3^rd International Congress on AI and Machine Learning August 03-04, 2022 Ireland, Dublin

Biography:

Abstract:

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.

 

Biography:

Benjamin Gidron is the founder and director of the Israeli Center for Third sector Research (ICTR) at Ben Gurion University of the Negev in Beer-Sheva, Israel. He has been researching issues pertaining to the Third Sector and related topics for the past 25 years.

He is a Professor at the Guilford Glazer School of Business & Management at BGU, where he founded and directs the Nonprofit Organizations Management Program . He came to the School of Management after serving for some 20 years at the BGU Spitzer Department of Social Work, where he served as the chair (1985-90). He holds the David and Dorothy Schwartzman Chair for Community Development.

Abstract:

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.