DISCOVERING UNOBSERVED HETEROGENEITY IN STRUCTURAL EQUATION MODELS TO AVERT VALIDITY THREATS

• 成果类型: Article
• 署名作者: Becker, Jan-Michael; Rai, Arun; Ringle, Christian M.; Voelckner, Franziska
• 署名单位: University of Cologne; University System of Georgia; Georgia State University; University System of Georgia; Georgia State University; Hamburg University of Technology; University of Newcastle
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2013/37.3.01
• 发表日期: 2013
• 页码: 665-+
• 关键词: TECHNOLOGY ACCEPTANCE MODEL; response-based segmentation; information-systems; finite-mixture; Measurement invariance; formative measurement; TASK INTERDEPENDENCE; MULTIPLE-REGRESSION; bayesian-analysis; measurement error

摘要

A large proportion of information systems research is concerned with developing and testing models pertaining to complex cognition, behaviors, and outcomes of individuals, teams, organizations, and other social systems that are involved in the development, implementation, and utilization of information technology. Given the complexity of these social and behavioral phenomena, heterogeneity is likely to exist in the samples used in IS studies. While researchers now routinely address observed heterogeneity by introducing moderators, a priori groupings, and contextual factors in their research models, they have not examined how unobserved heterogeneity may affect their findings. We describe why unobserved heterogeneity threatens different types of validity and use simulations to demonstrate that unobserved heterogeneity biases parameter estimates, thereby leading to Type I and Type II errors. We also review different methods that can be used to uncover unobserved heterogeneity in structural equation models. While methods to uncover unobserved heterogeneity in covariance-based structural equation models (CB-SEM) are relatively advanced, the methods for partial least squares (PLS) path models are limited and have relied on an extension of mixture regression-finite mixture partial least squares (FIMIX-PLS) and distance measure-based methods-that have mismatches with some characteristics of PLS path modeling. We propose a new method-prediction-oriented segmentation (PLS-POS)-to overcome the limitations of FIMIX-PLS and other distance measure-based methods and conduct extensive simulations to evaluate the ability of PLS-POS and FIMIX-PLS to discover unobserved heterogeneity in both structural and measurement models. Our results show that both PLS-POS and FIMIX-PLS perform