2016-12-24T18:07:49

AcknowledgementsMany individuals have contributed intellectually to this book. The literature on discrete choice analysis, combining sources of preference data and experimental design, is vast, with a history spanning at least sixty years. This book is a contribution to that literature, inspired by a need at the end of the twentieth century for a single source accessible to both practitioners and researchers who need some assistance in`travelling' through the essential components of the extant literature in order to undertake an appropriate systematic study of consumer choice behaviour.To Daniel McFadden, Norman Anderson and Moshe Ben-Akiva we owe a special debt for their contribution to the literature and for their inspiration to all authors. Wiktor Adamowicz and graduate students and sta in the Faculty of Economics and Business at the University of Sydney read earlier versions of the book and guided us in revisions. The in¯uence of a number of other colleagues has been substantial in our appreciation of the topic. We especially thank

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This article tests how well the information economics view of brand equity explains consumer brand choice in countries that represent different cultural dimensions. In this empirical analysis, the authors use survey and experimental data on orange juice and personal computers collected from respondents in Brazil, Germany, India, Japan, Spain, Turkey, and the United States. The results provide strong empirical evidence across countries for the role of brands as signals of product positions. In addition, the positive effect of brand credibility on choice is greater for consumers who rate high on either collectivism or uncertainty avoidance. Credible brands provide more value to collectivist consumers because such consumers perceive these brands as being of higher quality (i.e., reinforcing group identity). Credible brands provide more value to high-uncertainty-avoidance consumers because such brands have lower perceived risk and information costs.

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Multinomial logit (MNL) models are widely used in marketing research to analyze choice data, but it is not generally recognized that the unit of the utility scale in a MNL model is inversely related to the error variance. This means that, for instance, parameters of two identical utility specifications estimated from different data sources with unequal variances will necessarily differ in magnitude, even if the true model parameters that generated the utilities are identical in both sets. Despite a growing number of papers that compare MNL coefficients, no examples of appropriate tests of the joint and separate hypotheses of scale and parameter equality in MNL models exist in the marketing literature. The purpose of this paper is to address the proper procedure for MNL parameter comparisons between different data sets and to propose a simple relative scaling test that can be implemented with standard MNL estimation software. Several examples are given to illustrate the approach.

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