Does social media activity lead to more funds? – A study on Indian start-ups

• Published in: Journal of entrepreneurship in emerging economies Vol. 15; no. 5; pp. 967 - 987
• Main Authors: Singhal, Nidhi, Kapur, Deepak
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 31-10-2023; Emerald Group Publishing Limited
• Subjects: Access to information; Communication channels; Entrepreneurs; Funding; Social media; Social networks; Startups; Success; User generated content; United States > US; India; Start-up; Venture financing; Signaling; R; Seed funding; Social media; Twitter API; Emerging economies; Developing countries; Heckman’s model; Machine Learning; Python
• ISSN: 2053-4604; 2053-4612; 2053-4604
• DOI: 10.1108/JEEE-07-2021-0290

Purpose
This study aims to examine the impact of signaling through social media (SM) on funding achieved by start-ups.
Design/methodology/approach
This study follows a causal research design and is based on unique data set compiled from Crunchbase-Pro and Twitter. The sample size is 1,672 Indian start-ups. Heckman’s model and ordinary least squares regression is used to test the hypothesis.
Findings
Devising a thoughtful SM strategy, should be an integral part of the overall strategy of the start-ups looking out for funds. LinkedIn presence is in itself a positive signal. Active usage of Twitter and feedback from other Twitter users has a positive impact on funds raised by the start-up. Posting retweets and repetitive usage of URLs and media is not a predictor of funds raised by the start-up.
Practical implications
An early-stage strategy on SM adoption, especially Twitter can play an important role in attracting interest and attention of stakeholders. To capitalize SM, entrepreneurs should maintain an active SM account of the start-up.
Originality/value
India has emerged as one of the start-up hubs of the world. However, there is a dearth of literature on SM usage by start-ups in India. To the best of the authors’ knowledge, this study is first of its kind and establishes the results empirically based on more than 100k tweets for a large pool of Indian start-ups.