Scaling the user base of digital ventures through generative pattern replication : the case of ridesharing

Abstract

Digital ventures, for example Uber and Airbnb, seek to scale their user base quickly and effectively across markets in order to lock out competitors and drive adoption through positive feedback loops. I view such rapid global scaling as an organising logic by which digital ventures replicate a generic solution to recurring challenges. This thesis intends to understand the process by which digital ventures scale across a multitude of varied regional markets. By arguing that this process is qualitatively different from our current conceptualisations of scaling I aim to encourage more researchers to pay heed to scaling as an integral part of digital innovation literature. To this end I present a qualitative study of a digital venture called BlaBlaCar, a ridesharing venture that rapidly scaled its user base into 22 markets. My findings are based on original data, collected over a course of two years in two stages. First, by collecting observational data for four months, and second, by collecting 58 interviews across 15 offices globally. In this thesis I distinguish and describe scaling as the process of generative pattern replication (GPR), where an existing scaling pattern is specialised to the specific circumstances of a new market, and applied there. I trace three mechanisms underpinning rapid scaling across regional boundaries: instantiation, venture meshing, and value frame. I explain these mechanisms and how they interact in the process of GPR. My research speaks to the digital innovation literature by making a unique contribution: a novel perspective on scaling of digital ventures including a process model and related mechanisms. In addition, my proposed research findings have the potential to offer valuable insights for digital ventures looking for novel scaling and digital innovation management tools