Do Early Words from New Ventures Predict Fundraising? A Comparative View of Social Media Narratives

Published: 12 January 2024

Taha Havakhor

Authors: Taha Havakhor, Alireza Golmohammadi, Rajiv Sabherwal and Dinesh K. Gauri

Publication: MIS Quarterly
Volume 47, Issue 2, June 2023, Pages 611-638

Online equity markets have significantly changed the dynamics of connecting angels and individual equity investors to new ventures that seek early-stage capital. However, for those early-stage investors, information pointing to the success of business-to-business (B2B) new ventures (B2BNVs) is scattered and disconnected. This paper focuses on social media narratives (SMNs) as a source of insight for such investors and proposes that predicting a B2BNV’s likelihood of success requires a comparative view, i.e., a comparison of its SMNs with those of its competitors and customers. We expect that higher (lower) lingual similarity between the SMNs of an early-stage B2BNV and those of its prospective customers (competitors) predict its success. Using a longitudinal panel of 574 B2BNVs resulting in more than 2,700 venture-round observations, we find that a comparative view of a venture’s SMNs can give early-stage investors reliable predictions about the B2BNV’s ability to manage its market presence and its success in later stages. Our models show that a comparative view of SMNs increases the accuracy of predicting a B2BNV’s later-stage fundraising success by an average of 15%. Furthermore, predictive models can reliably point to a successful market presence in later stages, including the landing of customers, the winning of awards and competitions, the receiving of endorsements, the generating of revenue, and the successful patenting of products. Our study contributes to existing literature that focuses on the business impacts of social media by demonstrating the usefulness of comparative linguistics in social media analytics, i.e., comparing the firm’s social media communications to those of its competitors and business customers in the prediction of the entrepreneurial firm’s success.

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