High-tech gazelle firms at various stages of evolution: performance and distinctive features

Purpose: The purpose of this study is the detection and comparison of distinctive features of Gazelle firms (GFs) at three stages evolution outside the typical boundaries. Design/methodology/approach: The study uses Analysis of Variance and logistic regression to tests the performance of 2427 gazelles for (GFs) a five-year period (2015–2020). Findings: The study found that GFs prediction probability is low. In their second and third stages of evolution (initial growth and continuing growth), the gazelle growth effects appear. They are more effective in terms of profitability and turnover due to increasing sales and size. Practical implications: This study shows that stakeholders should give preference to GFs that demonstrate long-term (steady) growth. Such firms are more efficient and financially stable than firms with high short-term growth. Originality/value: The present study identifies patterns in the generation and development of GFs in high-tech industries outside the typical boundaries. © 2022, Emerald Publishing Limited.

Authors
Spitsin V.1, 5 , Vukovic D. 2, 3 , Mikhalchuk A.4 , Spitsina L.4 , Novoseltseva D.6
Publisher
Emerald Group Publishing Ltd.
Language
English
Status
Published
Year
2022
Organizations
  • 1 School of Engineering Entrepreneurship, National Research Tomsk Polytechnic University, Tomsk, Russian Federation
  • 2 International Laboratory for Finance and Financial Markets, Faculty of Economics, RUDN University, Moskva, Russian Federation
  • 3 Department of Regional Geography, Geografski institut Jovan Cvijic Srpske akademije nauka i umetnosti, Beograd, Serbia
  • 4 School of Core Engineering Education, National Research Tomsk Polytechnic University, Tomsk, Russian Federation
  • 5 Department of Economics, Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russian Federation
  • 6 Paul Sabatier University, Toulouse, France
Keywords
ANOVA; Gazelle firms; High-tech industries; Logistic regression; Russia
Date of creation
06.07.2022
Date of change
06.07.2022
Short link
https://repository.rudn.ru/en/records/article/record/83989/
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