Abstract
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.
Highlights
A firm’s use of digital technologies enables the activation of direct and indirect network effects (Katz & Shapiro, 1985; Parker, Van Alstyne, & Jiang, 2016) as part of the business model to create and appropriate value (Amit & Zott, 2001)
To answer this research question, this paper presents a unique case study of an industrial firm that has embraced the use of big data and machine learning in its development of dedicated customer service
Experience shows that some firms succeed in using digital technol ogies to activate direct and indirect network effects in their business models (Parker et al, 2016)
Summary
A firm’s use of digital technologies enables the activation of direct and indirect network effects (Katz & Shapiro, 1985; Parker, Van Alstyne, & Jiang, 2016) as part of the business model to create and appropriate value (Amit & Zott, 2001). In certain cases, such as the technology firms Amazon, Apple, Alphabet, Facebook, and Microsoft, this value is un precedented (Parker et al, 2016). This case study offers an in-depth understanding of how firms can create value by modifying the business model architecture to adopt and use machine learning technology and thereby activate data network effects (Foss & Saebi, 2017; Teece, 2010). The paper ends by of fering a discussion and presenting the conclusions of the study
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