Sheth Foundation Doctoral Dissertation Competition at EMAC 2018: Tetyana Kosyakova gewinnt den dritten Platz

New generations of marketing experts are crucial to develop the theories and tools that shape the future of our field. The EMAC/Sheth Foundation Doctoral Dissertation Competition recognizes and encourages this emerging talent. This distinguished award will be presented in 2018 for the second time. Three finalists will be honored with cash prizes and the opportunity to present their work at the EMAC 47th Annual Conference in Glasgow, UK.

Tetyana Kosyakova, Goethe University Frankfurt, Germany “Measuring Substitution and Complementarity Among Offers in Menu Based Choice Experiments”

Choice experiments designed to extend beyond the classic application of choice among perfect substitutes have become popular in marketing research. In these experiments, often referred to as menu-based choice, respondents face choice sets that may comprise substitutes, complements, and offers that provide utility independently, or any mixture of these three types.

The inferential challenge posed by data from such experiments is in the calibration of utility functions that accommodate a mix of substitutes, complements, and “independent” offers.

Moreover, while a prior understanding of the product categories under study may, for example, suggest that two offers in a set are essentially perfect substitutes, this may not be true for all respondents. To address these challenges, we combine Besag’s (1972, 1974) autologistic choice model with a flexible hierarchical prior structure. We explain from first principles how the autologistic choice model improves on the multivariate probit model, and on models that include cross-price effects in the utility function. We develop Bayesian inference for the autologistic choice model, including its intractable normalizing constant and find empirical support for our model in a menu based conjoint experiment investigating demand for game consoles and accessories. We illustrate implications for optimal pricing.

 

More details you find here

 

 

Top