How machine learning activates data network effects in business models: Theory advancement through an industrial case of promoting ecological sustainability
首次将数据网络效应理论与商业模式理论结合，通过工业案例展示机器学习如何通过数据网络效应提升盈利能力并减少生态负面影响。
A firm’s business model accounts for direct and indirect network effects, where the network size is a key enabler of value creation and appropriation. Additional conception of a business network’s contribution is provided by a recent advancement of the theory of data network effects, where machine learning is used to analyze large data sets to learn, predict, and improve. The more learning there is, the more value is generated, producing ever more data and learning and creating a virtuous circle. For the first time, this study combines the theory of data network effects with business model theory. The contribution lies in extending a business model’s lock-in effects through direct and indirect network effects to encompass data network effects. This paper provides a case study that supports the theoretical advancement and illustrates how this form of machine learning can increase profitability while reducing negative ecological impacts in an industrial context.