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Quantitative modelling in marketing and management
להגדלת הטקסט להקטנת הטקסט- ספר
"The field of marketing and management has undergone immense changes over the past decade. These dynamic changes are driving an increasing need for data analysis using quantitative modelling. Problem solving using the quantitative approach and other models has always been a hot topic in the fields of marketing and management. Quantitative modelling seems admirably suited to help managers in their strategic decision making on operations management issues. In social sciences, quantitative research refers to the systematic empirical investigation of social phenomena via statistical, mathematical or computational techniques. The first edition of "Quantitative Modelling in Marketing and Management" focused on the description and applications of many quantitative modelling approaches applied to marketing and management. The topics ranged from fuzzy logic and logical discriminant models to growth models and k-clique models. The second edition follows the thread of the first one by covering a myriad of techniques and applications in the areas of statistical, computer, mathematical as well as other novel nomothetic methods. It greatly reinforces the areas of computer, mathematical and other modeling tools that are designed to bring a level of awareness and knowledge among academics and researchers in marketing and management, so that there is an increase in the application of these new approaches that will be embedded in future scholarly output."-- Provided by publisher.
| כותר |
Quantitative modelling in marketing and management / edited by Luiz Moutinho & Kun-Huang Huarng. |
|---|---|
| מהדורה |
Second edition. |
| מוציא לאור |
New Jersey : World Scientific |
| שנה |
[2016] |
| הערות |
Description based upon print version of record. Includes bibliographical references. English |
| הערת תוכן ותקציר |
CONTENTS 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 ReferencesChapter 2. Role of Structural Equation Modelling in Theory Testing and Development 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 2. The PLSPM Algorithm 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 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 Part 2. Computer Modelling Chapter 5. Artificial Neural Networks and Structural Equation Modelling: An Empirical Comparison to Evaluate Business Customer Loyalty 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 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 3.3. MFF networks |
| היקף החומר |
1 online resource (569 p.) |
| שפה |
אנגלית |
| שנת זכויות יוצרים |
©2016 |
| מספר מערכת |
997010719668905171 |
תצוגת MARC
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