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  4. Predicting Startup Survival from Digital Traces: Towards a Procedure for Early Stage Investors
 
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Predicting Startup Survival from Digital Traces: Towards a Procedure for Early Stage Investors

Type
conference paper
Date Issued
2018-12-13
Author(s)
Antretter, Torben
Blohm, Ivo
Grichnik, Dietmar
Research Team
IWI6CCC, CCC, Crowdsourcing, IWI6
Abstract
We investigate whether digital traces can be used to predict early stage startup survival. Based on common survival factors from the entrepreneurship literature, we mined the digital footprints of 542 entrepreneurs and their ventures. Using a context-specific text mining approach, we performed a bootstrapping simulation in which we predict 5-year survival for different survival rates that range from 50% to 10%. Our results indicate that we can predict 5-year survival with an accuracy of up to 91%. With this study, we will provide an evidence-based taxonomy of digital traces for predicting early stage startup survival, identify the most important digital traces for doing so and benchmark our predictive approach against the actual investments of 339 business angels.
Project(s)
CC Crowdsourcing
Learning Algorithms for Discrimination Free Innovation Funding Activities
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Event Title
International Conference on Information Systems (ICIS)
Event Location
San Francisco, CA, USA
Event Date
13.12.2018-16.12.2018
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/99762
Subject(s)
  • information managemen...

  • business studies

Division(s)
  • ITEM - Institute of T...

  • IWI - Institute of In...

Eprints ID
255532
File(s)
Survival Prediction Paper - Resubmission.pdf (352.3 KB)
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