Measurement of Cannibalism Effects in buying experiments using Mixed Logit Models - The Example of a new Brand of the “Fruits of Lake Constance” Association -

Profeta, Adriano (2008): Measurement of Cannibalism Effects in buying experiments using Mixed Logit Models - The Example of a new Brand of the “Fruits of Lake Constance” Association -.

Abstract

One fundamental assumption of discrete choice regression is the assumption of independence of irrelevant alternatives (IIA). According to the IIA assumption no correlation is allowed between brands in buying experiments. As a consequence, in market simulations all remaining brands gain at the ratio of their starting market share if one brand is excluded from the simulation set. This often does not reflect the reality at the point-of-sale. Mixed-logit models offer the advantage that the IIA-assumption is completely relaxed. What is more, simulations based on mixed logit are able to model cannibalism effects. This paper applies mixed logit to buying behaviour research. A case study is presented where the introduction of a new apple brand at the German discounter “Penny” is simulated in a buying experiment.

• Item Type: MPRA Paper
• Language: English
• Keywords: Mixed Logit Model; IIA-assumption; cannibalism effect; choice experiment; apples
• Subjects: Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture; D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis; D - Microeconomics > D9 - Intertemporal Choice > D91 - Intertemporal Household Choice ; Life Cycle Models and Saving

References

BEN-AKIVA, M. and S.R. LERMAN (1985): Discrete Choice Analysis: Theory and Application to Travel Demand. Cambrigde, MA: MIT Press.

BROWNSTONE, D. and K. TRAIN (1999): Forecasting new product penetration with flexible substitution patterns. In: Journal of Econometrics 89: 109-129.

ENNEKING, U. (2003): Die Analyse von Lebensmittelpräferenzen mit Hilfe von Discrete- Choice-Modellen am Beispiel ökologisch produzierter Wurstwaren. In: Agrarwirtschaft 52 (5): 254-267.

ENNEKING, U. (2005): Präferenzen für Lebensmittelqualität aus mikroökonomischer und konsumentenpsychologischer Sicht, bisher unveröffentlichte Habilitationsschrift, Fakultät für Agrarwissenschaften der Universität Göttingen.

ENNEKING, U., C. NEUMANN ANDJJ, S. HENNEBERG (2007): How important intrinsic and extrinsic product attributes affect purchase decision. In: Food Quality and Preference (Volume 18): 133-138.

FEDOROV, V.V. (1972): Theory of Optimal Experiments. Translated and edited by STUDDEN, W.J. and E.M. KLIMKO. Academic Press, New York.

Alfnes, F. (2004): Stated Preferences for imported and hormone-treated beef: application of a mixed logit model. In: European Journal of Agricultural Economics 31 (1): 19-37.

GREENE, W.H. (2002): Nlogit Version 3.0 Reference Guide. New York. Econometric Software, Inc.

LOUVIERE, J., D. HENSHER and J. SWAIT (2000): Stated Choice Methods. Analysis and Application. Cambridge: Cambridge University Press.

MCFADDEN, D. (1974): Conditional Logit Analysis of Qualitative Choice Behavior. In: ZA REMBKA, R. (ed.): Frontiers in Econometrics. New York: Academic Press: 105-142.

PROFETA, A. (2006): Der Einfluss geschützter Herkunftsangaben auf das Konsumenten12 vehalten bei Lebensmitteln - Eine Discrete-Choice-Analyse am Beispiel Bier und Rindfleisch. Studien zum Konsumentenverhalten, Bd. 7, Hamburg.

PROFETA, A. and U. ENNEKING (2004): Interactions of Protected Geographical Indications (PGI) with brand names: The case of “Bavarian Beer” and “Munich Beer”. Unpublished Article. Technische Universität München, Weihenstephan. URBAN, D. (1993): Logit-Analyse. Statistische Verfahren zur Analyse von Modellen mit qualitativen Response-Variablen. Gustav Fischer Verlag, Stuttgart.