Differentiate and Conquer: Using Consumer Learning to Grow Out Your Niche

Item Type Monograph (Working Paper)
Abstract The recommendation effect introduces a new rationale for product differentiation other than the usual motivation to reduce price competition. We introduce consumer learning in a version of Hotelling's model (1929) of spatial competition with sequential consumer purchases and a second dimension of variation, quality, about which the consumers have differential information. With consumer learning, firms are confronted with two offsetting effects: differentiation decreases the likelihood that a product is bought in earlier periods, but, by making inference more valuable, it also increases the likelihood that later consumers buy the differentiated good. We show that there exists a unique equilibrium in which the second effect dominates, so that the market incumbent locates in the center of the market, while the entrant differentiates by producing an ex-ante niche product. Due to consumer learning, uninformed consumers are unambiguously better off in the equilibrium with differentiation than in the equilibrium of minimum differentiation which occurs without consumer learning. Informed consumers are better off in the latter equilibrium, so that the overall effect on consumer welfare depends on the parameters and we can show that in some cases transparency enhancing policies are welfare decreasing.
Authors Conze, Maximilian & Kramm, Michael
Language English
Subjects economics
HSG Classification contribution to scientific community
Date 2017
Official URL https://docs.maxconze.net/Conze and Kramm - Recomm...
Contact Email Address maximilian.conze@unisg.ch
Depositing User Dr. Maximilian Conze
Date Deposited 15 Jun 2017 08:10
Last Modified 19 Nov 2018 13:23
URI: https://www.alexandria.unisg.ch/publications/251020

Download

Full text not available from this repository. (Request a copy)

Citation

Conze, Maximilian & Kramm, Michael: Differentiate and Conquer: Using Consumer Learning to Grow Out Your Niche. , 2017,

Statistics

https://www.alexandria.unisg.ch/id/eprint/251020
Edit item Edit item
Feedback?