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3812. Analysing the performance of Business Incubators in India using a DEA-Decision Tree approach

Invited abstract in session TD-48: DEA applications in Policy Making and Planning I, stream Data Envelopment Analysis and its Application.

Tuesday, 14:30-16:00
Room: 60 (building: 324)

Authors (first author is the speaker)

1. M S Karthicanand
Department of Management Studies, Indian Institute of Technology Madras
2. Prakash Sai Lokachari
Department of Management Studies, Indian Institute of Technology Madras

Abstract

Startups drive economic growth through technological innovations, market expansion, and increased production. This trend is evident globally, with countries like India fostering entrepreneurship through well-funded support structures, notably business incubators. Incubator performance assessment is challenging due to existence of diverse frameworks and objectives. Current literature often overlooks improvement pathways for individual incubators. To address this, we employ Data Envelopment Analysis (DEA) for relative performance evaluation, identifying improvement opportunities without preconceived assumptions. A Classification Decision Tree based on DEA outcomes distinguishes incubator profiles, notably highlighting differences between university-based units and others. Mentoring, infrastructure, and technology support emerge as crucial, especially for Deep Tech startups, which require distinct assistance due to their capital intensity and technological uncertainty. Our study assesses 53 Indian non-profit incubators and reveals variations in profiles that are discussed in the context of supporting Deep Tech startups, making it vital for policymakers and incubator managers navigating the evolving entrepreneurial landscape.

Keywords

Status: accepted

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