This article discusses lateral collinearity in variance-based structural equation modeling (SEM), which can lead to misleading results. Lateral collinearity occurs when predictor and criterion variables measure the same construct, often going undetected by standard validity and reliability tests. The authors illustrate this issue using an SEM analysis of electronic communication in innovation teams, showing that standard tests fail to capture lateral collinearity. They propose a new approach for assessing both vertical and lateral collinearity in variance-based SEM. The study highlights the importance of full collinearity tests in empirical research, as they can detect collinearity that standard tests miss. The authors recommend conducting full collinearity tests alongside validity and reliability tests to ensure accurate results. The paper emphasizes the need for researchers to be aware of lateral collinearity and its potential to distort findings in multivariate analyses. The study also discusses the implications of lateral collinearity for information systems research and suggests solutions such as combining or removing redundant latent variables to address the issue. The authors conclude that full collinearity tests are essential for accurate SEM analysis and that researchers should prioritize them in future studies.This article discusses lateral collinearity in variance-based structural equation modeling (SEM), which can lead to misleading results. Lateral collinearity occurs when predictor and criterion variables measure the same construct, often going undetected by standard validity and reliability tests. The authors illustrate this issue using an SEM analysis of electronic communication in innovation teams, showing that standard tests fail to capture lateral collinearity. They propose a new approach for assessing both vertical and lateral collinearity in variance-based SEM. The study highlights the importance of full collinearity tests in empirical research, as they can detect collinearity that standard tests miss. The authors recommend conducting full collinearity tests alongside validity and reliability tests to ensure accurate results. The paper emphasizes the need for researchers to be aware of lateral collinearity and its potential to distort findings in multivariate analyses. The study also discusses the implications of lateral collinearity for information systems research and suggests solutions such as combining or removing redundant latent variables to address the issue. The authors conclude that full collinearity tests are essential for accurate SEM analysis and that researchers should prioritize them in future studies.