Toronto Metropolitan University
Browse

Can It Screen? Exploring the Usability of a Data-driven Lean Canvas Framework for Startup Selection in Accelerators

Download (1.93 MB)
thesis
posted on 2024-03-16, 19:13 authored by Ahmad Jowhar

The emergence of startups across various industries has resulted in an influx of new ventures seeking guidance from entrepreneurial programs to assist in their development, including startup accelerators. However, given the human capital and time constraints faced by accelerators, issues arise on selecting the most optimal startups. This thesis proposes a framework that would leverage startup’s application into an accelerator to create insightful features, through machine learningtechniques. Furthermore, the leancanvasframeworkwould be utilized to mapthe features to its respective dimensions and identify the impact each dimension has on the startup selection process. I extensively studied the effectiveness of this framework by analyzing startup’s application to a US-based accelerator. The most important features were a startup’s competitive advantage and industry similarity with accelerator programs. The proposed pipeline introduces a unique framework to assist in the startup selection process and contributes to the growth within the entrepreneurial ecosystem.

History

Language

English

Degree

  • Master of Science in Management

Program

  • Master of Science in Management

Granting Institution

Ryerson University

LAC Thesis Type

  • Thesis

Thesis Advisor

Morteza Kermani

Year

2022

Usage metrics

    Management (TRSM) (Theses)

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC