Covariance-Based Structural Equation Modeling in the Journal of Advertising: Review and Recommendations

790 citations recorded by the source corpus.

How researchers see this paper

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

Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling (2017, background): “For a more detailed description of reflective measurement, we refer to Hair, Babin, and Krey (2017) in this issue of Journal of Advertising.”
Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy (2024, positive): “Referring to Yu et al. (2023), the model fit was assessed using the standards employed by Bagozzi and Yi (1988), Hair et al. (2017), Hayduk (1987), and Hu and Bentler (1998).”
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “However, Hair et al. (2017d) also found that PLS-SEM’s performance in the structural model where”
A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario (2017, methodology): “As a rule of thumb, one cannot delete more than 20 percent of the total items in the model, be it CBSEM or PLS-SEM (Hair et al., 2017a; Hair, Black, Babin, & Anderson, 2010).”
What motivates users to hashtag on social media? (2019, methodology): “Both AVE and composite reliability (CR) show acceptable values on a factor level, and the global measurement indices echo this (Hair et al., 2017).”