The elephant in the room: Predictive performance of PLS models

Armstrong, 2012, Illusions in regression analysis, International Journal of Forecasting, 28, 689, 10.1016/j.ijforecast.2012.02.001

Ball, 1963, The significance of simultaneous methods of parameter estimation in econometric models, Journal of the Royal Statistical Society, Series C, 12, 14

Barclay, 1995, The partial least squares (PLS) approach to causal modeling, personal computer adoption and use as an illustration, Technology Studies, 2, 285

Baron, 1986, The moderator–mediator variable distinction in social psychological research – conceptual, strategic, and statistical considerations, Journal of Personality and Social Psychology, 51, 1173, 10.1037/0022-3514.51.6.1173

Becker, 2013

Chin, 1998, The partial least squares approach to structural equation modelling, 295

Chin, 2010, Bootstrap cross-validation indices for PLS path model assessment

Chin, 2015, On partial least squares variance-based SEM (VBSEM) vs. covariance-based SEM (CBSEM) for confirmatory analysis: It's all about the components and variance explained

Churchill, 1979, A paradigm for developing better measures for marketing constructs, Journal of Marketing Research, 16, 64, 10.1177/002224377901600110

Dana, 2004, The superiority of simple alternatives to regression for social science predictions, Journal of Educational and Behavioral Statistics, 29, 317, 10.3102/10769986029003317

Diamantopoulos, 2012, Guidelines for choosing between multi-item and single-item scales in construct measurement: a predictive validity perspective, Journal of the Academy of Marketing Science, 40, 434, 10.1007/s11747-011-0300-3

Dijkstra, 1983, Some comments on maximum likelihood and partial least squares methods, Journal of Econometrics, 22, 67, 10.1016/0304-4076(83)90094-5

Dijkstra, 2010, Latent variables and indices: Herman Wold’s basic design and Partial Least Squares, 23

Dijkstra, 2015, Consistent partial least squares path modeling, MIS Quarterly, 39, 297, 10.25300/MISQ/2015/39.2.02

Dubin, 1969

Evermann, 2012, Comparing the predictive ability of PLS and covariance analysis

Evermann, 2014, Comparing out-of-sample predictive ability of PLS, covariance, and regression models

Geisser, 1974, A predictive approach to the random effects model, Biometrika, 61, 101, 10.1093/biomet/61.1.101

Hair, 2011, PLS-SEM: indeed a silver bullet, Journal of Marketing Theory and Practice, 19, 139, 10.2753/MTP1069-6679190202

Hair, 2014

Hastie, 2009

Henseler, 2010, A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling, Structural Equation Modeling: A Multidisciplinary Journal, 17, 82, 10.1080/10705510903439003

Kaplan, 2009

Lam, 2002, Bootstrap prediction intervals for single period regression forecasts, International Journal of Forecasting, 18, 125, 10.1016/S0169-2070(01)00112-1

Lohmöller, 1989

Monecke, 2012, semPLS: structural equation modeling using partial least squares, Journal of Statistical Software, 48, 1, 10.18637/jss.v048.i03

Pornprasertmanit, 2014, simsem: SIMulated Structural Equation Modeling in R

Ringle, 2005

Schoemann, 2014, Using Monte Carlo simulations to determine power and sample size for planned missing designs, International Journal of Behavioral Development, 38, 471, 10.1177/0165025413515169

Sharma, 2013, 201

Shmueli, 2010, To explain or to predict?, Statistical Science, 25, 289, 10.1214/10-STS330

Shmueli, 2010, What is predictive about partial least squares?

Shmueli, 2011, Predictive analytics in information systems research, MIS Quarterly, 35, 553, 10.2307/23042796

Stone, 1974, Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society, Series B, 36, 111, 10.1111/j.2517-6161.1974.tb00994.x

Tenenhaus, 2005, PLS path modeling, Computational Statistics and Data Analysis, 48, 159, 10.1016/j.csda.2004.03.005

Vale, 1983, Simulating multivariate nonnormal distributions, Psychometrika, 48, 465, 10.1007/BF02293687

Wold, 1982, Models for knowledge

Woodside, 2011, Responding to the severe limitations of cross-sectional surveys: commenting on Rong and Wilkinson's perspectives, Australasian Marketing Journal, 19, 153, 10.1016/j.ausmj.2011.04.004

Woodside, 2013, Moving beyond multiple regression analysis to algorithms: calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory, Journal of Business Research, 66, 463, 10.1016/j.jbusres.2012.12.021