Demand Estimation for Evolving Products Considering Network Effects: A Networked Affinity Dynamics Model

Abstract As the digital marketplace continues to evolve with rapid changes in consumer preferences and the frequent introduction of product updates, traditional models for estimating demand are often unsuitable. These models fail to account for the dynamic nature of product development and the complex decision-making processes of consumers, particularly under the influence of social networks. To address this gap, this study presents a networked demand model based on decision field theory (DFT), focusing on the impact of network effects on consumer behavior. By incorporating the influence of social networks into the DFT-based demand model, we aim to capture how the decision-making process and subsequent demand are shaped by social interactions. We validate the networked model using real-world data from competing software products, demonstrating its practical applicability. Furthermore, we provide insight into how the network structure and strength of social influence impact demand.