Effects of the topology of a social network on the optimal price and the optimal total profit of a new product during its diffusion
Creators: name: Inostroza-Quezada, Ignacio E.
Publisher: name: No Publisher Supplied
Year: 2021

description: The advent of digital social networks (DSN) has provided consumers with an additional source of information about new products, so that consumers can increasingly rely on reviews of prior buyers connected with them in a DSN to learn about new products. This reliance has encouraged researchers to i) include consumerlevel interactions in DSN to study new products’ life cycle, and ii) exploit information about consumers’ connections in a network to design marketing strategies for new products.Early literature on diffusion of new products assumed that potential adopters were homogeneous and fully connected each other, and disregarded the global structure of connections among potential adopters in a DSN to design pricing strategies. This dissertation contributes to the literature on diffusion of new products in three ways. First, it develops a new-product diffusion model that captures heterogeneity among potential adopters in a social network with density smaller or equal than one. Second, it uses this new-product diffusion model to study the effect of the global structure of connections in the social network on the optimal pricing strategy and optimal profits for a new product. Third, it compares optimal total profits along the diffusion process of a new product when a firm launches this product only to a small proportion of potential adopters with the largest centrality in the network. Thiscontribution could help firms to design more profitable launching strategies of new products by focusing on the most influential potential adopters based on their position in the network. This dissertation distinguishes two pricing policies: a fixed and a dynamic pricing strategy. Additionally, it uses two centrality measures: degree and eigenvector centrality.The new-product diffusion model incorporates potential adopter-specific perceptions about the new product’s quality, as in Chatterjee and Eliashberg (1990). The approach uses Bayesian methods to update these perceptions as they receive signals from prior buyers connected with them at each time during the diffusion process. This dissertation illustrates the use of the diffusion model with a dataset from Armelini et al. (2015) which concerns the adoption of internet service among households in a dynamic social network. Reciprocated telephone calls among households establish the edges in this network.To study the effect of the structure of connections in the network on both the optimal pricing strategy and the optimal total profit for a new product, I use agentbased modeling (Rand and Rust, 2011). This modeling strategy uses simulations to study the long-term behavior of a group of heterogenous individuals who have if-then rules and interact each other. I simulate the diffusion process with social networks generated with Watts and Strogatz (1998)’s algorithm. This algorithm uses a randomness parameter whose value increases with the randomness in the structure of connections among consumers. Based on simulated diffusion processes, I study the effect of the global network structure on each of the optimal pricing strategies and optimal total profits for the new product. I describe this global structure with two network parameters: size and density.Results from the simulations suggest that i) both the optimal price and the optimal total profit increase with network density, given a social network of a fixed size and a diffusion that operates under a fixed pricing policy, and ii) for a 100-node social network with a given density, a dynamic pricing policy leads to larger optimal total profits than a fixed pricing policy. However, for a 100-node social network with a given density, results do not show a statistically significant difference between the optimal total profits along the diffusion process when the firm launches a new product to two consumers: one is the consumer with the largest degree centrality, and the other is the consumer with the largest eigenvector centrality.