Journal ArticleDOI
On the evaluation of structural equation models
Richard P. Bagozzi,Youjae Yi +1 more
TLDR
In this article, structural equation models with latent variables are defined, critiqued, and illustrated, and an overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence.Abstract:
Criteria for evaluating structural equation models with latent variables are defined, critiqued, and illustrated. An overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence. Model assessment is considered to be a complex process mixing statistical criteria with philosophical, historical, and theoretical elements. Inevitably the process entails some attempt at a reconcilation between so-called objective and subjective norms.read more
Citations
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Journal ArticleDOI
Specification, evaluation, and interpretation of structural equation models
Richard P. Bagozzi,Youjae Yi +1 more
TL;DR: This compendium of standards for the use and interpretation of structural equation models (SEMs) removes some of the mystery and uncertainty of the use of SEMs, while conveying the spirit of their possibilities.
Journal ArticleDOI
Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research
TL;DR: This comprehensive and systematic analysis offers initial evidence that the marker-variable technique can serve as a convenient, yet effective, tool for accounting for CMV, and common method biases in the IS domain are not as serious as those found in other disciplines.
Journal ArticleDOI
Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration
TL;DR: In this paper, a comparison between scale development and index construction procedures is made to trace the implications of adopting a reflective versus formative perspective when creating multi-item measures for organizational research.
Journal ArticleDOI
What drives mobile commerce? An empirical evaluation of the revised technology acceptance model
Jen-Her Wu,Shu-Ching Wang +1 more
TL;DR: This study presents an extended technology acceptance model (TAM) that integrates innovation diffusion theory, perceived risk and cost into the TAM to investigate what determines user mobile commerce (MC) acceptance.
References
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Journal ArticleDOI
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Claes Fornell,David F. Larcker +1 more
TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Significance tests and goodness of fit in the analysis of covariance structures
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
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Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
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