A Generative Decision Analytics Framework for Modelling Disruptive Innovation Diffusion

The framework of disruptive innovation has changed how we analyse and interpret many successful businesses, especially those that start from a disadvantageous position. A wealth of real-world cases has been studied to offer valuable insights into what disruptive innovation is and how it evolves. However, case studies only focus on a small set of numerous possible outcomes, unable to answer “what-if” questions – essential for revealing the core factors/mechanisms that define disruptive innovation. Following the philosophy of generative social science, this research builds an analytics framework that incorporates an agent-based model, aiming to “grow” typical diffusion patterns of disruptive innovation from microscopic consumer decisions while investigating underexplored mechanisms and alternative outcomes via numerical experiments. The duality of value dimensions is assumed to be the most essential characteristic of disruptive innovation in the framework. Other factors, including heterogeneous consumer preferences, complementary technologies, technological progress, and pricing, are experimented with as key factors that may shape the diffusion processes. The results demonstrate that the model can reproduce multiple diffusion patterns and stylised facts regarding disruptive innovation, such as market encroachment on low-end markets, opening new markets, capturing market shares from mainstream markets, being impacted by new disruptors, and its coexistence with sustaining innovation. The analysis indicates that the dual value dimensions endow an innovation with the potential of being disruptive, while other factors can adjust its actual disruptive effect. Based on the findings, theoretical and practical managerial suggestions are provided for disruptive innovation researchers and practitioners.