Analysis of the impact of Incubators on startup performance: a comparative study in the Italian context

The objective of this research was to carry out an examination of a sample of both incubated and non-incubated startups with the aim of discovering which is the impact of the incubation process. In particular, the study aimed to discover the characteristics, behaviors, and performance metrics that differentiate these two categories. Additionally, the research wanted to verify whether the observed behaviors and outcomes align with or deviate from the established state of art and prevailing trends within the startup ecosystem. The first phase of this research was the collection of contact information of a list of about 14.000 enterprises. The following step was the creation of a survey to be sent to firms to understand if they were incubated or not. The list of startups which answered to the survey was the starting point to carry out the analysis. The initial part of the analysis focused on the predominant sectors of the startups, which are Information and Communication Services (41%), Professional, Scientific and Technical Activities (25%), and Manufacturing (19%). The second part regarded the geographical distribution, and the five Italian regions where there is a higher number of startups are Lombardia (30%), Lazio (11%), Veneto (10%), Emilia Romagna (9%) and Campania (7%). Moreover, of all incubated ones, the 58% is in the North of Italy. This underlines the need for support and initiatives in the southern regions to bridge the existing gap and promote innovation in a more balanced way in the country. The third part of the analysis is about the growth of the number of employees. The data reveal that most of startups have a relatively small workforce, typically of 0-5 employees. The similarity in the distribution of team sizes between incubated and non-incubated startups suggests that both groups recognize the advantages of maintaining lean teams. Incubators may focus their support on enhancing the capabilities and resources of these small teams rather than significantly altering the team size. The last part is the analysis of revenue growth rates. Most of them are in the range of 0 to 95, with 67% of incubated startups and 57% of non-incubated ones belonging to this category. Furthermore, 10% of incubated startups and 11% of non-incubated ones, have negative growth rates, which could be attributed to various factors such as market challenges, operational impediments, or other obstacles. In the range of values from around 100 to around 1.000 there are 20% of incubated startups and 26% of non-incubated startups. Moreover, when examining values exceeding around 1.000, we observe that only 2% of incubated startups reach these levels, while there is 6% of non-incubated startups achieving this growth. This might suggest that non-incubated startups are surviving in specific markets, achieving substantial growth without the support of incubation. In conclusion, it was not possible to formulate entirely new assumptions or generalizations about the impact of incubators on startups due to the constraint of having a relatively limited number of startups that participated in the survey, which made the outputs not statistically significant enough. Instead, the analysis made it possible to verify that the trends observed within this limited dataset aligned with the findings documented in the existing literature.