2024 GAMMA NCA & PLS-SEM Workshop

2024 GAMMA WORKSHOP

Combined Usage of the Necessary Condition Analysis (NCA) and Partial Least Squares Structural Equation Modeling (PLS-SEM)

- 09:00-15:00 July 11, 2024

- University of Milan-Biccoca, Milan, Italy

1    Instructor and a short bio

Prof. Dr. Christian M. Ringle

Institute of Management and Decision Sciences (MDS)

Hamburg University of Technology (TUHH), Germany

Email: c.ringle@tuhh.de | Internet: https://www.tuhh.de/mds/team/prof-dr-c-m-ringle.html

Christian M. Ringle is a management and decision sciences professor at the Hamburg University of Technology (Germany). His research focuses on management and marketing topics, method development, business analytics, machine learning, and the application of business research methods for decision-making. His contributions have been published in journals such as the International Journal of Research in Marketing, Information Systems Research, Journal of the Academy of Marketing Science, MIS Quarterly, Organizational Research Methods, and The International Journal of Human Resource Management. Since 2018, he has been named a member of Clarivate Analytics' Highly Cited Researchers List. In 2014, Ringle cofounded SmartPLS (https://www.smartpls.com), a software tool with a graphical user interface for applying the partial least squares structural equation modeling (PLS-SEM) method. Besides supporting consultancies and international corporations, he regularly teaches doctoral seminars on business analytics and multivariate statistics, the PLS-SEM method, and the use of SmartPLS worldwide. 

More information about Professor Ringle can be found at 

https://www.tuhh.de/mds/team/prof-dr-c-m-ringle.html

2    Background on the combined use of PLS-SEM and the NCA

In their Journal of Management editorial, Bergh et al. (2022) highlight the relevance and increasing use of the necessary condition analysis (NCA; Dul, 2016) in management research. They also note the unique potential researchers can leverage by combining NCA with the partial least squares structural equation modeling (PLS-SEM) method in their studies. These findings also apply to marketing research. Our special session introduces and encourages the combined use of PLS-SEM and the NCA, enabling researchers to explore and validate hypotheses following a sufficiency logic while also considering a necessity logic. 

PLS-SEM belongs to a family of regression-based methods for estimating models with latent variables developed by the Swedish econometrician Herman Wold (1985). Since the 2000s, PLS-SEM has gained widespread popularity in a variety of disciplines, among them (international) marketing and management research (e.g., Hair et al., 2012; Richter et al., 2016; Sarstedt et al., 2022a; Sarstedt et al., 2022b). The method estimates theoretically established causal-predictive relationships (Wold, 1982) between latent variables while accounting for measurement error inherent in the indicators. The results can empirically substantiate the determinants (X) that lead to an outcome (Y) (Sarstedt et al., 2021). Authors interpreting their PLS-SEM findings often use expressions such as "X increases Y" or "a higher X leads to a higher Y." The interpretation follows a sufficiency logic (Richter et al., 2020), which is highly relevant for deriving managerial recommendations from the results. For example, researchers aim to understand the factors that lead to a stronger intention to use certain technology by applying different theories of technology acceptance (e.g., Lin & Lin, 2019); or they aim to understand the factors that contribute to a higher loyalty of their customers (e.g., Ahrholdt et al., 2019; Hegner-Kakar et al., 2018). 

In contrast to this sufficiency logic of standard PLS-SEM analyses (Becker et al., 2023; Hair et al., 2019; Sarstedt et al., 2021), the NCA follows a necessity logic ("X is necessary for Y") in that it seeks to identify necessary conditions in variable relationships. A necessary condition is a critical factor for an outcome: if the necessary cause is not in place, the outcome will not materialize. Hence, the necessary condition can be a bottleneck, critical factor, constraint, disqualifier, etc. The right level of a necessary condition must be put and kept in place to avoid guaranteed failure. By adding a different logic and data analysis approach, an NCA adds both rigor and relevance to theory, data analysis, and publications (Dul, 2020). Because of the high practical relevance of NCA results, this relatively new method has recently attracted much attention in the academic community (e.g., Bokrantz & Dul, 2023; Hauff et al., 2021; Richter & Hauff, 2022).

Using PLS-SEM and NCA in combination (Richter et al., 2020; Richter et al., 2023) allows researchers to determine the factors that produce the best possible outcome (i.e., the should-have factors; sufficiency logic) and those that are critical for an outcome (i.e., the must-have factors; necessity logic). Importantly, the should-have factors can only increase an outcome after the must-have factors have been taken care of. 

3    Course objectives and learning outcomes

This workshop will contrast the sufficiency and necessity logic and the foundations of a combined PLS-SEM and NCA use. Participants will comprehend the foundations of PLS-SEM, the NCA, and the combined use of both methods. For a case study illustration, we will use the SmartPLS 4 software (Ringle et al., 2022). We provide insights into the logic, assessment, challenges, and benefits of a combined use of PLS-SEM and NCA. More specifically, participants will comprehend the following topics:

•    When and how to use the NCA,

•    Fundamentals of PLS-SEM, 

•    The combined use of PLS-SEM and the NCA, 

•    Reporting and interpreting results, and

•    Combined PLS-SEM and NCA applications using the statistical software SmartPLS.

This course has been designed for researchers interested in state-of-the-art methods for their studies and publication projects. A basic knowledge of multivariate statistics and SEM techniques is helpful but not required.

4    Teaching and learning methods

•    The course is based on these textbooks: 

o    Dul, J. (2020). Conducting Necessary Condition Analysis. Thousand Oaks, CA: Sage. 

o    Dul, J. (2021). Advances in Necessary Condition Analysis, Version 0.1. Online book retrieved from: https://bookdown.org/ncabook/advanced_nca2/. 

o    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.

o    Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd edition. 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.

5    Registration and teaching resources

•    Conference participants can register for the workshop as part of the conference registration process. Please visit: https://2024gfmc.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

•    Comprehensive lecture slides will be provided to all participants

•    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 attending the workshop. Participants will receive detailed instructions – including a two-month SmartPLS 4 license key – shortly before the course starts.

6    Schedule 

Location: University of Milan-Biccoca, Milan, Italy

Time    Topic

09:00 – 10:30    Introduction to the necessary condition analysis (NCA) 

with a practical application example

10:30 – 11:00    Break

11:00 – 12:30    Foundations of partial least squares structural equation modeling (PLS-SEM) with practical application example

12:30 – 13:30    Lunch

13:30 – 15:00    The combined use of PLS-SEM and NCA with practical application example

7    References

Ahrholdt, D. C., Gudergan, S. P., & Ringle, C. M. (2019). Enhancing loyalty: When improving consumer satisfaction and delight matters. Journal of Business Research, 94(1), 18-27.

Becker, J.-M., Cheah, J. H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s Most Wanted Guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346. 

Bergh, D. D., Boyd, B. K., Byron, K., Gove, S., & Ketchen, D. J. (2022). What constitutes a methodological contribution? Journal of Management, 48(7), 1835-1848.

Bokrantz, J., & Dul, J. (2023). Building and testing necessity theories in supply chain management. Journal of Supply Chain Management, 59(1), 48-65.

Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and Methodology of "Necessary but not Sufficient" Causality. Organizational Research Methods, 19(1), 10-52. 

Dul, J. (2020). Conducting Necessary Condition Analysis. London: Sage.

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., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433.

Hauff, S., Guerci, M., Dul, J., & van Rhee, H. (2021). Exploring necessary conditions in HRM research: Fundamental issues and methodological implications. Human Resource Management Journal, 31(1), 18-36.

Hegner-Kakar, A.-K., Richter, N. F., & Ringle, C. M. (2018). The customer loyalty cascade and its impact on profitability in financial services. In N. K. Avkiran & C. M. Ringle (Eds.), Partial least squares structural equation modeling: recent advances in banking and finance (pp. 53-75). Cham (Switzerland): Springer.

Lin, C., & Lin, M. (2019). The determinants of using cloud supply chain adoption. Industrial Management & Data Systems, 119(2), 351-366.

Richter, N. F., & Hauff, S. (2022). Necessary conditions in international business research: advancing the field with a new perspective on causality and data analysis. Journal of World Business, 57, 101310.

Richter, N. F., Hauff, S., Ringle, C. M., Sarstedt, M., Kolev, A. E., & Schubring, S. (2023). How to Apply Necessary Condition Analysis in PLS-SEM. In H. Latan, J. J. F. Hair, & R. Noonan (Eds.), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (pp. 267-297). Springer International Publishing. 

Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020). When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243-2267.

Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlaegel, C. (2016). A critical look at the use of SEM in International Business research. International Marketing Review, 33(3), 376-404.

Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. Oststeinbek: SmartPLS. Retrieved from https://www.smartpls.com/

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022a). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035-1064.

Sarstedt, M., Hair, J. F., & Ringle, C. M. (2022b). "PLS-SEM: indeed a silver bullet" - retrospective observations and recent advances. Journal of Marketing Theory & Practice, Doi: 10.1080/10696679.2022.2056488.

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. 

Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under Indirect observations: Causality, structure, prediction: Part II (pp. 1-54). Amsterdam: North-Holland.

Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581-591). New York: Wiley.