Abstract
Early-stage venture capital (VC) is a risky type of financing activity due to startups’ extremely high failure rate and the many unknown variables related to a venture’s future success. VCs’ investment decisions are further challenged by a rise in competition, significant time constraints, and different cognitive biases. In recent years, Machine Learning (ML) models have been empirically studied and adopted in the industry as a solution to overcome the limits that VCs face in their investment decision-making process. Nevertheless, a qualitative assessment of such technology’s impacts in early-stage VCs sourcing and screening processes lacks in the academic literature. In this regard, the findings of this paper highlight beneficial impacts on the quality of the deal flow from the adoption of ML-based tools. Concerning the screening process, the findings suggest limited impacts of such technologies mainly due to a lack of detecting crucial human capital components, investor strong reliance on their abilities and industry structural dynamics.
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Di Giannantonio, R., Murawski, M., Bick, M. (2022). The Impact of Machine Learning-Based Techniques on the Scouting and Screening Processes of Early-Stage Venture Capital Firms. In: Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y.K., Mäntymäki, M., Pappas, I.O. (eds) The Role of Digital Technologies in Shaping the Post-Pandemic World. I3E 2022. Lecture Notes in Computer Science, vol 13454. Springer, Cham. https://doi.org/10.1007/978-3-031-15342-6_11
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DOI: https://doi.org/10.1007/978-3-031-15342-6_11
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