Numerous studies have been devoted to the factors influencing the market success of startups. However, at early stages of startup development, obtaining investments is a priority. Nevertheless, the topic of the influence of various factors, specifically the choice of business model, on the amount of investment received by a startup remains underexplored. Therefore, the aim of this research is to assess the impact of the business model on the amount of investment received by a startup at the Series A stage. The hypothesis being put forward is that the business model patterns utilized by the startup impact the amount of investment received at the Series A stage. To achieve this goal and test the hypothesis, Student's t-test and Mann-Whitney test were applied to a sample of 2313. As a result of the study, the influence of the business model for different industries was revealed. Considering that different business models have varying effects on the amount of investment, models leading to an increase or decrease in investment size were identified for startup founders. The results of this article enable startups to compare their chosen model with those that allow for larger investments and to adjust their chosen strategy. Additionally, this study stands out due to the uniqueness of the methods applied within the scope of the issues covered in the article and the unique sample size in assessing the impact of the business model factor. The findings of this research serve as a catalyst for incorporating the business model factor into further studies dedicated to a comprehensive assessment of a startup's investment attractiveness and the creation of a machine learning model to predict the success of obtaining investments and the amount of investment a startup can expect.
Translated title of the contributionВЛИЯНИЕ БИЗНЕС-МОДЕЛИ НА РАЗМЕР ИНВЕСТИЦИЙ, ПОЛУЧЕННЫХ СТАРТАПОМ НА СТАДИИ SERIES A НА РЫНКЕ США
Original languageEnglish
Pages (from-to)551-571
Number of pages21
JournalJournal of applied economic research
Volume22
Issue number3
DOIs
Publication statusPublished - 2023

    Level of Research Output

  • Russian Science Citation Index
  • VAK List

ID: 46057292