PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement

By Vincenzo Esposito Vinzi, L. Trinchera, Silvano Amato. 2010

1,089 citations recorded by the source corpus.

Subfields: Electrochemistry, Food Science

View Paper: https://www.semanticscholar.org/paper/a6b9ce57106ce5679cc377e86c499dc4fdebb4b8

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Citations in the corpus

Using Partial Least Squares Structural Equation Modeling in Tourism Research (2016, background): “All of these characteristics have been extensively discussed in the methodological literature on PLS-SEM (see Vinzi, Trinchera, and Amato 2010; Lohmöller 1989).”
Remembered experiences and revisit intentions: A longitudinal study of safari park visitors (2016, methodology): “In: EspositoVinzi, V., Chin, W.W., Henseler, J., and Wang, H. (eds.)”
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”
Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research (2012, neutral): “In this sense, PLS is prediction-oriented (Fornell and Bookstein, 1982; Vinzi et al., 2010).”
Linking Entrepreneurial Orientation with Innovation Performance in SMEs; the Role of Organizational Commitment and Transformational Leadership Using Smart PLS-SEM (2021, methodology): “PLS focuses on variance analysis, which could be done using Smart PLS [99].”
Online impulse buying of tourism products: The role of web site personality, utilitarian and hedonic web browsing (2016, neutral): “Furthermore, PLS does not required assumptions such as normality of data distributions and sample size (Vinzi et al., 2010), and it is an advantage when the primary concern of the analysis is the prediction originated or prediction accuracy (Sarstedt, 2008; Hair et al.”
An Evaluation of PLS Based Complex Models: the Roles of Power Analysis, Predictive Relevance and GoF Index (2011, neutral): “Because of the descriptive nature of GoF index, there is no inference based criteria to assess its statistical significance (Vinzi et al. 2010).”
Partial least squares path modeling: Time for some serious second thoughts (2016, methodology): “This labeling of the algorithm is confusing, because: (a) the term “PLS regression” is used also for a completely different algorithm (Hastie et al., 2013, Chapter 3; Vinzi et al., 2010); and (b) it obfuscates the fact that the analysis is simply regression with principal components.”
Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats (2013, methodology): “This requirement for models with only reflective measures also holds for the REBUS-PLS implementation in the PLSPM package (Sánchez and Trinchera 2013) for the statistical software R (R Core Team 2013).”
Technology Readiness and Customer Satisfaction with Travel Technologies: A Cross-Country Investigation (2017, neutral): “…often been conducted by examining path differences using a t -test based on the pooled standard errors obtained via a re-sampling procedure such as bootstrapping (Vinzi, Trinchera, and Amato 2010), problems can occur if a normal population or similar sample size is assumed (Chin and Dibbern 2010).”
Path modelling by sequential PLS regression (2011, methodology): “approach is to use PLS regression for estimating relations both between and within the blocks (References [32]; pp.”