PLS-SEM Workshop at the Global Marketing Conference, Seoul, 20-23 Jul 2023
INTEREST CATEGORY: MARKETING RESEARCH
POSTING TYPE: Revisits
Author: Kyung Hoon Kim
2023 GAMMA WORKSHOP
Partial Least Squares Structural Equation Modeling (PLS-SEM): Foundations
July 20, 2023
Yonsei University, Seoul, Republic of Korea
– Prof. Dr. Christian M. Ringle, Hamburg University of Technology (TUHH) (Germany)
Email: firstname.lastname@example.org | Internet: https://www.tuhh.de/hrmo/team/prof-dr-c-m-ringle.html
– Prof. Dr. Dr. h.c. Marko Sarstedt, Ludwig-Maximilians-University Munich (Germany) and Adjunct Research Professor, Babeș-Bolyai-University Cluj (Romania)
Email: email@example.com | Internet: https://www.en.marketing.bwl.uni-muenchen.de/
- Course objectives:
Partial least squares structural equation modeling (PLS-SEM) has recently received considerable attention in a variety of disciplines, including marketing, strategic management, management information systems, and many more.
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 enables 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 one-day workshop introduces participants to the state-of-the-art of PLS-SEM using the SmartPLS 4 software. After a brief introduction to the basic principles of structural equation modeling, participants will understand the foundations of PLS-SEM and how to apply the method by means of the SmartPLS 4 software. The workshop will cover various aspects related to the evaluation of measurement and structural model results. For this purpose, the instructors will make use of several examples and exercises.
- Learning outcomes:
This workshop is designed to familiarize with the potentials of using PLS-SEM in research. The objectives of this course are to provide a methodological introduction into the PLS-SEM approach (the nature of causal-predictive modeling, analytical objectives, some statistics) and the evaluation of measurement and structural model results. More specifically, participants will comprehend the following topics:
- Fundamentals of PLS-SEM,
- Model set up and estimation,
- Assessment and reporting of measurement model results,
- Assessment and reporting of structural model results, particularly focusing on the prediction-oriented results assessment, and
- Results interpretation.
This course has been designed for PhD students and faculty who are interested in learning how to use the PLS-SEM method in their own research applications. A basic knowledge of multivariate statistics and SEM techniques is helpful, but not required.
- Teaching and learning methods;
- The course is based on the PLS-SEM textbooks:
- Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 3rd edition. Thousand Oaks, CA: Sage.
- Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage.
- Presentations: The session will cover theory and its application.
- Computer exercises using the latest SmartPLS 4 version: Specifically, theoretical explanations underlying the software procedures and practical exercises where participants will apply their learning to real-world examples provided by the instructors.
- Registration and practical issues:
- Conference participants can register for the workshop as part of the conference registration process. Please visit: https://2023gmc.imweb.me/
- Tuition: GAMMA Prestige Club Member: USD 100; KSMS Member: USD 150; Non-KSMS Member (Student): USD 200; Non-KSMS Member (Faculty): USD 300
- Bring your laptop computer and a 2 or 3-way power extension lead.
- Download and install the SmartPLS 4 software from https://www.smartpls.com/ before coming to the workshop. Participants will receive detailed instructions – including a two-months license key – shortly before the course starts.
- Teaching resources:
Comprehensive lecture slides will be provided to all participants
Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 3rd edition. Thousand Oaks, CA: Sage.
Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM), Thousand Oaks: Sage.
Journal Articles (selection):
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, Mirror on the Wall: A Comparative Evaluation of Composite-based Structural Equation Modeling Methods. Journal of the Academy of Marketing Science, 45(5), 616-632.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31(1), 2-24.
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking Some of the Rethinking of Partial Least Squares. European Journal of Marketing, 53(4), 566-584.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common Beliefs and Reality about Partial Least Squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(2), 182-209.
Rigdon, E. E. (2012). Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods. Long Range Planning, 45(5-6), 341-358.
Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On Comparing Results from CB-SEM and PLS-SEM. Five Perspectives and Five Recommendations. Marketing ZFP, 39(3), 4-16.
Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in Partial Least Squares Structural Equation Modeling use in Marketing in the Last Decade, Psychology & Marketing, 39(5), 1035-1064.
Sarstedt, M., Hair, J. F., & Ringle, C. M. (2022). “PLS-SEM: Indeed a silver bullet” – A Retrospective and Recent Advances. Journal of Marketing Theory & Practice, Advance online publication.
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation Issues with PLS and CBSEM: Where the Bias Lies!, Journal of Business Research, 69(10), 3998-4010.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In: Homburg, C., Klarmann, M., and Vomberg, A. (Eds.). Handbook of Market Research, New York et al.: Springer.
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., & Ringle, C. M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.
– Date: July 20, 2023
– Location: Yonsei University, Seoul, Republic of Korea
|09:00 – 10:30||Introduction to PLS-SEM and the SmartPLS software|
|10:30 – 11:00||Break|
|11:00 – 12:30||Assessing measurement model results (part I)|
|12:30 – 13:30||Lunch|
|13:30 – 15:00||Assessing measurement model results (part II)|
|15:00 – 15:30||Break|
|15:30 – 17:00||Assessing structural model results; outlook on advanced topics|
- Instructors’ short bios:
Christian M. Ringle, is a chaired professor of management the Hamburg University of Technology (Germany). His research addresses the field of management and quantitative methods for business analytics, machine learning, and market research. Christian’s contributions in these fields have been published in journals such as International Journal of Research in Marketing, Information Systems Research, Journal of Business Research, Journal of the Academy of Marketing Science, MIS Quarterly, and Organizational Research Methods. With more than 100,000 citations within 5 years (Google Scholar), he is one of the most influential researchers in the field of economics and business. Since 2018, Christian has been included in the Clarivate Analytics’ Highly Researchers list.
Marko Sarstedt is a chaired professor of marketing at the Ludwig-Maximilians-University Munich (Germany) and an adjunct research professor at Babeș-Bolyai-University Cluj-Napoca (Romania). His main research interest is the advancement of research methods to further the understanding of consumer behavior. His research has been published in Nature Human Behavior, Journal of Marketing Research, Journal of the Academy of Marketing Science, Multivariate Behavioral Research, Organizational Research Methods, MIS Quarterly, Psychometrika, Structural Equation Modeling: A Multidisciplinary Journal, and British Journal of Mathematical and Statistical Psychology, among others. Marko’s research has been cited over 100,000 times according to Google Scholar and he has repeatedly named member of Clarivate Analytics’ Highly Cited Researchers List, which includes the “world’s most impactful scientific researchers.”