2307/23044047
Bollen KA, Kirby JB, Curran PJ, Paxton PM, Chen F (2007) Latent variable models under misspecification two-stage least squares (2SLS) and maximum likelihood (ML) estimators. Soc Methods Res 36(1):48–86. https://doi.org/10.1177/0049124107301947
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Chin WW (1998) The partial least squares approach for structural equation modeling. In: Marcoulides GA (ed) Methodology for business and management. Modern methods for business research. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US, pp 295–336
Cho G, Jung K, Hwang H (2019) Out-of-bag prediction error: a cross validation index for generalized structured component analysis. Multivar Behav Res. https://doi.org/10.1080/00273171.2018.1540340
Dijkstra TK (2017) A perfect match between a model and a mode. In: Partial least squares path modeling: basic concepts, methodological issues and applications. Springer, Berlin, pp 55–80. https://doi.org/10.1007/978-3-319-64069-3_4
Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26. https://doi.org/10.1214/aos/1176344552
Eklöf JA, Westlund AH (2002) The pan-European customer satisfaction index programme—current work and the way ahead. Total Qual Manag 13(8):1099–1106. https://doi.org/10.1080/09544120200000005
Fomby TB, Johnson SR, Hill RC (2011) Advanced econometric methods. Advanced econometric methods. Springer, New York. https://doi.org/10.1007/978-1-4419-8746-4
Fornell C, Johnson MD, Anderson EW, Cha J, Bryant BE (1996) The American customer satisfaction index: nature, purpose, and findings. J Mark 60(4):7. https://doi.org/10.2307/1251898
Gallier J, Quaintance J (2019) Algebra, topology, differential calculus, and optimization theory for computer science and engineering. Philadelphia, PA. Retrieved Feb 20, 2019, from https://www.cis.upenn.edu/~jean/math-basics.pdf
Gerbing DW, Hamilton JG (1994) The surprising viability of a simple alternate estimation procedure for construction of large-scale structural equation measurement models. Struct Equ Model A Multidiscip J 1(2):103–115. https://doi.org/10.1080/10705519409539967
Hair JF, Hult GTM, Ringle CM, Sarstedt M, Thiele KO (2017) Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J Acad Mark Sci 45(5):616–632. https://doi.org/10.1007/s11747-017-0517-x
Hwang H, Takane Y (2014) Generalized structured component analysis: a component-based approach to structural equation modeling. Chapman and Hall/CRC Press, New York
Hwang H, Malhotra NK, Kim Y, Tomiuk MA, Hong S (2010) A comparative study on parameter recovery of three approaches to structural equation modeling. J Mark Res 47(4):699–712. https://doi.org/10.2139/ssrn.1585305
Hwang H, Takane Y, Tenenhaus A (2015) An alternative estimation procedure for partial least squares path modeling. Behaviormetrika 42(1):63–78. https://doi.org/10.2333/bhmk.42.63
Hwang H, Sarstedt M, Cheah JH, Ringle CM (2019) A concept analysis of methodological research on composite-based structural equation modeling: bridging PLSPM and GSCA. Behaviormetrika. https://doi.org/10.1007/s41237-019-00085-5
Jarvis CB, MacKenzie SB, Podsakoff PM (2003) A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J Consum Res 30(2):199–218. https://doi.org/10.1086/376806
Jöreskog KG (1970) Estimation and testing of simplex models. Br J Math Stat Psychol 23(2):121–145. https://doi.org/10.1111/j.2044-8317.1970.tb00439.x
Jöreskog KG (1978) Structural analysis of covariance and correlation matrices. Psychometrika 43(4):443–477. https://doi.org/10.1007/BF02293808
Lay DC, Lay SR, McDonald JJ (2015) Linear algebra and its applications, 576
Lohmöller J-B (1989) Latent variable path modeling with partial least squares. Springer, New York. https://doi.org/10.1007/978-3-642-52512-4
Marsh HW, Hau KT, Balla JR, Grayson D (1998) Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivar Behav Res 33(2):181–220. https://doi.org/10.1207/s15327906mbr3302_1
Rego LL, Morgan NA, Fornell C (2013) Reexamining the market share-customer satisfaction relationship. J Mark 77(5):1–20. https://doi.org/10.1509/jm.09.0363
Reinartz W, Haenlein M, Henseler J (2009) An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int J Res Mark 26(4):332–344. https://doi.org/10.1016/j.ijresmar.2009.08.001
Rigdon EE (2012) Rethinking partial least squares path modeling: in praise of simple methods. Long Range Plan 45(5–6):341–358. https://doi.org/10.1016/j.lrp.2012.09.010
Roldán JL, Sánchez-Franco MJ (2012) Variance-based structural equation modeling: guidelines for using partial least squares in information systems research. In: Mora M, Gelman O, Steenkamp AL, Raisinghani M (eds) Research methodologies, innovations and philosophies in software systems engineering and information systems. IGI Global, Hershey, pp 193–221. https://doi.org/10.4018/978-1-4666-0179-6.ch010
Sarstedt M, Hair JF, Ringle CM, Thiele KO, Gudergan SP (2016) Estimation issues with PLS and CBSEM: where the bias lies! J Bus Res 69(10):3998–4010. https://doi.org/10.1016/j.jbusres.2016.06.007
Sharma PN, Shmueli G, Sarstedt M, Danks N, Ray S (2018) Prediction-oriented model selection in partial least squares path modeling. Decis Sci 00:1–41. https://doi.org/10.1111/deci.12329
Shmueli G, Ray S, Velasquez Estrada JM, Chatla SB (2016) The elephant in the room: predictive performance of PLS models. J Bus Res 69(10):4552–4564. https://doi.org/10.1016/J.JBUSRES.2016.03.049
Tenenhaus M (2008) Component-based structural equation modelling. Total Quality Manag Bus Excell 19(7–8):871–886. https://doi.org/10.1080/14783360802159543
Tenenhaus M, Esposito Vinzi V, Chatelin Y-M, Lauro C (2005) PLS path modeling. Comput Stat Data Anal 48(1):159–205. https://doi.org/10.1016/J.CSDA.2004.03.005