Understanding data-driven business model innovation in complexity: A system dynamics approach

With the growing complexity of today's big data environments, data-driven business model innovation has shown the key features of a complex system, such as dynamics and non-linearity, but relevant research mainly draws a static and linear perspective, which necessitates unveiling data-driven business model innovation as a complex system. To this end, building on complexity theory, this study divides the complex system of data-driven business model innovation into three interdependent subsystems (i.e., big data, business model innovation, and data value). Each subsystem has its more granular components and elements. Then, the system dynamics approach is adopted to clarify the coevolution process and its key influencing factors of data-driven business model innovation. By conducting simulation and sensitivity analysis of key variables, the findings suggest that, like the "flywheel effect", big data insight, value proposition, customer performance, and firm performance increase with time. Among them, big data insight, value proposition, and customer performance have a basically consistent pace. By contrast, firm performance grows much more slowly at the beginning but has a stronger acceleration in later stages. Besides, improving big data analytics cannot directly increase data value. Only when combined with businesses, it can create marginal benefits, among which business matching is the most salient. This study not only contributes to the advancement of complexity theory and data-driven business model innovation but also deepens business model research through holistic and systematic approaches.