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Understanding and predicting the behaviour of decision makers when choosing among discrete goods has been one of the most fruitful areas of applied research over the last thirty years. An understanding of individual consumer behaviour can lead to significant changes in product or service design, pricing strategy, distribution channel and communication strategy selection, as well as public welfare analysis. This graduate and practitioner guide, first published in 2000, deals with the study and prediction of consumer choice behaviour, concentrating on stated preference (SP) methods - placing decision makers in controlled experiments that yield hypothetical choices - rather than revealed preferences (RP) - actual choices in the market. It shows how SP methods can be implemented, from experimental design to econometric modelling, and suggests how to combine RP and SP data to get the best from each type. The book also presents an update of econometric approaches to choice modelling.

A multidisciplinary graduate and practitioner guide, first published in 2000, which offers the theory and application of stated choice methods.

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CONTENTS

List of figures = ⅸ

List of tables = xi

Acknowledgements = xv

1 Choosing as a way of life = 1

 1.1 Introduction = 1

 1.2 Decision making and choice behaviour = 2

 1.3 Conceptual framework = 8

 1.4 The world of choice is complex : the challenge ahead = 10

 Appendix A1 Choosing a residential telecommunications bundle = 19

2 Introduction to stated preference models and methods = 20

 2.1 Introduction = 20

 2.2 Preference data come in many forms = 20

 2.3 Preference data consistent with RUT = 25

3 Choosing a choice model = 34

 3.1 Introduction = 34

 3.2 Setting out the underlying behavioural decision framework = 35

 3.3 Random utility maximisation = 37

 3.4 The basic choice model - a particular model formulation = 44

 3.5 Statistical estimation procedure = 47

 3.6 Model outputs = 51

 3.7 Behavioural outputs of choice models = 57

 3.8 A simple illustration of the basic model = 62

 3.9 Linking to the later chapters = 65

 Appendix A3 Maximum likelihood estimation technique = 66

 Appendix B3 Linear probability and generalised least squares models = 72

4 Experimental design = 83

 4.1 Introduction = 83

 4.2 Factorial designs = 84

 4.3 Fractional factorial designs = 89

 4.4 Practical considerations in fractional designs = 94

 4.5 Design strategies for simple SP experiments = 96

5 Design of choice experiments = 111

 5.1 Introduction = 111

 5.2 Multiple choice experiments = 112

 5.3 General design principles for choice experiments = 119

 5.4 Availability designs for labelled alternatives = 126

 Appendix A5 Some popular choice designs = 131

6 Relaxing the IID assumption - introducing variants of the MNL model = 138

 6.1 Setting the context for behaviourally more plausible models = 138

 6.2 Deriving the mean and variance of the extreme value type 1 distribution = 142

 6.3 Introduction to the nested logit model = 144

 6.4 Empirical illustration = 154

 6.5 The nested logit model - empirical examples = 162

 6.6 Tests of overall model performance for nested models = 176

 6.7 Conclusions and linkages between the MNL／NL models and more complex models = 182

 Appendix A6 Detailed characterisation of the nested logit model = 183

 Appendix B6 Advanced discrete choice methods = 189

7 Complex, non-IID multiple choice designs = 213

 7.1 Introduction = 213

 7.2 Designs for alternatives with non-constant error variances = 214

 7.3 Designs for portfolio, bundle or menu choices = 215

 7.4 Summary = 226

8 Combining sources of preference data = 227

 8.1 Appreciating the opportunity = 227

 8.2 Characteristics of RP and SP data = 228

 8.3 The mechanics of data enrichment = 233

 8.4 Is it always possible to combine preference data sources? = 243

 8.5 A general preference data generation process = 248

 8.6 Summary = 251

9 Implementing SP choice behaviour projects = 252

 9.1 Introduction = 252

 9.2 Components of the choice process = 252

 9.3 The steps in an SP choice study = 255

 9.4 Summary = 282

10 Marketing case studies = 283

 10.1 Introduction = 283

 10.2 Case study 1 : preference heterogeneity vs. variance heteroscedasticity = 283

 10.3 Case study 2 : choice set generation analysis = 292

 10.4 Summary = 297

11 Transportation case studies = 298

 11.1 Introduction = 298

 11.2 Case study 1 : introducing a new alternative : high speed rail and the random effects HEV model in an SP-RP context = 299

 11.3 Case study 2 : high speed rail and random effects HEV in a switching context = 301

 11.4 Case study 3 : valuation of travel time savings and urban route choice with tolled options in an SP context = 306

 11.5 Case study 4 : establishing a fare elasticity regime for urban passenger transport : non-concession commuters with SP-RP and HEV = 315

 11.6 Conclusions to chapter = 328

12 Environmental valuation case studies = 329

 12.1 Introduction = 329

 12.2 Environmental valuation : theory and practice = 329

 12.3 Case study 1 : use values - recreational hunting site choices = 331

 12.4 Case study 2 : passive use values = 343

 12.5 The passive use value controversy : can SP help? = 350

 12.6 Conclusions = 352

13 Cross validity and external validity of SP models = 354

 13.1 Introduction = 354

 13.2 A brief review of preference model comparisons = 356

 13.3 Preference regularities = 357

 13.4 Procedures for testing preference regularity = 363

 13.5 Empirical case studies and results = 369

 13.6 Summary and conclusions = 379

References = 382

Index = 399