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Quantitative modelling in marketing and management [electronic resource]
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
| כותר |
Quantitative modelling in marketing and management [electronic resource] / by Luiz Moutinho, Kun-Huang Huarng. |
|---|---|
| מוציא לאור |
Singapore Hackensack, N.J. : World Scientific |
| שנה |
c2013 |
| הערות |
Description based upon print version of record. Includes bibliographical references and index. English |
| הערת תוכן ותקציר |
CONTENTS Preface Introduction Part 1. Statistical Modelling Part 2. Computer Modelling Part 3. Mathematical and Other Models References Chapter 1. A Review of the Major Multidimensional Scaling Models for the Analysis of Preference/Dominance Data in Marketing Wayne S. DeSarbo and Sunghoon Kim 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 Chapter 2. Role of Structural Equation Modelling in Theory Testing and Development Parikshat S. Manhas, Ajay K. Manrai, Lalita A. Manrai and Ramjit 1.1. Structural equation modelling 1.2. Terminology, rules, and conventions 2. Structural Equation Modeling - Example 2.1. Model identification Model specification 2.2. Goodness-of-fit 2.3. Model fit summary for the current example 3. Model Estimation, Modification and Interpretation APPENDIX Steps To Launch Amos Graphics Chapter 3. Partial Least Squares Path Modelling in Marketing and Management Research: An Annotated Application Joaquín Aldás-Manzano 2. The PLSPM Algorithm 3. PLSPM Properties: Strengths and Weaknesses 4. 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. DEA- Data Envelopment Analysis: Models, Methods and Applications Dr. Alex Manzoni and Professor Sardar M.N Islam 2. Basic DEA Model 3. Slack and Returns to Scale 4. Assumptions, Strengths and Limitations 5. Applications, Examples and Computation Programs 6. Conclusion Acknowledgement Chapter 5. Statistical Model Selection Graeme D Hutcheson 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 Chapter 6. Artificial Neural Networks and Structural Equation Modelling: An Empirical Comparison to Evaluate Business Customer Loyalty Arnaldo Coelho, Luiz Moutinho, Graeme D Hutcheson and Maria Manuela Santos Silva |
| היקף החומר |
1 online resource (530 p.) |
| שפה |
אנגלית |
| מספר מערכת |
997010713163905171 |
תצוגת MARC
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