Impact tech startups: A conceptual framework, machine-learning-based methodology and future research directions

Benjamin Gidron, Yael Cohen-Israel, Kfir Bar, Dalia Silberstein, Michael Lustig, Daniela Kandel

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.

Original languageEnglish
Article number10048
Issue number18
StatePublished - 1 Sep 2021


  • Entrepreneurship
  • Hybrid ventures
  • Impact Tech
  • Impact investing
  • Innovation
  • Machine learning
  • SDG
  • Social enterprises
  • Startups

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


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