Schulte-Althoff, Matthias, Daniel Fuerstenau, Gene Moo Lee, Hannes Rothe, Robert Kauffman. “What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. Working Paper. [ResearchGate]
- Previous title: “A Scaling Perspective on AI startup”
- Presented at HICSS 2021 (SITES mini-track), Copenhagen Business School 2021, FU Berlin 2021, University of Cologne 2021, University of Bremen 2021, Humboldt Institute for Internet and Society 2021, WITS 2022
We examine how firm revenue scales with labor for revenue-per-employee (RPE) and is moderated by firm-level AI investment. We compare AI start-ups, in which AI provides a competitive advantage, with digital platforms and service start-ups. We use propensity score matching to explain the scaling of start-ups and find evidence for sublinear scaling intensity for revenue as a function of labor. Our study suggests similar scaling intensities between AI and service start-ups, while platform start-ups produce higher scaling intensities. We show that an increase in employee counts is associated with major revenue increases for platform start-ups, while increases were modest for service and AI start-ups.
Schulte-Althoff, Matthias, Kai Schewina, Gene Moo Lee, Daniel Fuerstenau. “On the Heterogeneity of Startup Tech Stacks”. Working Paper [HICSS version]
- Presented in HICSS 2020 (Maui, HI)
- Previous title: On the Heterogeneity of Digital Infrastructure in Entrepreneurial Ecosystems
Digital infrastructure is the backbone on which digital startups realize business opportunities, and the homogeneity or heterogeneity of the technological base can have significant downstream impacts on business risks, inflexibilities, and growth barriers. On the nexus of digital entrepreneurship and infrastructure studies, we suggest a conception of startup digital infrastructure as organized in tech stacks; tech stacks contain individual technological elements that are combined in a single startup, while the way this is done will be inspired by shared templates within the ecosystem. Given that there is limited understanding of the heterogeneity (or homogeneity) of startup tech stacks, we use public registry datasets from StackShare and Crunchbase to identify common tech stacks of startups. Through our analysis, we identify ten commonly used startup tech stacks, which we use to measure and analyze the heterogeneity of startup tech stacks and its antecedents. OLS regression analysis shows that a startup’s technologies’ interrelatedness, its age, and investor funding are associated with the heterogeneity of startup tech stacks. The overall analysis suggests that while startups may make individual choices regarding technology usage, there could be underlying commonalities and imprinting effects across startups, exposing them to common risks in terms of their digital infrastructures. This could pose important implications for startups, investors, and society at large