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Exploring the dynamics of high-growth determinants for gazelle companies using interpretive structural modeling

Margarida P. Santos (ISCTE Business School, University Institute of Lisbon, Lisbon, Portugal)
Fernando A. F. Ferreira (ISCTE Business School, BRU-IUL, University Institute of Lisbon, Lisbon, Portugal) (Fogelman College of Business and Economics, University of Memphis, Memphis, TN, USA)
Neuza C. M. Q. F. Ferreira (NECE-UBI, Research Center for Business Sciences, University of Beira Interior, Covilhã, Portugal)
João J. M. Ferreira (Department of Management and Economics, NECE-UBI, Research Center for Business Sciences, University of Beira Interior, Covilhã, Portugal) (QUT Australian Centre for Entrepreneurship Research, Brisbane, Australia)
Ieva Meidutė-Kavaliauskienė (Vilnius Gediminas Technical University, Vilnius, Lithuania) (BRU-IUL, University Institute of Lisbon, Lisbon, Portugal)

Journal of Small Business and Enterprise Development

ISSN: 1462-6004

Article publication date: 28 March 2024

Issue publication date: 27 August 2024

187

Abstract

Purpose

Gazelle companies are characterized by rapid growth in a short time. Identifying the determinants of this exponential expansion is important as these firms have a significant impact on the economy. They generate increased employment and investment by investors interested in new opportunities. Previous studies have failed to reach a consensus about what fosters high growth in gazelle companies as each firm’s geographical, political and economic context is different. The present research uses cognitive mapping and interpretive structural modeling (ISM) to overcome the limitations of prior investigations and identify factors that can potentially accelerate growth in gazelle companies.

Design/methodology/approach

Two sessions were held with an expert panel with knowledge about and experience with these firms. In the first session, data were collected to create a group cognitive map, while the second meeting comprised ISM-based analyses of the high-growth determinants identified and the causal relationships between them. A final consolidation session was held to discuss the results with two members of the Committee for Central Region Coordination and Development (i.e. Comissão de Coordenação e Desenvolvimento Regional do Centro – a public entity that grants gazelle awards in Portugal).

Findings

The analysis system created was tested, and the results demonstrate that the dual methodology used can increase our understanding of the dynamics of high-growth determinants and lead to more informed and potentially better evaluations of gazelle companies. Indeed, once high-growth determinants in gazelle companies are understood, this information can help other firms implement the same business model to achieve similarly rapid growth. The strengths and shortcomings of this new structured analysis model are also analyzed.

Originality/value

The authors know of no prior work reporting the integrated use of cognitive mapping and ISM in this study context.

Keywords

Acknowledgements

This work was partially funded by the Portuguese Foundation for Science and Technology (Grants UIDB/00315/2020 and UIDB/04630/2020). Non-confidential information related to this study can be retrieved from the corresponding author upon request. We are grateful to the expert panel members: David Perrolas, Diogo Mota, Filipe Resende, Pedro Pinto, Sebastião Carvalho e Silvia Caetano. The authors also would like to express their gratitude to Carla Coimbra and Raquel Martins, members of the Committee for Central Region Coordination and Development, Portugal, for their availability and relevant insights provided during the consolidation phase of the results.

Citation

Santos, M.P., Ferreira, F.A.F., Ferreira, N.C.M.Q.F., Ferreira, J.J.M. and Meidutė-Kavaliauskienė, I. (2024), "Exploring the dynamics of high-growth determinants for gazelle companies using interpretive structural modeling", Journal of Small Business and Enterprise Development, Vol. 31 No. 5, pp. 905-928. https://doi.org/10.1108/JSBED-11-2023-0534

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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