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  • 标题:Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions
  • 本地全文:下载
  • 作者:Benjamin Gidron ; Yael Israel-Cohen ; Kfir Bar
  • 期刊名称:Sustainability
  • 印刷版ISSN:2071-1050
  • 出版年度:2021
  • 卷号:13
  • 期号:18
  • 页码:10048
  • DOI:10.3390/su131810048
  • 语种:English
  • 出版社:MDPI, Open Access Journal
  • 摘要: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.
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