Optimal Pricing for the Growth of Innovations with Direct Network Externalities : An Agent-Based Approach

Purpose – This study explores optimal pricing strategies for innovations with direct network externalities. The utility of these innovations increases with the number of adopters and thus the innovation takeoff requires a certain number of adopters. Design/methodology/approach – Agent-based Modeling and Simulation.Findings – The relationship between price and NPV of revenue resembles an asymmetric inverse U-shape. 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 NPV of sales.Practical implications – Pricing durable new products with network externalities are more challenging than other types of innovations. The price pattern of most new durable products are out of the control of firms and historical data indicates that the price of such innovations drops over time which further highlights the importance of their introductory pricing. 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 studying 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 shows 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 innovations with network externalities