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

Abstract

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.