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On the evaluation of structural equation models

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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.

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Understanding the relationships between internal resources and capabilities, sustainable supply management and organizational sustainability†

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Implementing Marketing Strategies: Developing and Testing a Managerial Theory

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The employee-organization relationship, organizational citizenship behaviors, and superior service quality

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Information asymmetry and levels of agency relationships

TL;DR: In this article, the authors describe a marketing exchange characterized by an information asymmetry between suppliers and customers, where customers are faced with both adverse selection and moral hazard problems that cause them to make poor decisions.
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Feeling bad: antecedents and consequences of negative emotions in ongoing change

TL;DR: In this paper, the authors examined how and why ongoing organizational change is experienced emotionally on an everyday basis and identified important consequences of such emotional experiences, such as negative emotions, trust in the organization and withdrawal from the organization.
References
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Journal ArticleDOI

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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...
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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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