Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares

By John Hulland, Michael J. Ryan, Robert K. Rayner. 2010

96 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Food Science, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/b78fabd77b1841fe25a09157280a5061a62c4184

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Citations in the corpus

Big data analytics and firm performance: Effects of dynamic capabilities (2017, methodology): “…the higher-order BDA capabilities model, the study applied partial least squares based structural equation modeling (PLS-SEM) because it estimates hierarchical models by removing the uncertainty of inadmissible solutions using its flexible assumptions (Hair et al., 2011, Hulland et al., 2010).”
Modelling quality dynamics, business value and firm performance in a big data analytics environment (2017, methodology): “…the second-order hierarchical system quality, information quality, business value and firm performance, the study applied PLS-SEM because it estimates hierarchical model by removing the uncertainty of inadmissible solutions using its flexible assumptions (Hair et al., 2011; Hulland et al., 2010).”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, background): “The mean absolute error (MAE; e.g., Hulland et al. 2010) is a commonly used quantity to measure how close estimates and pre-specified parameters are.”
Re-examining the relationships among perceived quality, value, satisfaction, and destination loyalty (2018, methodology): “Data collected from the survey was analyzed using descriptive statistics, exploratory factor analysis and partial least squares-SEM (PLSSEM) analysis through XL-STAT/PLSPM. PLS was adopted due to the higher order formative conceptualization for PQ (see Hulland et al. 2010).”
Performance Implications of Mismatched Governance Regimes across External and Internal Relationships (2011, methodology): “Specifically, we reestimated our structural model after allowing a method factor to load freely onto all indicators, following the general PLS estimation procedure described by Podsakoff et al. (2003) and adapted by Liang et al. (2007, pp. 85–87).”
Common Beliefs and Reality About PLS (2014, background): “, Marcoulides & Saunders, 2006) and has been empirically examined in various simulation studies (e.g., Areskoug, 1982; Goodhue et al., 2012; Hulland et al., 2010; Lu et al., 2011; Reinartz et al., 2009; Vilares & Coelho, 2013).”