The University of Waikato

PLS-SEM Using SmartPLS Training Workshop

How to join for free

Just send an email to amanda.wilson@waikato.ac.nz (and CC to siggi.gudergan@waikato.ac.nz and c.ringle@tuhh.de)

Location

University of Waikato, Waikato Management School, Gate 7, Hillcrest Road, Hamilton, New Zealand - Room MSB 1.36 and 1.37, Find on Google Maps

Agenda

PLS-SEM Foundations on Tuesday, March 24, 2020:

  • 13:00 to 15:00 Model-setup and PLS-SEM estimation; introduction to SmartPLS 3

30 minutes break

  • 15:30 to 17:00: Assessment and reporting of measurement and structural model results; application in SmartPLS 3

Advanced PLS-SEM Topics on Wednesday, March 25, 2020:

  • 9:00 to 10:30: Prediction-oriented results analysis; application in SmartPLS 3

20 minutes break

  • 10:50 to 12:20: Higher-order constructs (e.g., second-order models); application in SmartPLS 3

40 minutes lunch break

  • 13:00 to 15:00: More PLS-SEM advances and their application in SmartPLS 3

Course Objectives

This two-day workshop introduces participants to the state-of-the-art of partial least squares structural equation modeling (PLS-SEM) using the SmartPLS 3 software. PLS-SEM 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-SEM 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. 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 Journal of Marketing Theory and Practice; Hair et al. 2012a, according to Google scholar the most-cited Journal of the Academy of Marketing Science article since 2012),
  • strategic management (Hair et al. 2012b, according to Google scholar the most-cited Long Range Planning article since 2012),
  • management information systems (Ringle et al. 2012, according to Google scholar the fifth-most cited MIS Quarterly article since 2012), and
  • business administration in general (Sarstedt et al. 2016, according to Google scholar the second-most cited article published in the Journal of Business Research since 2016).

Also, several review studies across different disciplines substantiate that PLS-SEM has become a standard method in the multivariate analysis toolbox: e.g., Human Resource Management (Ringle et al., 2018), Hospitality Research (Ali et al., 2018), Information Systems Research (Hair et al., 2017), Management Accounting (Nitzl, 2016), International Business (Richter et al., 2016), Tourism (do Valle and Assaker, 2016), Psychology (Willaby et al., 2015), Supply Chain Management (Kaufmann and Gaeckler, 2015), Family Business (Sarstedt et al., 2014), Operations Management (Peng and Lai, 2012), Strategic Management (Hair et al., 2012), Marketing (Hair et al., 2012), Management Information Systems (Ringle et al., 2012), Accounting (Lee et al., 2011), and International Marketing (Henseler et al., 2009).

Learning outcomes

This course has been designed to familiarize participants with the potentials of using the multivariate analysis method PLS-SEM in their research. The objectives of this course are to provide (1) an in-depth methodological introduction into the PLS-SEM approach the nature of causal modeling, analytical objectives, some statistics), (2) the evaluation of measurement results, and (3) complementary analytical techniques. More specifically, participants will understand the following topics:

  • Model development and fundamentals of PLS-SEM and latent variable models
  • Assessment and reporting of measurement and structural model results including Bootstrapping
  • New criteria for model assessment such as HTMT for discriminant validity
  • Prediction-oriented results analysis including PLSpredict and predictive model comparison
  • Importance-performance map analysis (IPMA) of PLS-SEM results
  • Higher-order constructs (e.g., second-order models)

Case studies using the SmartPLS 3 software are an integral part of the course. Thereby, the participants learn how to use the PLS-SEM method and the SmartPLS 3 software for their own analyses.

Who should attend?

This course has been designed for full-time faculty and PhD students who are interested in learning how to step-up their research towards well-designed and publishable outputs that potentially survive the test of time and are read and cited. A basic knowledge of univariate and multivariate statistics and SEM techniques is helpful, but not required.

Learning methods

  • Lectures/Presentations: The sessions will cover theory and its application. 
  • Computer exercises and case studies useing the latest SmartPLS 3 version.

Teaching resources

The Book on PLS-SEM and Software

Hair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, and Marko Sarstedt (2017), A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousands Oak, CA: Sage Publications.

Hair, Joseph F., Marko Sarstedt, Christian Ringle, and Siegfried P. Gudergan (2018), Advanced issues in partial least squares structural equation modeling. Thousands Oaks, CA: Sage Publications.

Ringle, Christian M., Sven Wende, and Jan-Michael Becker (2015), SmartPLS 3. Bönningstedt: SmartPLS.

   

Journal Articles and Book Sections

More PLS-SEM literature and publications