Modeling Technological Substitution by Incorporating Dynamic Adoption Rate

Modeling diffusion dynamics of multi-generation innovation requires a critical examination of external factors that may affect its diffusion process. It has been observed that due to companies continuously varying marketing strategies, the adoption rate of an innovation alters with time. However, there are other factors such as the launch of a new competitive product or improved product generation, which may affect the growth of an innovation. The time-instance at which these changes are observed is called change-point. Motivated by this phenomenon, the present research identifies the launch of a new generation as a change-point where adoption function of the previous generation experiences a structural change. The objective of the current research is to improve the forecasting accuracy of a diffusion model for technological innovations by integrating essential factors that affect the diffusion process. From the findings of empirical analysis, it can be inferred that the proposed two-generational diffusion model illustrates the diffusion pattern of Dynamic Random Access Memory (DRAM) semiconductors remarkably well. In fact, the computed results show that the suggested model has better forecasting ability than previously established multi-generation models.