Network orchestration business models (NOBMs), such as Uber, Alibaba, and Airbnb, create value from an interaction between people. Every trip, every purchase, every stay generates data on the behaviour of customers and suppliers. The differences between these and the variety of actions they carry out make the data generated by the network far-reaching. As in biological evolution, where minor variations accumulated over time lead to the separation between one species and another, businesses adapt their proposal to meet the demands of customers and users. Orchestration leads to a beautiful ongoing complexity between data and economics.
The scope of the value propositions generated by data makes NOBMs long-range and continuously improving (Libert et al., 2014). The digitization of a NOBM enters when the data and its interactions are used to amplify, regulate or create information. In other words, the data creates other data. When NOBMs reach large scales, the constraints that arise from global and technological trends must be considered. The two most significant digital transformations, Industry 4.0 and financial technology (Fintech) aim to introduce the digital company, where the responsiveness of information to the physical world and the inclusion of digital financial services is integrated into organizations, people and assets. Technologies then become facilitators or disruptors (Briggs et al., 2020), creating adaptability mechanisms. Is there a way to regulate them? What is the maximum size or dimension that a business network can achieve?
Disruptive technologies like Blockchain have participated in the rapid evolution of several big brands. Their role is that of enablers and disablers of digital interaction (Swan, 2015). Blockchain allows chained cryptography of data and its historical relationship. Its main capabilities are a decentralized and distributed digital ledger that interacts with many actors. The complex central phenomenon associated with Blockchain is the self-ordering of the silos of knowledge and information.
Machine learning techniques are excellent catalysts (Hinton, 1992). Machine learning makes the collection and transmission of information more accessible, cheaper and faster. In addition, data interpretation and accuracy can be increased, making it possible to interconnect new industries. In short, "Machine Learning" takes NOBMs based on big data to a global scale that directly influences the social structure. How fast can a big data-based business evolve? Is there a scale at which it is impossible to maintain the model's sustainability?
We are going to argue and answer the previous questions. First, we have to make an assertion: the interactions, in conjunction with the continuous evolution of the value proposition in a NOMB, allow the rapid creation and deterioration of disruptive technologies, while enablers respond as catalysts for the performance of business operations. This back and forth between enablement and disruption generates a complex adaptive model. Let us explain a little more about the importance of such behaviour.
First, it is widely recognized that the ability of macroeconomic phenomena changes the environment in which microeconomic decisions are made (Fanelli et al., 1995). The stabilization of any macroeconomic variable of canonical fluctuation (such as GDP, national income or price indices) is one of the crucial searches to define strategies at the national level of any country (Gillis et al., 1992). From the point of view of agency theory (Braun and Guston, 2003), aggregation at the micro-level can create business patterns (Page, 2008). These patterns transported to the macroscale induce a new behaviour in society that, in turn, is fed back with disruptive technologies.
Nevertheless, if the economic feedback phenomenon is the father or mother of such successful models as Uber, Airbnb or Visa, what is why these are so difficult to reproduce? Is there a relationship between size, information and sustainability? In the next part, we will discuss more this matter from a social and technological perspective.
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