Additional Information
Book Details
Abstract
Marketing Research: An International Approach is a comprehensive text written with the decision-maker in mind. It is written from the perspective of the firm conducting marketing research in the national and international markets irrespective of its country of origin. This tools-oriented book shows how international marketing managers can transform existing (Secondary) and newly collected (primary) data into useful information.
This is a comprehensive and advanced marketing research book that offers an analytical and decision-oriented framework of the subject. This book looks at firms conducting market research in the national and international markets irrespective of its country of origin.
This book is written for advanced undergraduate and graduate students studying Marketing Research. It is also appropriate for practitioners who wish to keep abreast of the most recent developments in the field.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Marketing Research: An International Approach | i | ||
| Contents | v | ||
| Foreword | ix | ||
| Preface | xi | ||
| Acknowledgements | xiv | ||
| About the authors | xv | ||
| Publisher’s acknowledgements | xvi | ||
| Globalization of markets and international marketing research | 1 | ||
| Learning objectives | 1 | ||
| Introduction | 1 | ||
| Changes in the global environment | 6 | ||
| Consequences of “de-globalization” | 8 | ||
| The changing role of the international marketresearcher | 10 | ||
| Summary | 13 | ||
| Questions | 13 | ||
| References | 14 | ||
| End notes | 14 | ||
| Online and other secondary datasources | 15 | ||
| Learning objectives | 15 | ||
| Introduction | 15 | ||
| Advantages and disadvantages of secondarydata | 16 | ||
| Evaluating secondary data | 17 | ||
| Sources of internal secondary data | 19 | ||
| Sources of external secondary data | 25 | ||
| Competitive intelligence (CI) | 27 | ||
| Special problems and methods associated withsecondary data | 33 | ||
| Collecting international secondary data acrossthe internet | 36 | ||
| Summary | 42 | ||
| Questions | 43 | ||
| References | 43 | ||
| Observational and trackingmethods | 45 | ||
| Learning objectives | 45 | ||
| Introduction | 45 | ||
| Criteria for using observation | 46 | ||
| Classification of observation methods | 47 | ||
| Mechanical observation methods | 51 | ||
| Human observation: mystery shopping | 54 | ||
| Advantages and limitations of observational research | 58 | ||
| Summary | 59 | ||
| Questions | 60 | ||
| References | 61 | ||
| Focus groups | 63 | ||
| Learning objectives | 63 | ||
| Introduction | 63 | ||
| Framework for focus groups | 65 | ||
| Types of focus groups | 72 | ||
| Advantages and disadvantages of focus groups | 74 | ||
| Online focus groups | 76 | ||
| Quantifying text: methodological challengesand available software | 80 | ||
| Summary | 85 | ||
| Questions | 87 | ||
| References | 87 | ||
| Other qualitative research methods | 89 | ||
| Learning objectives | 89 | ||
| Introduction | 89 | ||
| Individual, in-depth interviews | 89 | ||
| Protocol analysis | 93 | ||
| Projective methods | 94 | ||
| Grounded theory | 98 | ||
| Action research | 102 | ||
| The Delphi method | 103 | ||
| Scenario planning | 106 | ||
| Summary | 109 | ||
| Questions | 109 | ||
| References | 110 | ||
| Measurement and scaling | 113 | ||
| Learning objectives | 113 | ||
| Introduction | 113 | ||
| The measurement process | 114 | ||
| Basic scales of measurement | 116 | ||
| Measuring attitudes | 118 | ||
| Reliability and validity in measurement | 126 | ||
| Choice of scales in cross-national research | 132 | ||
| Summary | 133 | ||
| Questions | 134 | ||
| References | 135 | ||
| End notes | 135 | ||
| Survey and questionnaire design | 137 | ||
| Learning objectives | 137 | ||
| Introduction | 137 | ||
| Survey design | 138 | ||
| Questionnaire design | 147 | ||
| Sampling | 159 | ||
| Summary | 170 | ||
| Questions | 171 | ||
| References | 173 | ||
| End note | 174 | ||
| Quantitative models in marketing | 175 | ||
| Learning objectives | 175 | ||
| Properties of a good model | 175 | ||
| Different categories of models | 177 | ||
| Building models | 189 | ||
| Summary | 190 | ||
| Questions | 191 | ||
| References | 192 | ||
| End note | 193 | ||
| Analysis of variance and multiple regression | 195 | ||
| Learning objectives | 195 | ||
| Introduction | 195 | ||
| Variance as a tool for analyzing sales | 196 | ||
| Using multiple regression for analyzing exports | 218 | ||
| Summary | 226 | ||
| Questions | 228 | ||
| References | 229 | ||
| End notes | 230 | ||
| Discriminant analysis and logisticregression | 233 | ||
| Learning objectives | 233 | ||
| Discriminant analysis | 233 | ||
| Logit choice models: when discriminant analysis(and ordinary regression) does not apply | 246 | ||
| Summary | 261 | ||
| Questions | 262 | ||
| References | 263 | ||
| End notes | 264 | ||
| Profiling customers: factor analysis | 269 | ||
| Learning objectives | 269 | ||
| Introduction: common aspects of factor and cluster analysis | 269 | ||
| Introductory aspects of factor analysis | 272 | ||
| Numerical example of factor analysis | 283 | ||
| Running the example in SPSS | 290 | ||
| Rotation of factors | 294 | ||
| Additional issues in factor analysis | 301 | ||
| Summary | 320 | ||
| Questions | 322 | ||
| References | 322 | ||
| End notes | 324 | ||
| Cluster analysis and segmentation of customers | 327 | ||
| Learning objectives | 327 | ||
| Using factor analysis for clustering observations | 327 | ||
| Traditional clustering of subjects or cases(respondents) | 330 | ||
| Hierarchical cluster analysis: an example | 334 | ||
| Non-hierarchical cluster analysis: an example | 345 | ||
| How many clusters should be used? | 354 | ||
| An empirical example of k-means cluster:segmenting readers of sales flyers | 361 | ||
| Summary | 368 | ||
| Questions | 369 | ||
| References | 374 | ||
| End notes | 375 | ||
| Positioning the product: MDS | 377 | ||
| Learning objectives | 377 | ||
| The importance of product positioning | 377 | ||
| MDS – an introduction | 380 | ||
| Visual representation of cross-tabs:correspondence analysis | 402 | ||
| Summary | 408 | ||
| Questions | 410 | ||
| References | 412 | ||
| End notes | 414 | ||
| Systematic product development: conjoint analysis | 417 | ||
| Learning objectives | 417 | ||
| Introduction | 417 | ||
| Example: conjoint analysis for a red wine | 426 | ||
| Conjoint analysis for a bank account | 438 | ||
| A final note on conjoint analysis | 461 | ||
| Summary | 461 | ||
| Questions | 462 | ||
| References | 466 | ||
| End notes | 466 | ||
| Advanced methods for categorization: CHAID and latent class analysis | 469 | ||
| Learning objectives | 469 | ||
| Introduction | 469 | ||
| CHAID | 471 | ||
| Latent class (LC) models | 484 | ||
| Summary | 494 | ||
| Questions | 495 | ||
| References | 497 | ||
| Several dependent variables: canonical correlation and structural equation modelling | 501 | ||
| Learning objectives | 501 | ||
| Canonical correlation | 502 | ||
| Introduction to structural equation modelling(SEM) | 511 | ||
| The measurement model | 512 | ||
| The path model | 529 | ||
| Summary | 537 | ||
| Questions | 540 | ||
| References | 542 | ||
| End notes | 544 | ||
| Data mining | 545 | ||
| Learning objectives | 545 | ||
| Introduction | 545 | ||
| Data mining with SPSS Clementine | 550 | ||
| Selected applications of data mining: rulemodelling and neural networks | 568 | ||
| Summary | 577 | ||
| Questions | 578 | ||
| Bibliography and references | 579 | ||
| End notes | 580 | ||
| Putting it all together: an international marketing information system | 581 | ||
| Learning objectives | 581 | ||
| Introduction | 581 | ||
| Analyzing analytic capabilities: four questions | 582 | ||
| Building an international MIS | 587 | ||
| Summary | 596 | ||
| Questions | 596 | ||
| Bibliography and references | 596 | ||
| End note | 597 | ||
| Index | 599 |