The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal component analysis reduced dimensionality to eight factors with a 74% explained variance. Factor 1 was positively related to pre-incubation variables; factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3, or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16% of the total), incubators with strong deficits in incubation phases 1, 2, and 3. They are small-sized business incubators, often located in rural areas or cities, with a low graduation rate. Group 2 (30%), business incubators with a very high graduation rate and strongly positive values in factors 1 and 2. Factor 3, although positive, is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54%) is the majority, with values close to clusters 2 and 3.

This research quantified the relationships among the different phases of the business incubation process. 89 surveys coming from business incubators in Spain in the period 2022–2023 have been collected. A structural equation model (SEM) was applied to determine the association among incubation phases 1, 2, 3, and 4. The results showed that the “spreading entrepreneurship” phase had a strongly positive significative influence on preincubation, phases 1 and 2 (hypothesis 1) basic incubation, phase 3 (hypothesis 4), and advanced incubation phase 4 (hypothesis 5). Besides, a moderate positive influence was found between preincubation and basic incubation (hypothesis 2) and between preincubation and advanced incubation (hypothesis 6). In this context, spreading entrepreneurship will be a useful tool to determine the success of entrepreneurship during the incubation process. Improving variables such as counseling, channels and training will positively impact incubation. Therefore, taking action at the spreading entrepreneurship stage to improve the business incubator results, and evaluate the structural deficiencies of entrepreneurs to improve their training level and technicians' specialization is recommended. Applying SEM models in business incubators to evaluate their influence on graduation rates would also be of great interest.

Business incubators contribute to the growth of a country, and it is of great interest to deepen knowledge of the impact of incubation phases on the results of incubators to evaluate the effectiveness of developed incubation programs. The objective of this research was to propose a model that quantitatively related different incubation phases to the graduation rate of business incubators in Spain. A sample of 88 incubators was obtained. The survey included 42 items identified in different phases (spreading entrepreneurship, 9 items; pre-incubation, 9 items; basic incubation, 9 items; advanced incubation, 6 items; and graduation, 9 items) and four hypotheses relating to the existence of a positive influence from the startup incubation phases on the incubators results. These were validated by using a structural equation model (SEM) with five latent variables. Three of the four proposed hypotheses that linked startup pre-incubation (H2), basic incubation (H3), and advanced incubation (H4) with graduation rates in Spanish incubators were accepted. These startup incubation stages showed a positive influence on the startup graduation rate. The advanced incubation stage had a very strong relationship with the graduation rate (β = 0.543). Furthermore, a strong indirect effect between business incubation and the graduation rate, explaining 71% of the success of the incubators, was found. Proposals for improvement in each incubation phase to enhance the results of the business incubators are provided. Furthermore, future challenges that should be incorporated into the development of incubator programs, such as the social focus, the implementation of a training and monitoring model, an increase in network businesses, the internationalization of incubators with a globalized approach, the sustainability of the startup’s approach, and the transfer focus, are raised. Given the high variability of Spanish incubators and the wide sampling range, the model could be extended to other contexts with similar behavior within the sample range.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3 or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small sized business incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factors 1 and 2. Factor 3, although positive, it is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.