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On the evaluation of structural equation models
Richard P. Bagozzi,Youjae Yi +1 more
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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.read more
Citations
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Use of Structural Equation Modeling in Tourism Research Past, Present, and Future
TL;DR: Although SEM practices have improved in some areas, tourism researchers do not always engage in the recommended best practices, and suggestions to improve use of SEM in tourism studies are discussed.
Journal ArticleDOI
Cognitive, affective, conative, and action loyalty: Testing the impact of inertia
TL;DR: In this article, the authors proposed and tested an extended Oliver's (1997) four-stage loyalty model by employing a multi-dimensional approach for each attitudinal loyalty stage while considering the moderating impact of inertia.
Journal ArticleDOI
Redefining market orientation from a relationship perspective: Theoretical considerations and empirical results
TL;DR: In this paper, the authors explored the notion of market orientation with particular focus on interorganizational relationships and argued that the relationships are important and that the overall market orientation of firms needs to be translated to a relationship level in order to be effective.
Journal ArticleDOI
An Empirical Analysis of the Antecedents and Performance Consequences of Using the Moodle Platform
TL;DR: This study uses the Moodle system to make up for the shortcomings of learning in a large class, and find out the difference of learning performance and acceptance between traditional learning and digital learning.
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Building trust in m‐commerce: contributions from quality and satisfaction
Yung Shao Yeh,Yung‐Ming Li +1 more
TL;DR: The results showed that despite customisation, brand image and satisfaction all directly affecting customer trust towards the vendor in m‐commerce, customisation and brand image...
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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