Handbook of Partial Least Squares: Concepts, Methods and Applications

By Vincenzo Esposito Vinzi, William Chin, Jörg Henseler, Hui-chen Wang. 2010

1,218 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/85df55ea5f79d9a308dca429cd3858e322c95a73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The Cognitive-Affective-Conative Model of Destination Image: A Confirmatory Analysis (2013, background): “Indirect effects, also called mediating effects, take place when an independent variable, X, has an impact on a third variable, Z, which then influences the dependent variable, Y (Henseler & Fassott, 2010).”
Using PLS path modeling in new technology research: updated guidelines (2016, background): “Note that early suggestions for PLS-based GoF measures such as the “goodness-of-fit” (see Tenenhaus et al., 2004) or the “relative goodness-of-fit” (proposed by Esposito Vinzi et al., 2010) are – in opposite to what their name might suggest – not informative about the goodness of model fit (Henseler”
PLS' Janus Face – Response to Professor Rigdon's ‘Rethinking Partial Least Squares Modeling: In Praise of Simple Methods’ (2014, methodology): “The Handbook of PLS (Esposito Vinzi et al, 2010) and its many references is an impressive tribute to the fruitfulness and intellectual stimulus of Wold’s approach.”
The impacts of brand experiences on brand loyalty: mediators of brand love and trust (2017, background): “The aim of this study is to predict the relationships among multiple latent variables, rather than to confirm a particular theory (Hair et al., 2011; Hair, Sarstedt et al., 2012; Vinzi et al., 2010); (2).”
Precision of farmer-based fertility ratings and soil organic carbon for crop production on a Ferralsol (2015, methodology): “The PLS regression model guided the creation of a latent variable model from which all soil properties that influence yield are combined so that it establishes maximum explanation of variation in yield (Esposito Vinzi et al., 2010).”
Satisfaction and revisit intentions at fast food restaurants (2020, methodology): “Purpose of bootstrapping is to compute the standard error of coefficient estimates in order to examine the coefficient’s statistical significance [89].”
A comparative review of dimension reduction methods in approximate Bayesian computation (2012, background): “Partial least squares regression seeks the orthogonal linear combinations of the explanatory variables which have high variance and high correlation with the response variable (e.g. Boulesteix and Strimmer 2007; Vinzi et al. 2010; Abdi and Williams 2010).”
A study on the adoption of IoT smart home service: using Value-based Adoption Model (2017, methodology): “This value must be 0.7 or higher for the variables to be assessed as reliable (Esposito Vinzi, Chin, Henseler, & Wang, 2010).”
Creativity, self-efficacy, and small-firm performance: the mediating role of entrepreneurial orientation (2015, background): “, the loadings) rather than on the correlations observed between the manifest variables in the dataset (Vinzi et al. 2010).”