Pricing and Referrals in Diffusion on Networks
Matt V. Leduc, Matthew O. Jackson, Ramesh Johari

TL;DR
This paper models how network structure influences technology adoption, showing that referral incentives and pricing strategies can optimize sales and welfare depending on network topology.
Contribution
It introduces a dynamic game framework analyzing referral incentives and pricing strategies for technology adoption on networks, highlighting optimal policies for different network types.
Findings
Referral incentives induce early adoption among high- and low-degree consumers.
Optimal strategies vary: referral incentives are best for some networks, while price discounts work better for others.
The model reveals welfare implications of different marketing policies.
Abstract
When a new product or technology is introduced, potential consumers can learn its quality by trying the product, at a risk, or by letting others try it and free-riding on the information that they generate. We propose a dynamic game to study the adoption of technologies of uncertain value, when agents are connected by a network and a monopolist seller chooses a policy to maximize profits. Consumers with low degree (few friends) have incentives to adopt early, while consumers with high degree have incentives to free ride. The seller can induce high-degree consumers to adopt early by offering referral incentives - rewards to early adopters whose friends buy in the second period. Referral incentives thus lead to a `double-threshold strategy' by which low and high-degree agents adopt the product early while middle-degree agents wait. We show that referral incentives are optimal on certain…
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Taxonomy
TopicsGame Theory and Applications · Digital Platforms and Economics · Consumer Market Behavior and Pricing
