Artikel

Post-pandemic performance of micro, small and medium-sized enterprises: A Self-organizing Maps application

This paper examines the post-pandemic performance of micro, small, and medium-sized firms using Self-Organizing Maps (SOMs), a type of Artificial Neural Network that groups patterns based on their similarities. The goal is to identify the key characteristics that enable firms to face market changes and overcome the effects of the global COVID-19 pandemic crisis. Considering business failure theory, a set of critical factors (including internal production processes, firm age, number of employees, resilience, financial resources, commercial strategies, management, and the impact of external factors) is used to assess the performance of Argentinian firms. The study categorizes these firms into four clusters based on their patterns. The results reveal a trade-off between a firm's age and its number of employees, confirming that younger firms with fewer employees, limited financial resources, relatively weaker management, internal production process issues, and lower resilience tend to perform poorly, despite facing fewer impact of external factors. Consequently, the findings emphasize the significance of internal fundamentals and resilience in achieving success or avoiding failure. This highlights the effectiveness of SOM as a tool to visualize the characteristics that lead to successful paths and the survival of firms.

Language
Englisch

Bibliographic citation
Journal: Cogent Business & Management ; ISSN: 2331-1975 ; Volume: 10 ; Year: 2023 ; Issue: 3 ; Pages: 1-13

Classification
Management
Neural Networks and Related Topics
Bankruptcy; Liquidation
Firm Performance: Size, Diversification, and Scope
Business Economics
Subject
business failure
business performance
COVID-19
SMEs
SOM

Event
Geistige Schöpfung
(who)
Martinez, Lisana B.
Scherger, Valeria
Orazi, Sofía
Event
Veröffentlichung
(who)
Taylor & Francis
(where)
Abingdon
(when)
2023

DOI
doi:10.1080/23311975.2023.2276944
Last update
10.03.2025, 11:42 AM CET

Data provider

This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Martinez, Lisana B.
  • Scherger, Valeria
  • Orazi, Sofía
  • Taylor & Francis

Time of origin

  • 2023

Other Objects (12)