Texas A&M International University

About: Texas A&M International University is a education organization based out in Laredo, Texas, United States. It is known for research contribution in the topics: Poison control & Population. The organization has 592 authors who have published 1428 publications receiving 39794 citations.

Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach

Abstract: The author discusses common method bias in the context of structural equation modeling employing the partial least squares method PLS-SEM Two datasets were created through a Monte Carlo simulation to illustrate the discussion: one contaminated by common method bias, and the other not contaminated A practical approach is presented for the identification of common method bias based on variance inflation factors generated via a full collinearity test The author's discussion builds on an illustrative model in the field of e-collaboration, with outputs generated by the software WarpPLS They demonstrate that the full collinearity test is successful in the identification of common method bias with a model that nevertheless passes standard convergent and discriminant validity assessment criteria based on a confirmation factor analysis

An effect size primer: A guide for clinicians and researchers.

Abstract: Increasing emphasis has been placed on the use of effect size reporting in the analysis of social science data. Nonetheless, the use of effect size reporting remains inconsistent, and interpretation of effect size estimates continues to be confused. Researchers are presented with numerous effect sizes estimate options, not all of which are appropriate for every research question. Clinicians also may have little guidance in the interpretation of effect sizes relevant for clinical practice. The current article provides a primer of effect size estimates for the social sciences. Common effect sizes estimates, their use, and interpretations are presented as a guide for researchers.

Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations

Abstract: Variance-based structural equation modeling is extensively used in information systems research, and many related findings may have been distorted by hidden collinearity. This is a problem that may extent to multivariate analyses in general, in the field of information systems as well as in many other fields. In multivariate analyses, collinearity is usually assessed as a predictor-predictor relationship phenomenon, where two or more predictors are checked for redundancy. This type of assessment addresses vertical, or “classic,” collinearity. However, another type of collinearity may also exist, called here “lateral” collinearity. It refers to predictor-criterion collinearity. Lateral collinearity problems are exemplified based on an illustrative variance-based structural equation modeling analysis. The analysis employs WarpPLS 2.0, with the results double-checked with other statistical analysis software tools. It is shown that standard validity and reliability tests do not properly capture lateral collinearity. A new approach for the assessment of both vertical and lateral collinearity in variance-based structural equation modeling is proposed and demonstrated in the context of the illustrative analysis.

Do Non–Audit Service Fees Impair Auditor Independence? Evidence from Going Concern Audit Opinions

Abstract: We find no significant association between non–audit service fees and impaired auditor independence, where auditor independence is surrogated by auditors’ propensity to issue going concern audit opinions. We also find no association between going concern opinions and either total fees or audit fees. In addition, our findings are robust to controlling for unexpected fees, to controlling for endogeneity among our variables, and to several alternative research design specifications. Our results are consistent with market–based incentives, such as loss of reputation and litigation costs, dominating the expected benefits from compromising auditor independence.

Principles of canonical action research

Abstract: Despite the growing prominence of canonical action research (CAR) in the information systems discipline, a paucity of methodological guidance contin- ues to hamper those conducting and evaluating such studies. This article elicits a set of five principles and associated criteria to help assure both the rigor and the relevance of CAR in information systems. The first principle relates to the devel- opment of an agreement that facilitates collaboration between the action researcher and the client. The second principle is based upon a cyclical process model for action research that consists of five stages: diagnosis, planning, inter- vention, evaluation and reflection. Additional principles highlight the critical roles of theory, change through action, and the specification of learning in terms of impli- cations for both research and practice. The five principles are illustrated through the analysis of one recently published CAR study.