Attraction models are very popular in marketing research for studying the effects of marketing instruments on market shares. However, so far the marketing literature only considers attraction models with certain functional forms that exclude threshold or saturation effects on attraction values. We can achieve greater exibility by using the neural net based approach introduced here. This approach assesses brands' attraction values by means of a perceptron with one hidden layer. The approach uses log-ratio transformed market shares as dependent variables. Stochastic gradient descent followed by a quasi Newton method estimates parameters. For store-level data, neural net models perform better and imply a price response that is qualitatively different from the well-known multinomial logit attraction model. Price elasticities of neural net attraction models also lead to specific managerial implications in terms of optimal prices. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Zugl.: Ulm, Univ., Diss., 2007 u.d.T.: Guenkova-Luy, Teodora: Coordination of multimedia services and applications in mobile, heterogeneous network environment

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Zugl.: Chemnitz, Techn. University, Diplomarbeit, 2006.

Zugl.: Saarbrücken, Univ., Diss., 2006

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Stuttgart, Univ., Diss., 2007.

Zugl.: München, Techn. Univ., Diss., 2006 u.d.T.: Lang, Daniel: On the evaluation and classification of routing protocols for mobile ad hoc networks / Hergestellt on demand