Journal
DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS
Volume 44, Issue 4, Pages 11-43Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2544415.2544417
Keywords
partial least squares; composite reliability; Monte Carlo simulation
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The accurate estimation of reliability is of great importance to the conduct and interpretation of empirical research as it is used to judge the quality of reported research, often plays a role in publication decisions, and is a key element of meta-analytic reviews. When employing partial least squares (PLS) as the method of analysis, the reliability of the composites involved in the model is typically the parameter examined. In this research, we describe the existence of three important issues concerning the accuracy of composite reliability estimation in PLS analysis: the assumption of equal indicator weights, the bias in loading estimates, and the lack of independence between indicator loadings and weights. We subsequently present an alternative approach to correct these issues. Using a Monte Carlo simulation we provide a demonstration of both the effects of these issues on research decisions and the improved accuracy of the alternative method.
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