Quantitative modelling in marketing and management / [edited] by Luiz Moutinho & Kun-Huang Huarng.

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ReferencesChapter 2. Role of Structural Equation Modelling in Theory Testing and Development; 1. Introduction; 1.1. Structural equation modelling; 1.2. Terminology, rules, and conventions; 2. Structural Equation Modelling-Example; 2.1. Model identification; 2.2. Goodness-of-fit; 2.3. Model fit summary for the current example; 3. Model Estimation, Modification, and Interpretation; APPENDIX; References; Chapter 3. Partial Least Squares Path Modelling in Marketing and Management Research: An Annotated Application; 1. Introduction; 2. The PLSPM Algorithm.

Preface; Introduction; Part 1. Statistical Modelling; Chapter 1. A Review of the Major Multidimensional Scaling Models for the Analysis of Preference/Dominance Data in Marketing; 1. Introduction; 2. The Vector MDS Model; 2.1. The individual level vector MDS model; 2.2. The segment level or clusterwise vector MDS model; 3. The Unfolding MDS Model; 3.1. The individual level simple unfolding model; 3.2. The segment level or clusterwise multidimensional unfolding model; 4. A Marketing Application; 4.1. The vector model results; 4.2. The simple unfolding model results; 5. Discussion.

3. PLSPM Properties: Strengths andWeaknesses4. Applied Example: The Role of Trust on Consumers Adoption of Online Banking; 4.1. The model; 4.2. Method; 4.3. Estimating a PLSPM. Step 1. Dealing with second order factors; 4.4. Estimating a PLSPM. Step 2. Validating the measurement (outer) model; 4.4.1. Reliability; 4.4.2. Convergent validity; 4.4.3. Discriminant validity; 4.5. Estimating a PLSPM. Step 3. Assessing the structural (inner) model; 4.5.1. R2 of dependent LV; 4.5.2. Predictive relevance; 4.6. Estimating a PLSPM. Step 4. Hypotheses testing; 5. Conclusion; References.

Chapter 4. Statistical Model Selection1. Introduction; 2. Some Example Analyses; 2.1. Tourism in Portugal; 2.2. Union membership; 3. Problem 1: Including Non-Important Variables in the Model; 3.1. Simulating data; 3.2. Models derived from simulated data; 4. Problem 2: Not Including Important Variables in the Model; 4.1. Modelling fuel consumption; 5. Conclusion; References; Part 2. Computer Modelling; Chapter 5. Artificial Neural Networks and Structural Equation Modelling: An Empirical Comparison to Evaluate Business Customer Loyalty; 1. Introduction; 2. Literature Review; 2.1. Loyalty.

2.2. Loyalty determinants3. Research Method; 3.1. ANNs; 3.2. Structural equation modelling; 4. Comparisons; 4.1. Latent variables; 4.2. Causal interactions; 4.3. Learned associative properties; 4.4. Interconnectivity-neurons and indicators; 4.5. Predictability; 5. Results; 5.1. Results from the SEM; 5.2. Results from ANN; 6. Comparing Modelling Performance; 7. Comparing Results; 8. Conclusion; References; Chapter 6. The Application of NN to Management Problems; 1. Artificial Neural Networks in the Management Field; 2. Why use ANNs?; 3. ANNs; 3.1. Architecture of NNs; 3.2. Learning algorithms.

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