Optimal pricing for the growth of innovations with direct network externalities: an agent-based approach

摘要

Purpose - This study aims to explore optimal pricing strategies for innovations with direct network externalities - the effect that the number of adopters of an innovation has on the utility of the innovation to other potential adopters. Examples of such innovations are fax machines, e-mail, cellular phones, and software programs such as word processors. The success of these innovations requires a minimum number of adopters.Design/methodology/approach - The paper uses agent-based modeling and simulation.Findings - The relationship between price and net present value (NPV) of revenue resembles an asymmetric inverse U-shape. A low-pricing strategy outperforms high-pricing, while a moderate-pricing strategy outperforms both low- and high-pricing strategies. Moreover, heterogeneity of consumer price sensitivity positively affects the NPV of sales.Practical implications - Pricing durable new products with network externalities is more challenging than other types of innovations. The results indicate that firms can maximize their NPV by adopting a moderate pricing strategy. Moreover, firms must consider heterogeneity of consumer price sensitivity along with the market price elasticity when making pricing decisions. Detailed strategic implications and recommendations are discussed.Originality/value - Several recent studies have called for examining pricing strategies for new products with network externalities. The study findings challenge the common wisdom that a penetration pricing strategy is an optimal approach for durable products with network externalities. Moreover, while other studies have highlighted the importance of market price elasticity, extensive simulation experiments conducted in this study show that heterogeneity of consumer price sensitivity is an important factor that must be considered. Finally, the study presents an agent-based modeling approach for exploring optimal pricing of innovations with network externalities.