Partial least squares structural equation modeling (PLS-SEM) has recently received considerable attention in a variety of disciplines, including marketing (Hair et al 2011, according to Google scholar the most-cited article ever published in JMTP; Hair et al. 2012a, according to Google scholar the most-cited JAMS article since 2012), strategic management (Hair et al. 2012a, according to Google scholar the most-cited LRP article since 2012), and management information systems (Ringle et al. 2012, according to Google scholar the second-most cited MIS Quarterly article since 2012).
PLS is a composite-based approach to SEM, which aims at maximizing the explained variance of dependent constructs in the path model. Compared to other SEM techniques, PLS allows researchers to estimate very complex models with many constructs and indicator variables. Furthermore, PLS-SEM allows to estimate reflective and formative constructs and generally offers much flexibility in terms of data requirements. This workshop introduces participants to the state-of-the-art of PLS-SEM using the SmartPLS 3 software.
The objectives of this course are to provide an in-depth methodological introduction into:
- the PLS-SEM approach (the nature of causal modelling, analytical objectives, some statistics).
- the evaluation of measurement and structural model results.
- advanced analytical techniques.
Participants of the first module will understand the following topics:
- Model development and fundamentals of PLS-SEM.
- Current debates about PLS-SEM.
- Assessment and reporting of measurement model results, including the new criterion for discriminant validity testing: The heterotrait-monotrait ratio of correlations (HTMT).
- Assessment and reporting of structural model results.
- Mediating effects.
- Moderating effects (interaction effects).
After having taken the second module, which also includes a brief recap of the basics of PLS-SEM, participants will understand the following topics:
- Higher-order modeling
- Confirmatory tetrad analysis
- Importance-performance map analysis
- Measurement invariance
- Multigroup analysis
- Unobserved heterogeneity (FIMIX-PLS, PLS-POS, and further techniques)