Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

The aim of this thesis is to develop a greater understanding of consumers' brand repertoires. This research is part of the brand choice literature, which involves the analysis of all parts of the brand choice process. While there is clearly a need for research on the size of consumers' repertoites, little research has been conducted on this topic to date. This thesis provides much needed descriptive knowledge in relation to repertoire size, as well as providing information about the potential influence of a selection of consumer characteristics on this measure.

The Dirichlet Model has been fitted to purchase behaviour in many product categories. The model uses the Dirichlet multinomial distributions to account for heterogeneity between customers in brand choice. The research reported here applies the concept of heterogeneity, developed in the Dirichlet Model, to other areas in marketing. It works through a range of classic market research techniques showing the changes and improvements that result from the consideration of heterogeneity in brand choice. The analysis has implications for (1) sample size calculations (2) the estimation of variance and reliability for nominal variables, (3) the evaluation of logistic and multinomial logit models, (4) the method and design of research which uses discrete choice models, (5) the evaluation of the similarities and differences between product categories and (6) the analysis and measurement of purchase feedback effects. The work also examines methods for identifying if a set of data conforms to the Dirichlet distribution. The work develops a concept of heterogeneity for a nominal variable, which was always known but the implications not fully understood. Discrete choice is a random Bernoulli trial based on a probability. The thesis embodied in the work presented here is that: across the population there is not a single probability, but a probability variable. The probability distribution of this variable is known as the mixing distribution. Analysis should focus on the attributes of this probability variable, and in particular its heterogeneity, rather than on the specific discrete brand choice. If all choice is based on the one, single underlying probability then there is no heterogeneity in the probabilities; there is no mixing distribution. If there is no heterogeneity in the probabilities then an analysis of the discrete choice is an analysis of random data evolving from repeated Bernoulli trials. The Dirichlet and Dirichlet multinomial distributions provide a strong framework for the analysis of the probability variable. / thesis (PhDBusinessandManagement)--University of South Australia, 2000.

Thesis (M.App.Psy.)--University of Waikato, 2007. / Title from PDF cover (viewed March 19, 2008) Includes bibliographical references (p. 60-63)

Thesis (Ph. D.)--University of Adelaide, School of Agriculture and Wine, Discipline of Wine and Horticulture, 2003. / Includes list of Supplementary refereed publications relating to thesis; and of Refereed conference papers, as appendix 1. Includes bibliographical references (p. 316-343).

Thesis (Ph.D.)--Edith Cowan University, 2004. / Submitted to the Faculty of Business and Public Management. Includes bibliographical references.

Thesis (M. Bus. )--Edith Cowan University, 2006. / Submitted to the Faculty of Business and Law. Includes bibliographical references.

Thesis (M.B.A.) - Carleton University, 2002. / Includes bibliographical references (p. 251-259). Also available in electronic format on the Internet.

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 85-89). Also available on the World Wide Web.