Interest in understanding Human Beings’ behaviour can be traced back to the early days of mankind. However, interest in consumer behaviour is relatively recent. In fact, it is only since the end of World War II and following economic prosperity of some nations (e.g., U.S.A.) that the world witnessed the rise of a new discipline in the early 1950s; namely, Marketing Research. By the end of the 1950s, academic papers on modelling and analysis of consumer behaviour started to appear (Ehrenberg, 1959; Frank, 1962). The purpose of this research is to propose an integrated decision framework for modelling consumer behaviour with respect to store incidence, category incidence, brand incidence, and size incidence. To the best of our knowledge, no published contribution integrates these decisions within the same modelling framework. In addition, the thesis proposes a new estimation method as well as a new segmentation method. These contributions aim at improving our understanding of consumer behaviour before and during consumers’ visits to the retail points of a distribution network, improving consumer behaviour prediction accuracy, and assisting with inventory management across distribution networks. The proposed modelling framework is hybrid in nature in that it uses both non-explanatory and explanatory models. To be more specific, it uses stochastic models; namely, probability distributions, to capture the intrinsic nature of consumers (i.e., inner or built-in behavioural features) as well as any unexplained similarities or differences (i.e., unobserved heterogeneity) in their intrinsic behaviour. In addition, the parameters of these probability distribution models could be estimated using explanatory models; namely, multiple regression models, such as logistic regression. Furthermore, the thesis proposes a piece-wise estimation procedure for estimating the parameters of the developed stochastic models. Also proposed is a three-step segmentation method based on the information provided by the quality of fit of stochastic models to consumer data so as to identify which model better predicts which market segments. In the empirical investigation, the proposed framework was used to study consumer behaviour with respect to individual alternatives of each decision, individual decisions, and all decisions. In addition, the proposed segmentation method was used to segment the panellists into infrequent users, light to medium users, and heavy users, on one hand, and split loyals, loyals, and hardcore loyals, on the other hand. Furthermore, the empirical evidence suggests that the proposed piece-wise estimation procedure outperforms the standard approach for all models and decision levels. Also, the empirical results revealed that the homogeneous MNL outperforms both the heterogeneous NMNL and DMNL when each one of these distributions is applied to all decisions, which suggests the relative homogeneity in consumer decision making at the aggregate or integrated decision level. Last, but not least, through the use of the proposed framework, the thesis sheds light on the importance of consumer choice sequence on the quality of predictions, which affects the quality of segmentation. The reader is referred to chapter 3 for details on these contributions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:620839 |
| Date | January 2012 |
| Creators | Adnane, Alaoui M'Hamdi |
| Contributors | Ouenniche, Jamal; Archibald, Tom |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/9415 |
Page generated in 0.009 seconds