PLS Applications Symposium; 15-17 April 2026; Laredo, Texas

PLS Applications Symposium; 15-17 April 2026; Laredo, Texas
(Abstract submissions accepted until 6 February 2026)

*** Attendance (face-to-face or online) ***

The Symposium will be conducted as part of the multidisciplinary Annual Western Hemispheric Trade Conference, organized by the Center for the Study of Western Hemispheric Trade. Our workshop in PLS-SEM will be conducted entirely online. Our expectation is that participants will be allowed to attend Conference sessions either face-to-face or online.

When indicating the type of their submission, participants should indicate whether they intend to attend face-to-face or online. This should be done within parentheses after indicating the submission type. For example - "Type of submission: Presentation (online)".

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

https://plsas.net

*** Workshop on PLS-SEM ***

On 15 April 2026 a full-day workshop on PLS-SEM will be conducted online by Dr. Ned Kock and Dr. Geoffrey Hubona, using the software WarpPLS. Dr. Kock is the original developer of this software, which is one of the leading PLS-SEM tools today; used by thousands of researchers from a wide variety of disciplines, and from many different countries. Dr. Hubona has extensive experience conducting research and teaching topics related to PLS-SEM, using WarpPLS and a variety of other tools. This workshop will be hands-on and interactive, and will have two parts: (a) basic PLS-SEM issues, conducted in the morning (9 am - 12 noon) by Dr. Hubona; and (b) intermediate and advanced PLS-SEM issues, conducted in the afternoon (2 pm - 5 pm) by Dr. Kock. Participants may attend either one, or both of the two parts.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Viewing Nonlinear Relationships - Solving Collinearity Problems - Conducting a Factor-Based PLS-SEM Analysis - Using Consistent PLS Factor-Based Algorithms - Exploring Statistical Power and Minimum Sample Sizes - Exploring Conditional Probabilistic Queries - Exploring Full Latent Growth - Conducting Multi-Group Analyses - Assessing Measurement Invariance - Creating Analytic Composites.

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Ned Kock
Symposium Chair
https://plsas.net

Statistical significance and effect size tests in SEM: Common method bias and strong theorizing

The article below provides evidence in support of a few very important methodological propositions: (a) we should not do away with classic statistical significance tests, but should combine them with effect size tests, and tests of common method bias; (b) high quality theorizing is very important if we are to profitably use a combination of classic statistical significance, effect size, and common method bias tests; and (c) the full collinearity VIF threshold in common method bias assessment for factor-based PLSF-SEM should be 10, as opposed to the 3.3 number used with classic composite-based PLS algorithms.

Kock, N., & Dow, K. E. (2025). Statistical significance and effect size tests in SEM: Common method bias and strong theorizing. Advances in Management Accounting, 37(1), 95–105.

Link to full-text file for this article:

Statistical significance and effect size tests in SEM: Common method bias and strong theorizing.

Abstract:

We generally acknowledge the problematic nature of classic statistical significance tests based on P-values or confidence intervals. In fact, we demonstrate based on an illustrative model for which we created simulated data, that with low and high statistical power, path coefficients in structural equation modeling whose true values are zero, routinely end up being found to be significantly different from zero at the P < .05 level. However, we argue that we should not do away with classic statistical significance tests, and that these tests can be useful but should be complemented by other methodological tools, including effect size tests, and tests of common method bias. We also argue that high quality theorizing is very important if we are to profitably use a combination of classic statistical significance, effect size, and common method bias tests.

Important note for PLSF-SEM users (repeated below for emphasis):

The full collinearity VIF threshold in common method bias assessment for factor-based PLSF-SEM should be 10, as opposed to the 3.3 number used with classic composite-based PLS algorithms.

Best regards to all!

Do employees care about diversity and inclusion? Why academic research should not be politically biased

The article below suggests, among other things, that the degree to which a company promotes diversity and inclusion has a negligible effect on how an employee rates the company (which reflects job satisfaction). This is in fact not the main theme of the article, but it is something that received plenty of pushback from reviewers. The analysis was conducted a while ago, when research results that did not strongly support diversity and inclusion were typically viewed rather unfavorably by review panels in many academic journals. We thank the prestigious journal Personnel Review for their academic integrity.

Kock, N., Haddoud, M.Y., Onjewu, A.-K., & Yang, S. (2025). Unveiling workplace dynamics: Insights from voluntary disclosures on business outlook and CEO approval. Personnel Review, 54(2), 474–497.

Links to full-text versions of the article:

https://scriptwarp.com/pubs/Kock_etal_2025_PR_WorkplaceDynamics.pdf

https://pure-oai.bham.ac.uk/ws/portalfiles/portal/253297641/KockN2025Unveiling_AAM.pdf

https://www.emerald.com/insight/content/doi/10.1108/pr-03-2024-0251/full/html

Abstract:

Purpose: This inquiry extends the discourse on job satisfaction and employee referral. It aims to examine the moderating effects of perceived business outlook and CEO approval in the dynamics of job satisfaction and employee referral. A model predicting job satisfaction and employee referral through the lens of Herzberg’s two-factor theory is developed and tested. Design/methodology/approach: To remedy the overreliance on self-reported surveys, impeding generalization and representativeness, this study uses large evidence from 14,840 voluntary disclosures of US employees. A structural equation modeling technique is adopted to test the hypotheses. Findings: The inherent robust path analysis revealed intriguing findings highlighting culture and values as exerting the most substantial positive impact on job satisfaction, while diversity and inclusion played a relatively trivial role. Moreover, employees’ view of the firms’ outlook and their approval of the incumbent CEO were found to strengthen the job satisfaction–referral nexus. Originality/value: The study revisits the relationship between job satisfaction and employee referral by capturing the moderating effects of perceived business outlook and CEO approval. We believe that this investigation is one of the first to capture the impact of these two pivotal factors.

The figure below summarizes the results of the study. The overall rating variable reflects satisfaction with one’s job at a particular company, which predictably influences the probability that a person will recommend the company to a friend as a potential employer. If we had relied only on statistical significance tests, the effect of diversity and inclusion on job satisfaction would actually be negative and statistically significant. But based on the small effect size, we felt that it would be more scholarly to report the effect in question as indistinguishable from zero. With large samples, the likelihood of type I errors (false positives) increases dramatically in statistical significance tests, whether P values or confidence intervals are used.

Shiyu got us the awesome Glassdoor dataset, while Yacine and Adah-Kole did most of the theory development and later discussion work (thank you, my talented co-authors). The curious thing is that I did the analyses for this article, using WarpPLS and double-checking with other analysis tools, and was not only surprised but rather displeased with the results. But why was I displeased with the results? Well, as an academic, I work in a very diverse environment, and find that diversity stimulating. In particular, I am very interested about countries and regions (domestically and abroad), their cultures, and histories. Furthermore, as someone with a diverse background, I have lived in Brazil and New Zealand, before settling in the US. While in the US, Belgium was like a second home for several years, as I travelled there often to consult for the European Commission.

Yet, regardless of personal background, and for the sake of societal credibility, academics must report research results as they are, to the best of their ability. Furthermore, they must report research results independently from political orientation and how they personally feel about those results. Finally, they have to resign themselves to the fact that all empirical studies provide incomplete views of the world, and usually call for more research using different approaches and epistemologies.

Best regards to all!

PS: I thank Nadya Larumbe for her comments on a previous version of this post.