The Diffusion of Goods Considering Network Externalities: A System Dynamics-Based Approach

The diffusion of goods showing network externalities differs from that of conventional products. In particular, two effects are important: the bandwagon and the penguin effect. An incrementally refined system dynamics model helps to understand this special diffusion process better. Leverage points for the management of such products can be identified using simulation results. In recent years, trends towards an information society have emphasized the importance of goods satisfying information and communication needs. Many products of this market segment, e.g. electronic mail, contain characteristics, which make a specific examination necessary. These characteristics are based on the fact that the utility of these products cannot be regarded as a constant value. In case of this specific type of products, utility is a dependable variable resulting in a distinct diffusion behavior. The occurrence of a variable utility can be explained by a concept called “network externality”. A product is characterized by a network externality, if the utility of a product is a function of the installed base. The utility of products including network externalities rises with this value leading to an interdependency of users. This interdependency is based on two properties of products with network externalities: the penguin effect and the bandwagon effect. This paper examines the way how the diffusion of goods is influenced by these effects using the system dynamics approach. Based on the basic diffusion model of Bass, characteristics of network externalities are integrated successively to simulate the diffusion behavior of these specific goods. This analysis helps in understanding causes and effects of network externalities. After that, conclusions concerning successful diffusion strategies for these particular goods are derived from the system dynamics model. Starting points for further research are indicated at the end of this paper. * The authors like to thank Frank H. Maier for his helpful comments during the model building process. A Model to Calculate Product Diffusion Historically, economic models of the diffusion of innovations are based on biological and sociological research. On this base, diffusion is defined as “the process by which an innovation is communicated through certain channels over time among the members of a social system” (Rogers 1983). Although this general definition is valid for any kind of innovation, in this paper only the launch of new products in a certain market are investigated. Process, social, or organizational innovations are not considered. In Roger’s definition, communication is the driving force that creates the wish to buy a product. Communication—seen as the exchange of information between members of a social system—initiates diffusion and influences size and speed of this process (Maier 1995). Models of diffusion, therefore, describe how communication drives diffusion; they are “the mathematical representation of the process of diffusion” (Kalish and Lilien 1986). One of the best known diffusion models is from Bass (1969). As a mixed-influence model, it integrates effects of mass and impersonal communication (Mahajan and Peterson 1985). It distinguishes between two types of customers: innovators and imitators. Innovators become customers because they are interested in novelties. Imitators ground their decision to buy a product on the behavior of other members of the system. Bass formulates the following equation in order to calculate product sales x for a specific time period t: ) X N ( X N ) X N ( x t t t t − β + − α = N symbolizes the total market potential, Xt cumulated sales from period 1 to t-1. α and β are innovator and imitator parameter, respectively. They contain the weight of innovator or imitator influence on the diffusion process. Electric refrigerators: Dishwashers: BW television: Potential Adopters P Adopters A Adoption Rate AR ALPHA BETA