Dissertation / Doktorarbeit, die am 27.03.2006 erfolgreich an einer Internationale Wirtschaftshochschule in Deutschland im Fachbereich Betriebswirtschaft eingereicht wurde. Abstract: In Chapter 2, "Foundations", we provide a description of selected parts of theories which we believe are helpful to better understand the contribution of this thesis. We start with the presentation of several behavioral hypotheses in preference and utility theory. Next, we describe the basics of inferential statistics and Conjoint Analysis. Then, we describe probabilistic entropy, in addition to that a later established version of it, and its axiomatization as a general inference principle. We conclude Chapter 2 by presenting La Mura's decision-theoretic entropy, a version of entropy as an inference technique for expected utilities. La Mura had developed this connection between probabilistic entropy and expected utilities in his Ph.D. thesis. Based on his work, the initial research objective for this dissertation had been to make his approach applicable to the inference of unique consumer utilities given some observed evidence, having in mind the vast amounts of data that nowadays are available to analysts but still not used very effectively, in order to jointly overcome the limitations of Conjoint Analysis as mentioned above. In the following five chapters you will see that our research has instead resulted in a new method, namely Entropy Analysis, which is not based on expected utility functions but on ordinary utility functions. We close Chapter 2 with a conclusion for the following chapters. In Chapter 3, "Entropy Analysis", we derive the new method combining probabilistic cross-entropy and ordinary utility functions. We start by imposing a set of conditions on the inference method. Then, we suggest a normalization of utility functions such that they become formally a probability measure. Finally, we present and prove our main result. In Chapter 4, "Irrational Behavio...
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Textprobe aus Kapitel 2.4, Conjoint Analysis:
As mentioned in Chapter 1, Conjoint Analysis is the most popular utility
inference method available today. The name Conjoint Analysis does not
represent one single, in some sense well-defined, formula or technique to
infer a utility function in a given context. Instead, it is a collection of
approaches that has been extended by many researchers with a plentitude of
refinements and improvements.
A common core that all approaches under the umbrella Conjoint Analysis
share is a link to the initial and seminal contribution that introduced
conjoint measurement and the usage of conjoint measurement in marketing for
utility inference. The differences between most approaches can be found in
how data are collected and parameters for the inferred utility function are
estimated.
In contrast to the so-called expectancy-value models, a compositional
approach in which the utility for some object is determined by the weighted
sum of the object's perceived attribute levels and associated value ratings
separately judged by the respondent, Conjoint Analysis is a decompositional
approach. Respondents judge a set of product descriptions, and then the
analyst finds so-called part-worths for the individual attributes that are
most consistent with the respondents' overall preferences.
Since its start in the early 1970s, a plethora of new Conjoint Analysis
models has been introduced to improve various aspects of the method.
Nevertheless, the basic framework has not changed. Therefore, we would like
to follow the lines of an overview and procedural description of the
Conjoint Analysis methodology given by Green and Srinivasan.
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