Performance Mapping of Startups in India Using Data Envelopment Analysis
Pratigya Kwatra () and
Gokulananda Patel ()
Additional contact information
Pratigya Kwatra: IILM University
Gokulananda Patel: Birla Institute of Management Technology
A chapter in Advances in the Theory and Practice of Data Envelopment Analysis, 2025, pp 460-473 from Springer
Abstract:
Abstract Performance measurement of start-ups is an emerging field of research. Relative efficiencies of thirty-nine Indian start-ups that became unicorns in the year 2022 were assessed using the DEA slack-based model. The inputs used in the DEA model consisted of human, financial, and technological capital indicators, while the output indicators were primarily financial parameters. Eight Decision Making Units achieved relative efficiency of 1 and 13 DMUs displayed efficiency of more than 50%. The results indicate that start-ups in India are operating at a lower efficiency. The performance of unicorns in India has been studied for the first time in this paper. The study is original in terms of indicators used in the DEA model to predict start-up performance and the application of slack-based DEA model for analyzing start-up efficiency.
Keywords: Start-up; Performance measurement; DEA; Slack-based model; Relative efficiency (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-98177-7_31
Ordering information: This item can be ordered from
http://www.springer.com/9783031981777
DOI: 10.1007/978-3-031-98177-7_31
Access Statistics for this chapter
More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().