PLS-SEM or CB-SEM: updated guidelines on which method to use

By Joseph F. Hair, L. Matthews, Ryan L. Matthews, Marko Sarstedt. 2017

4,486 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0a4787bf5c0e58fa43ff45c9874140b0d421002c

How researchers see this paper

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

In Artificial Intelligence

In Information Systems and Management

In Statistics and Probability

Citations in the corpus

Understanding memorable tourism experiences and behavioural intentions of heritage tourists (2021, background): “In addition, the values of Q2 are 0.50, 0.21, and 0.45 for MTE, eWOM intention, and revisit intention respectively, which are considered very high to assess predictive reliance of research model (Hair et al., 2017).”
Comparison Between CB-SEM and PLS-SEM: Testing and Confirming the Maqasid Syariah Quality of Life Measurement Model (2019, methodology): “Indicators with 0.40 to 0.70 of factor loading were suggested to be deleted, however, they may be considered for the analysis as long as the convergent validity is satisfied (Hair et al., 2017a).”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, background): “According to Hair et al. (2017), composite reliability is more appropriate because it considers the indicators ’ differential weights while Cronbach ’ s alpha weights the indicators equally.”
An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance (2021, methodology): “However, simple guidelines were set forth by Preacher and Hayes (2004) and Hair Jr, Matthews, Matthews, and Sarstedt (2017) to select the right technique depending on the nature of the research work.”
Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model (2021, methodology): “Second, the Heterotrait ‐ Monotrait Ratio (HTMT) of the correlations between variables were below the 0.9 threshold for all cases (Hair et al., 2017; Henseler et al., 2015).”
Personality Traits, Social Self-Efficacy, Social Support, and Social Entrepreneurial Intention: The Moderating Role of Gender (2021, methodology): “We used SmartPLS 3.0 software (Hair et al. 2017) to measure confirmatory factor analysis (CFA) and structural relationships among the study variables.”
Perceived public support and entrepreneurship attitudes: A little reciprocity can go a long way! (2020, background): “The Standardised Root Mean Square Residual (SRMR), which captures the root mean square discrepancy between the observed correlations and the model - implied correlations (Hair et al., 2017), amounted to 0.05, which is less than the recommended threshold of 0.08 suggested by Hu and Bentler (1998).”
The Influence of Place Attachment, Ad-Evoked Positive Affect, and Motivation on Intention to Visit: Imagination Proclivity as a Moderator (2020, methodology): “outer weight significance and collinearity (Hair et al. 2017)).”
Formative Vs. Reflective Measurement Model: Guidelines for Structural Equation Modeling Research (2020, methodology): “Among the advantages possessed by PLS-SEM if to be compared with the basic regression is its ability in handling various independent variable at one time even when it displays multicollinearity ([3]; [6]).”