Common Beliefs and Reality About PLS

Abstract

This article addresses Rö nkkö and Evermann's criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann's study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rö nkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

The Effect of Absorptive Capacity on Green Customer Capital under an Organizational Unlearning Context

“…First, we had a composite measurement model. Both theoretical studies [97][98][99] and empirical simulation studies [100,101] recommend and support the use of PLS for composite models. Second, as per Chin's [102] indications, we used PLS because we employed latent variable scores in subsequent analysis for modeling a second-order multidimensional construct, applying the higher-order component two-stage approach [103].…”

The Impact of Managerial and Adaptive Capabilities to Stimulate Organizational Innovation in SMEs: A Complementary PLS–SEM Approach

“…More specifically, SmartPLS 3 [116,117] was used to estimate the research model (for detailed reasons of why and when to use PLS-SEM, see for example, Richter, Cepeda, Roldán, & Ringle [118]). Despite a surprising level of animosity towards PLS-SEM [118][119][120][121], PLS-SEM has been widely accepted by the scholarly community, including authors, reviewers, and editors [122][123][124][125]. The following points summarize why this study adopted PLS-SEM instead of Linear structural relations (LISREL) or Analysis of a moment structures (AMOS) as more suitable statistical techniques: (1) the structural model is complex, and contains four series of dependent relationships [118,126]; (2) the research objective of the structural model is prediction oriented, and explaining the variance in key target constructs [126,127]; (3) this study analyzes the relationships between managerial capability, adaptive capability, and organizational innovation; which is being considered in the initial stages of theory development, therefore motivated us to investigate the related phenomena in this emerging area [118]; (4) the sample size (n = 210) is also believed to be relatively small [127], finally; (5) this study also takes advantage of PLS-SEM in terms of its less rigorous requirements for restrictive assumptions, which motivates researchers to develop and estimate such models through enabling them to avoid additional limiting constraints [117,128].…”

“…The results are given in Table 4. Finally, as additional analysis, this study also reports a recently introduced overall goodness-of-fit measure-standardized root mean square residual (SRMR)-as an index for PLS-SEM model validation [123]. The difference between the observed correlation and the predicted correlation is defined as the absolute measure of model fit.…”

Predictores de la satisfacción laboral en la industria automotriz de Ciudad Juárez, México

“…Porque es frecuente que los datos obtenidos en estudios transversales no sigan una distribución normal multivariada (Landeros y González, 2006), se consideró pertinente utilizar un enfoque no paramétrico basado en PLS-SEM para la comprobación de las hipótesis de investigación, (Henseler et al, 2014). La cantidad de casos recolectados (301) cumple el criterio recomendado por para el tamaño de muestra, quienes sugieren un mínimo de 75 observaciones para detectar una R 2 de 0.25 con un nivel de significancia del 1 %, y una potencia estadística del 80 % para modelado de Ecuaciones Estructurales con Cuadrados Mínimos Parciales (PLS-SEM).…”