Over many years conjoint analysis has become the favourite tool among marketing practitioners and scholars for learning consumer preferences towards new products or services. Its wide acceptance is substantiated by the high validity of conjoint results in numerous successful implementations among a variety of industries and applications. Additionally, this experimental method elicits respondents’ preference information in a natural and effective way. One of the main challenges in conjoint analysis is to efficiently estimate consumer preferences towards more and more complex products from a relatively small sample of observations because respondent’s wear-out contaminates the data quality. Therefore the choice of sample products to be evaluated by the respondent (the design) is as much as relevant as the efficient estimation. This thesis contributes to both research areas, focusing on the optimal design of experiments (essay one and two) and the estimation of random consideration sets (essay three). Each of the essays addresses relevant research gaps and can be of interest to both marketing managers as well as academicians.
|Qualification||Doctor of Philosophy|
|Award date||14 Feb 2014|
|Place of Publication||Getafe, Spain|
|Publication status||Published - 2014|