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Typology of Business Incubators in Spain according to the Stages of Startups Incubation

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15 August 2024

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19 August 2024

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Abstract
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.
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Subject: Business, Economics and Management  -   Human Resources and Organizations

1. Introduction

Startups are engines of social and economic development (Ressin 2022); that influence the reduction of unemployment (The World Bank 1999; (Ferreiro Seoane, Mendoza Moheno, and Hernández Calzada 2018) and poverty (Camayo et al. 2017), and favour investment, the growth of the local economy and the improvement of the quality of life (Ballering and Masurel 2020; Xu et al. 2021). Currently, small and medium-sized enterprises (SME) represent 90% of the business fabric in developed countries, a figure that increases to 95% in developing countries. In addition, startups are strategic in innovation, technological advancement, and the viability of ventures ( Shehada et al. 2020; United Nations 2024).
Entrepreneurship support during the incubation stage constitutes a priority activity in business incubators and means a strategic factor in the success of the venture and its sustainability (Martínez-Martínez 2022). This phase favours the creation of value, technology transfer, the promotion of innovation, cluster development and the coordination of participation of universities, research institutes and the business community ( De la Hoz-Villar and Prieto-Flórez 2020).
There are multiple nomenclatures to refer to business incubators (BI); such as germinators, business hotels, business incubators, business boutiques, entrepreneur centers, innovative business centers, entrepreneurship centers, business innovation centers, new business centers, business promotion centers, business development support centers, business schools, etc. (Flores-Bueno and Jerez 2023; Ayyash, McAdam, and O’Gorman 2022). In each case, depending on the country, the socioeconomic context, and other factors, there will be differences between them. However, business incubators are frequently non-profit entities (Blanco Jiménez et al. 2023) aimed to support entrepreneurs from the initial idea to their full establishment in the market.
Business incubators (BI) showed high heterogeneity among them. We have found a great diversity of entrepreneurial ecosystems; with marked differences in the technology use, knowledge capital (Kapasi and Galloway 2019); the availability of spaces of knowledge, resource heterogeneity and capital formation (Kitagawa and Robertson 2012 its university orientation, with two competing narratives, commercial and educational (Nicholls-Nixon and Valliere 2020); van Weele et al. (2020) indicated that the heterogeneity of BI is conditioned by the availability of tangible and intangible resources. In this sense Ayyash, McAdam, and O’Gorman (2022) provided a more nuanced understanding of the heterogeneity that underlies the organizational form referred to as a BI.
Business incubator classification helps optimize performance by aligning client selection criteria with program objectives, uniqueness of ideas, and standard selection tools ensuring the startups receive the right support to thrive (Ramar and Magheswari 2023). Furthermore, business incubators play a vital role in economic development by supporting startups, providing resources and facilitating financing, which in turn contributes to job creation and wealth generation (Shekapure et al. 2024). Besides, the efficiency of economic development relies on the improvement of business infrastructure, where business incubators serve as a key element, offering diverse support to businesses at different stages of development (Almeida et al. 2021). Apart from this, business incubators are seen as future educational and learning centers, nurturing small businesses and preparing young people for the challenges of the global market, emphasizing the importance of their classification for tailored educational and training programs (Diawati and Sugesti 2023).
Several references on business incubators were found (Centro de Información y Red de Creación de Empresas), although few were focused on the phases that make up business incubators and the business model (Breivik-Meyer et al. 2020; Niammuad et al. 2013). To deep knowledge of it is of great interest as the incubation phases could be associated with the viability of the ventures (Paoloni and Modaffari 2022). In this sense, when it comes to incubator business models, the nuances of the value creation perspective are largely ignored and treated as a “black box” (Gómez 2002).
This paper outlines a means by which the heterogeneity of business incubators can be quantified according to the diversity that can be viewed. The main purpose is offering a new perspective for the study of BIs that focuses on their heterogeneity during the incubation process.
The EU (DG XVI) defined business incubators as public and private interlocutors, which develop a complete and integrated system of activities and services of excellence for small and medium-sized enterprises, with the aim of creating and developing innovative activities (Morales et al. 2019).
Business incubators provide entrepreneurs with information, advice, management guidance, accommodation spaces; whether shared (pre-incubator or coworking) or individual (office), training programs, networking, events and connection activities, networks of mentors and other resources, and have become an important element of the business ecosystem, contributing greatly to the generation of value (Cabrera Soto and Souto Anido 2023; (Vaz, de Carvalho, and Teixeira 2022).
A strategic objective of the incubators was to facilitate the ideal framework for the creation, development, and maturity of business initiatives. They provide services to their clients and configure an ecosystem that increases the chances of success and venturer’s survival. Through incubators, companies receive training, advice, technological and financial links at their initial stage, a time of greatest vulnerability (De Esteban et al. 2022). In addition, the incubator promotes the culture of innovation, business promotion and the training of new entrepreneurs. (Chaves-Maza and Fedriani 2022).
The success of a BI is measured by the survival rate of the companies incubated or hosted in it (Díaz, Anido, and Martínez 2019). According to the Small Business Administration in the United States (2008) the success rate of incubated companies was 80%, while this percentage dropped to 38% in startups not incubated in a BI (Díaz Macías and Mora Macías 2019). In this sense, ventures linked to incubators showed a greater probability of survival (Paredes, Peñaloza, and Rivera 2020). According to the National Business Incubation Association (NBIA), BI is an efficient and dynamic process that provides managerial help, aimed at obtaining economic resources and exposure to “Critical Business” that reduces between 10% and 15% the failure rate in the early stages of the company (Díaz Macías and Mora Macías 2019).
Several authors considered the role of business incubators to be positive in the economic transformation of territories (Paredes, Peñaloza, and Rivera 2020; Paz 2022). In addition to promoting the strategy of support lines for entrepreneurs, they also function as a canter of attraction, retention, and expansion of companies. BI develop services for entrepreneurs and act as a focus for innovation of new projects, products, and services. The coordination of the different BI favours an efficient system of aid to entrepreneurs that guarantees a dynamic and sustainable flow (Ramírez, González, and Tene 2020).
The detailed description of the bibliography about the BI stages, shown below, helped us identify those key variables of each incubation phase, for later use in the survey and in the construction of the typology. According to Blanco Jiménez et al. (2023); Lúa, Martínez, and Fontes (2020), and Rudawska (2020) there are different phases that the entrepreneur goes through in the business incubator: Phase 1. Initial advice, First contact. Doubts are resolved, the idea is presented, the resources available are optimized, etc. It is accessed through the appointment service. Phase 2. Pre-incubation, in this phase the business plan is carried out in an interval of 4 to 6 months and with a technical advisory team. On the one hand, in the early stage of launching a project is where the idea is generated, the business model and the value proposition are defined. On the one hand, in this stage the commercial and business opportunities are assessed (Zuluaga and Morales 2016), but on the other hand, in this phase the abandonment rate is higher (Hackett and Dilts 2004).
Once the pre-incubation phases and the Business Plan are completed, the “birth of the entrepreneurship” begins, where it reaches a legal entity to operate in the market. Incubators facilitate the establishment of the company, streamlining administrative procedures, with a reduction in time and costs (Hausberg and Korreck 2020). After the establishment of the venture, the Incubation phase begins. Incubation is divided into two phases, depending on the services provided by the incubator: basic and advanced incubation. Phase 3. Basic Incubation, in this stage the incubator provides spaces (coworking), infrastructure, tools, resources and contacts necessary for the creation and development of products and services. Phase 4. Advanced Incubation, the incubators provide additional services to those of the previous phase, such as: training, networking, participation in events, connection activities, mediation, testing laboratories, among others. During this phase, agreements with partners and strategic partners are promoted; both for financing, scaling of production and internationalization of the venture. The services provided by business incubators are variable depending on the type of services, the clients, and the structure of the organization (Deyanova et al. 2022).
Business incubators were differentiated by the services offered; from those focused-on technology, to others focused on business development (Antunes et al. 2020). However, all of them provide active support to entrepreneurs through training, administrative support, office space and infrastructure, technology transfer, assistance to help reduce time to market, consulting services, networking, and the funds needed to help grow the new business (Kasanagottu and Bhattacharya 2018). Since COVID 19, virtual business incubators have proliferated, which support the entrepreneur without providing a physical space to carry out their activity (Handoyo et al. 2021; Reit 2022 ).
Finally, the startups will abandon business incubators, and the incubation process will be assessed through the viability of the company. Graduation rate is the response variable most frequently used to measure incubators’ success (Tang et al. 2021).
The effect of business incubators on the ventures’ success has been widely analyzed, but there is a gap regarding the role played by the incubation phases. In this sense Alayoubi et al. (2020) related the effect of knowledge of strategic objectives on the achievement of technological innovation at the Palestine Technical College. The reported results indicated a strong positive increase in the strategic requirements and the innovation achieved (leadership, pioneering thinking, pioneering culture, strategic resource management).
Shehada et al. (2020) were focused on how to improve the performance of business incubators in the Gaza Strip. Owda et al. (2019) identified personal variables and their effect on promoting job creation in the Gaza Strip through business incubators. In this case, the researchers analyzed 92 projects located in business incubators in the Gaza Strip, addressing the study of gender and technical knowledge.
Benavides-Sánchez et al. (2023) studied business incubators and the role developed by universities as a catalyst between student entrepreneurs, teachers, researchers, and investors. The need was found to build multidisciplinary work teams, with collaborative work networks.
Habiburrahman et al. (2022) delved into the concept of incubators and identified critical success factors, such as synergistic products, processes, innovation management, communication, culture, experience, information technologies, innovation skills, functional and implementation skills. The eleven factors were similar in incubators and startups, although with a different order of priority.
Consequently, business incubators constitute key elements in the creation of firms and are fundamental for business development (Mecha-López and Velasco-Gail (2023); Leal et al. 2023), sustainable development (Ballering and Masurel 2020), and territorial cohesion (Toril and de Pablo Valenciano 2009; Gail and López 2021; Mas-Verdú et al. 2015). Likewise, it is complex to quantify the success of business incubators, because there are different metrics, indicators and approaches, such as: business innovation (Lian 2020), efficiency (Ramírez González 2017), performance (Al-Mubaraki and Wong 2011), the entrepreneur’s perspective (Antonovica, de Esteban Curiel, and Herráez 2023), among others.
However, there are few studies that analyze business incubators with the incubation phases approach (Olmedo et al. 2023; Velasco Uribe 2020). In this research we considered the business incubators in Spain globally, assuming their diversity, and their stage of development. Their grouping according to their variability and the incubation plan they are developing is of great interest; both for the development of in situ improvements and for the development of specific sectoral policies that enhance their development (Arribas, Novales, and Vicente 2021) and improve survival or graduation rates (de Esteban Escobar 2020).
The objective of this work was to classify business incubators in Spain according to their heterogeneity during the incubation process phases. Therefore, this research seeks to deepen knowledge of the stages that startups go through in business incubators, its organizational model, and which the differentiating factors were. For this purpose, a typology of business incubators in Spain was developed in relation to the incubation phases of the startups, and they were subsequently characterized.
The business incubators were classified according to the indicators of the different phases of the incubation process: 1: Spreading Entrepreneurship; Phase 2: Pre-incubation; Phase 3: Basic-Incubation and Phase 4: Advanced incubation. The groups achieved showed high homogeneity within the group and heterogeneity between then.
In a second stage, a characterization of the incubator types was carried out with the graduation variables and other operational variables.
This research will allow to classify the business incubators, grouping the different types of incubators existing in Spain according to its four incubation phases (Spreading entrepreneurship, Pre-incubation, Basic incubation, and Advanced incubation).
Deepen in the knowledge of the variability reasons will allow developing improvement actions acting on the key organizational factors. Additionally, this work will serve to promote appropriate and specific policies and strategies for each business incubator identified group. Likewise, its subsequent characterization favors the association of the groups with the results and the remaining operational variables (dimension, profile, etc).
The variables involved in the classification of business incubators are applicable in any country, so the results could be extended to other scenarios, although modulated by the different socioeconomic context.
After this introduction, this article will be organized as follows: in section 2, the methodology will be presented including the the population, the survey applied, and the multivariate statistical analysis used. In section 3, the results will be described, firstly the typology of business incubators and then its characterization. Section 4 will provide a discussion of the results. To end, section 5 will describe the conclusions, the limitations of the study and future lines of research.

2. Materials and Methods

For the drafting of this work, the surveys that were sent to all the business incubators in Spain were used for the preparation of the Funcas report “The services provided by incubators and accelerators of companies in Spain. Ranking 2022/2023”. Funcas is a center of analysis -a think tank- dedicated to economic and social research and its dissemination, promoting the interaction between the academic sphere and the real economy. It is part of the CECA Social Work.
Funcas has been, for many years, a benchmark in the field of economic forecasting and in the analysis of Spanish and EU public policies. At present, the Foundation is also very active in the field of finance – financial regulation and digitalisation, financial markets... – and in a variety of social issues.
The Funcas ranking on business incubators and accelerators, which has taken ten editions, becomes a benchmark in the analysis of best practices in business incubators and accelerators in Spain.

2.1. Population and Survey

412 business incubators from Spain in 2022 were taken as population (60). 88 business incubators that participated in the Funcas’ report (21.36 % of the whole) were selected. Data were collected by using Funcas’ survey applied in Spain in 2022. The survey included 62 questions relating to the following aspects: operative and general data (20 items), phases of the business incubators (33), and survival’s indicators (9) (Survey applied in Table S1).
This research was carried out in several steps: questionnaire design, data collection, and finally the data analysis. Based on the literature review, key indicators of the business incubators were identified, and a set of variables was collected suitable for answering the research questions. During this stage, inclusion of each item was assessed by the research group.
Subsequently, the questionnaire was applied using the following procedure: The survey was sent to the person in charge of each incubator, including an email and telephone number. In parallel, the manager was contacted, explaining the importance of the questionnaire and how to answer it. Doubts and the interpretation of the items were also solved. From there, a regular relationship usually arises, with constant feedback, improvements to the questionnaire were proposed, important information for both parties that should appear in the final reports was highlighted, among others. Thanks to this relationship, it has been possible to maintain the FUNCAS questionnaire actively over time.
Table 1. Business incubation phase variables.
Table 1. Business incubation phase variables.
Variable Variable description
SPREADING ENTREPRENEURSHIP (SP)
1.Advice Provide an information and advisory service to the general public.
2.Advice_free Offering a service for free.
3.Advice_nb How many events does the incubator perform per year?
4.Events Does the business incubator hold events that aim to spread the entrepreneurial spirit?
5.Nb_y How many events does the business incubator hold per year?
6.Channels Are there channels of information/communication/promotion of services?
7.Publicat_Frec Publication frequency in communication channels.
8.Traing Offering transversal courses and entrepreneurship support courses.
9.Traing_Frec Number of courses offered per month.
PREINCUBATION (PRE)
10.Shar_spac Existence of business pre-incubator or coworking facility
11.Spac_free Existence of free spaces to work
12.Space_req Are there any requirements to enter the preincubation phase?
13.Proj_nb Number of pre-incubated projects per year.
14.Proj_advice Having expert consulting sessions for pre-incubators.
15.Proj_mon There is monitoring of pre-incubated projects.
16.Proj_traing There are cross-sectional training workshops.
17.Proj_Mtime Number of years spent in preincubation stage.
18.PAE Is the business incubator an Entrepreneur Care Point (PAE)?
BASIC INCUBATION (INC)
19.Entry There are selection criteria for access to incubation.
20.Entry_crit Which are the selection criteria for access to incubation?
21.Servic Services included in the rate.
22.Nt_Frec Frequency of networking meetings.
23.C_Frec Frequency of consultancy sessions.
24.Ment_Frec Frequency of mentoring sessions.
25.Mon_Frec Frequency follow-up or monitoring sessions.
26.Traing Offer of training courses adapted to the needs of clients.
27.Traing_nb Number of courses offered per month.
ADVANCED INCUBATION (ADV)
28.Nc_agree Interest groups with which the incubator has an agreement/collaboration agreement.
29.Comp_exp Percentage of hosted companies exporting their products.
30.Comp_fd Number of hosted companies that have raised funding while hosted.
31.Comp_job Average number of jobs generated by the hosted companies.
32.Inc_disc A special rate is offered on technology services or products.
33.Inc_agree Interest groups with which the incubator has an agreement/collaboration agreement.
Table 2. Outcome and operative’s variables of incubators.
Table 2. Outcome and operative’s variables of incubators.
Variable Variable description
GRADUATION (GRD)
34.Agree Does the incubator have agreements to facilitate the installation of companies abroad once outside the nursery? 0, No; 1, Yes
35.Crit Graduation criteria. 1, Non-compliance with objectives and others; 2, Limited period of time; 3 Meeting objectives
36.Com_nb Total number of companies graduated since the incubator opened. 1, <10; 2, 10- 50; 3, 51-100; 4, >100
37.Com_Iv Of the graduate companies, what is the percentage that continues their activity abroad now? 1, <25; 2, between 26-50; 3, 51-75; 4, >76
38.Com_dd Percentage of companies that ceased their activity during their stay. 1, >76; 2, between 51- 75; 3, 26-50; 4 <25
39.Com_fd_Pb Percentage of graduates who have obtained funds/public funding. 1, <20; 2, between 21-40; 3, 41- 60; 4, 61-80; 5, >81
40.Com_fd_pr Percentage of graduates who have obtained funds/private funding. 1, <20; 2, between 21-40; 3, 41-60; 4, 61-80; 5, >81
41.Mon Contact with graduates is maintained. 0, No; 1, Yes
42.Mon_act There are specific actions/initiatives with the graduates. 1, Nothing specific is done or frequent contact with them is maintained; 2, Survival and Evolution Tracking; 3, Networking events between graduates and entrepreneurs/professionals of interest; 4, Trainers/Lowers of Hosted Enterprises; 5, Networking meetings or events between graduates and hosted.
OPERATIVE (GA)
43.Network Belong to a network. 0, No; 1, Yes
44.Offices_nb Capacity of the incubator (Nº of offices). 1, <10; 2, 11-20; 3, 21-30; 4 >30
45.Newslett_Frec Shipping Frequency. 1, Not send; 2, Quarterly; 3, Monthly; 4, Weekly
46.Staff Staff required for daily operations (N° persons). 1, <3; 2, 4-5; 3, >5 persons
47.Expenses Annual operating expenses budget (€/y).
48.Revenues Annual operating revenue budget (€/y).

2.2. Statistical Analysis

In a first stage, 33 business incubators phases’ variables were selected (9 items linked to phase 1 of Spreading, 9 to phase 2 of Pre incubation enterprises, 9 associated to phase 3 of Basic incubation phase and finally 6 items to phase 4 of Advanced incubation). According to Niammuad et al. 2013 the criteria of coefficient of variation higher than 60%, uncorrelated variables and non-linear dependence, for the selection, of the variables were considered. A principal component analysis (PC) was used to reduce the number of variables and summarise most of the variability (Capatina et al. 2023). Based on the partial correlation matrix and the initial PC models, the number of variables was reduced to 23. In this research, 8 factors were selected, the orthogonal varimax rotation was applied to relate more easily the selected variables to the extracted factors. The Bartlett sphericity test and the Kaiser-Meyer- Olkin index KMO>0.7 (Gelasakis et al. 2012).
In a second stage, the business incubators were classified using cluster analysis. Firstly, hierarchical groupings were developed based on Ward’s method, using the Euclidean, squared Euclidean and Manhattan distances. The optimal number of clusters was selected using the Elbow method (Syakur et al. 2018; Shi et al. 2021). The clustering method whose discriminant function correctly classified the highest percentage of cases was chosen, and it generated significant differences in the largest number of original variables.
Additionally, the characterization of the business incubators was carried out using the preliminary variables (Table 2). Quantitative variables (original and adjusted) were analyzed by means of a one-way analysis of variance (ANOVA) and Tukey’s multiple range test. Qualitative variables were compared with the Chi2 test. All statistical analyses were performed using the Statgraphics Centurion versión XVI.1. software https://www.statgraphics.com/download-statgraphics-centurion-xvi

3. Results

3.1. Typology of Business Incubators

  • Principal Component Analysis (PC)
8 factors were extracted, corresponding to an eigenvalue greater than 1 (Niammuad et al. 2013). These factors explained 73.47% of the data variability. Table 3 shows the matrix of factor loadings after rotation. 21 variables were selected in the PC analysis of the 33 business incubators variables, which were exclusively assigned to one extracted factor.
The first factor explained 32% of the variance and grouped variables related to Phase 2 of business preincubation. The variables with a strong degree of association were Pre_proj_mon and Pre_proj_advice. These variables are related to carrying out monitoring and follow-up of the projects, having experts in project development. The variables that showed a moderate link with the factor were the transversal training of the incubator (Pre_proj_traing), the number of projects incubated per year (Pre_proj_nb) and the existence of coworking in the business incubator (Pre_shar_spac). All these variables were linked to phase 2 of the incubators or Pre-incubation.
The second factor picked up 9.91% of the explained variance and grouped 4 variables: two corresponding to Phase 1 of the incubation, relating to the dissemination of information, transversal training, and its frequency (Sp_Traing and Sp_Traing_Frec). The other two variables that made up the factor were linked with basic and advanced incubation. One from phase 3 of basic incubation of startups: number of courses offered per month (Inc_traing_nb) and another from phase 4 of advanced incubation related to the number of agreements with other entities (Adv_inc_agree). This factor was related to training and the degree of collaboration offered by the business incubator.
The third factor explained 6.65% of the variance through variables strongly associated with Phase 3 of incubation, with coefficients greater than 0.7. The variables identified were: Frequency of consulting sessions (Inc_c_Frec), frequency of mentoring sessions (Inc_ment_Frec), and frequency of entrepreneurship monitoring follow-up (Inc_mon_Frec). They are variables linked to the monitoring and control of activity. The first three factors explained 48.7% of the accumulated variance and collected 12 variables.
Factors 4, 6 and 7 collect the variables from phase 1 of the startup incubation and were linked to the spreading entrepreneurship. These three factors only explained 15% of the variability and will be related to spreading. The rest of the factors, from the fourth to the eighth, individually explained a percentage of variance of less than 6% and were associated with one or two variables directly. The values of the variables used in the equation were standardized by subtracting their means and dividing them by their standard deviations. Also, as it is shown in Table 3, the communalities considered as estimators of the proportion of variability in each variable attributable to the extracted factors are shown (Niammuad et al. 2013; Capatina et al. 2023).
  • Cluster Analisys
The scores of the eight factors selected for each of the business incubators analyzed were used as independent variables. This statistical procedure generated 3 clusters from 76 observations provided (Figure 1). The clusters obtained showed homogeneity within the group and heterogeneity between them. The procedure began with observation in separate groups that were subsequently grouped into close pairs to form a new group (Shi et al. 2021). After recalculating the distance between groups, the two now closest groups are combined. This process was repeated until finally reaching the three groups shown in Figure 1. The number of clusters was selected based on the distribution of the data, the experience of the analyst and the congruence of the results (Syakur et al. 2018).
Table 4 shows the scores obtained by the centroids of each group and disaggregated by factors. Likewise, Figure 1 shows the dendrogram. Through cluster analysis, 3 groups were obtained; the first with 16% of the business incubators, the second with 30% and the third with 53% of the cases.
Group 1 is a minority (15.79% of cases); In this group, business incubators were incorporated with very low and negative values regarding preincubation (Phase 2), negative values of training and collaboration (Spreading Phase) and strong negative values regarding the monitoring and control of the ventures (Phase 3). The business incubators included in Cluster 1 would have to focus their efforts on improving the practices included in Phase 2 and 3 of the incubation. It is recommended to start improving services from the variables identified in Table 3.
Group 2 represented 30.26% of the business incubators. They were incubators that show the highest values in Pre-incubation (4.2), Training and collaboration (4.0) and Incubation (1.5). This group was the leading group, although the values were high in these factors, they should focus their efforts on improving Phase 3 and other variables linked to the remaining factors (Table 3).
Group 3 was made up of 53.95% of the cases. With intermediate values between the previous groups. Negative values or values close to zero stand out in the first three components, which are the most relevant (Table 3). The practices associated with the variables of phases 2, 1 and 3 of the incubation form their strategic improvement objectives.
The construction of the typology and knowledge of these groups is necessary to propose specific measures for each of them, seeking their strengths and weaknesses and how to correct their behavior in the most effective and appropriate way (Paz 2022; Niammuad et al. 2013).

3.2. Characterization of the Typology of Business Incubator

31.58% of business incubators had a network to share data and resources, compared to 68.42% that did not offer the service (p < 0.05). Incubators with networks were mainly distributed in clusters 2 and 3 (Table 5). The strength of network connections over time is associated with business benefits (van Rijnsoever 2020).
The variable size of the business incubator (44.Offices_nb) showed significant differences by cluster (p < 0.05) (Table 5). Incubators with the largest size and greatest capacity to host ventures were concentrated in cluster 2 and, secondarily in cluster 3. On the contrary, the smallest incubators correspond to cluster 1 and to a lesser extent a business incubators group of cluster 3. The size of the business incubator is a variable of great importance (van Rijnsoever 2020) and is also related to the survival rate (Klingbeil and Semrau 2017).
The variable frequency of communication between the business incubator and the startups for each cluster (45.Newslett_Frec) obtained significant differences with a high level of confidence (p < 0.001). The startups in cluster 1 did not have frequent communication in 35.53% of the cases and similar behavior in large part of startups from cluster 3 was observed. On the contrary, in cluster 2 the incubators with high (3) and very high frequency of communication (4) were concentrated (Table 5 and Figure 2). According to Paniagua-Rojano (2022) the frequency of communication and the type of communication, according to the theory of dynamic capabilities, was linked to an increase in business results (Bastanchury et al. 2019).
Figure 3 shows the classification of business incubators according to the number of people that make up the operational staff (46.Staff) and the cluster. Significant differences are obtained with a high level of significance (p < 0.001) (Table 5). 77.64% of the incubators were very small size. They had, at most, one person on the staff for their daily operation. On the other hand, the largest incubators, with more than five people for their operational operation, represented 14.47% of the total.
The business incubators belonging to cluster 1 showed very low dimensions with respect to the number of people, while in cluster 2 those with a greater organizational structure were concentrated. These results were significant and are associated with the viability of the startups (Oh, Chang, and Jung 2018; Almeida et al. 2021).
The graduation or survival rate was used in this research as a response variable for the business incubators. Table 5 and Table 6 show the main economic results and the graduation rate of the incubators regarding to cluster. 89% of the incubators declared that the startups housed in their facilities did not generate profits, compared to 10% that did declare profits. Regarding costs, the results reported that 82% of the cases showed high fixed costs, compared to 13% with a predominance of variable costs. The predominance of the variable cost structure was mostly associated with the incubators of cluster 2, in 40% of the sample. The results were according to Lukes et al. (2019).
The graduation rate was evaluated based on the indicators in tables 5 and 6. The first one that showed significant differences (p < 0.05) was the existence of protocolized agreements that facilitate the installation of the company outside the incubator (34.Grd_agree) (Table 5). 67% of the incubators did not have these agreements and were grouped entirely in clusters 1 and 3. On the contrary, the incubators with protocolized agreements were located in cluster 2 and slightly in cluster 3. Authors such as Peña Ramírez et al. (2019) indicated the importance of these agreements for the development and growth of startups.
Table 6 shows how the clusters were characterized based on the general and quantitative graduation variables. ANOVA and Tukey’s multiple range test were used to differentiate means.
The variable 37.Grd_com_Iv was linked to the monitoring of the venture once it leaves the incubator. In 76% of the cases, the startups maintained contact with the incubator, while 24% did not. The results showed significant differences between clusters (p < 0.01) (Table 6). The business incubators of Cluster 2 showed greater monitoring of the startups, on the contrary those of Cluster 1 showed the lowest monitoring rates. Pattanasak et al. (2022) reported the great value involved in keeping contact between business incubators and startups alive and dynamic startups.
Subsequently, the clusters were characterized according to the specific activities developed with the graduates (42.Grd_mon_act). Significant difference between clusters were obtained (p < 0.001). In Cluster 2, incubators predominate with regular meetings and networking events between graduates and residents. Hosted Enterprises training is also frequent. On the contrary, in Cluster 1 and 3 there is a frequent absence of specific meetings between graduates and hosts and an absence of improvement in the training of the activity. This inter pares training is basic for solving problems in better response times and more effectively (Zapata-Guerrero et al. 2021).
Regarding the viability or survival rate of the companies, once they leave the incubator (37.Grd_com_Iv) by cluster, significant differences were founded (p < 0.05). 62% of companies continued their activity and were viable, with a range of values between 3.30 and 3.46 in clusters 2 and 3. This percentage decreases sharply to 40% in the incubators of cluster 1. The variable 40. Grd_com_fd_pr. represented the percentage of startups that have been financed with private funds, both for investment and Venture Capital. Although the percentage is very low (less than 40%), we found in cluster 2 values much higher than the remaining clusters (p <0.05).
Regarding the budget of income and expenses (Table 6) of the business incubators (47.Expenses), significant differences were observed in cluster 2, with very high values and marking the existence of a scale effect. Thus, the average annual budget in cluster 2 was 856,224 euros, a value much higher than that of the other groups (p < 0.05). Likewise, the annual operating income budget in the incubators (48.Revenue) showed significant differences between clusters (p < 0.05). The incubators in Cluster 2 have a higher level of income (scale effect) than the remaining groups and those in group three showed intermediate values between Cluster 1 and 2. In cluster 3 we found business incubators with a low level of expenses and an intermediate level of income. It could indicate the existence of a group of small size but economically efficient business incubators. Results were similar to founded by Funcas´s ranking 2022/2023 (Blanco Jiménez et al. 2023)

4. Discussion

The progress of startups in business incubators was segregated into four phases: Spreading, Pre-Incubation; Basic Incubation and Advanced incubation (Blanco Jiménez et al. 2023) In this research, 33 variables were used that have different effect on the incubators’ success; Spreading (9), Pre-Incubation (9); Basic Incubation (9); and Advanced incubation (6). In addition, a group of response variables were selected, related to the survival rate of the startups (9 variables) and 20 general or operational variables were considered for characterization.

4.1. Variable Identification

Identification of variables was done by using Principal Components Analysis (PC), the factors responsible for heterogeneity during the incubation stage of the startups were identified. 33 initial variables were analyzed on the partial correlation matrix and the preliminary models, in order to reduce the number of variables. The principal components analysis verified, on the one hand, the goodness of each of the proposed variables and secondly, its reduction to eight factors or latent variables that explained 73.47% of the existing variability. The first factor was strongly linked to Phase 2 of Pre-Incubation, the second factor was associated with the variables of Phase 1 of incubation (Spreading) and the third factor was related to Phase 3 of incubation (Basic Incubation). These three factors explained 41.91% of the variance. The remaining factors were mainly linked to phase 1 of Spreading.
First factor obtained, was linked to preincubation variables and was focused on an incubation’s short phase with a duration between 4 and 6 months. However, this factor strongly explained the high variability between business incubators in Spain. On the other hand, it was associated with the graduation rate and survival of startups. In this phase the entrepreneur is carrying out his Business Plan with the support and advice of the business incubator technicians. Entrepreneurs in this phase were also offered expert advice and information on financing sources (Blanco Jiménez et al. 2023).
The appropriate building of the Business plan was positively associated with the survival of the startups and their viability in the market (Gaytán Cortés 2020). The business plan is a tool that makes it easier for organizations to chart a route to achieve objectives, consider obstacles and propose solutions for the development of activities in the future (Pico, Montalvo, and Pallerols 2023). Likewise, the Business plan helps to forecast a contingency plan in the event of possible disturbances (Alonso González 2023; Becerra Ardila, Arenas Díaz, and Aguilera Monroy 2023). Also, the results indicated the importance of a competitive operative staff in this first phase of preincubation of startups.
The second factor obtained in the analysis of principal components was the diffusion of the entrepreneurial spirit (Spreading). According to Funcas (Blanco Jiménez et al. 2023). incubators in this phase constitute a reference for startups, offering expert support, training sessions, social networks, training in tools, among others (Lian 2020). Although it is a priority for entrepreneurs to discover the link between the startup and the incubator and how this alliance contributes to graduation (Blanco Jiménez et al. 2023), it is a phase of information gathering, where the entrepreneur has not started the execution of his project, but has gone to the business incubator to resolve doubts, advice and obtain guidance regarding his business idea (Landeros García et al. Jiménez 2021). At this initial moment, the entrepreneur consults with different advisory services and goes to different incubators, so the appropriate approach to the project and his trust in the know-how of the staff constitutes an element of competitiveness compared to other incubators. Quality of the mentoring provided within these incubators depends on the incubator staff, emphasizing their fundamental role in guiding entrepreneurs in business development and strategy formulation( Kehinde et al. 2024);
Regarding the third factor, basic incubation, according to the Funcas ranking, in this phase, entrepreneurs have already matured their business ideas, studied their viability and, therefore, converted their idea into a business project. It is the go to market phase in which the planned project is carried out ( Paoloni and Modaffari 2022). It is the most critical stage of an entrepreneur in which entrepreneurs must be provided with an especially favourable growth environment, and a series of specific resources and services must be made available to them, which allows them to successfully reach the maturity of the project. This factor is a priority according to the study by Funcas (Blanco Jiménez et al. 2023), however the results of the research relegated it to third place and it only explained 6.86% of the variability.
The variables that made up advanced incubation did not appear to be very relevant in the study of the phases of startups in the incubator. According to Redondo and Camarero (2017); Romero, Leon, And Castellanos (2020); Salas Laime (2021) and Barrios Zarta and Gómez 2021, in the advanced incubation phase, companies develop internationalization strategies, seek new financing, scale up production and enter a growth phase. During the previous phases, pre-incubation and basic incubation, startups face the “valley of death”, a stage in which companies are developing their business project and are not yet solvent, they do not generate enough profits to cover all their costs and that lasts until sales stabilize.
Once this period has passed, around two to three years of life (depending on the economic sector in which the startup operates), the companies begin to scale, so the support of the Business incubators favours the projects, but it is not as definitive for their survival as in the previous stages (Zapata-Molina et al. 2022; Momin, Mehak, and Kumar 2023). At this stage, companies normally need capital to finance their growth or to make the leap into international markets. This capital is not always provided by business incubators, so the importance of the incubator for the survival of the company is not so relevant.

4.2. Bussines Incubators Typology

Three groups or clusters made up the population of Spanish business incubators, which were subsequently characterized with the output variables; both general and graduation. Group 1 (16% of business incubators) was the smallest and had markedly negative values in the centroids with respect to the first three factors. This group represents those incubators with more structural deficits and lower graduation rates. Group 2 (30% of the sample) constitutes the leading group with positive values in all factors and strongly positive in factors 1 and 2. It brings together the largest business incubators and highest graduation rates. The analysis also showed factors where it is necessary to focus improvements. Group 3 (54%) is an intermediate group with positive values in its factors, although low and close to zero. This group is clearly the recipient of improvement policies and, within it, different strategies are developed.
Deepening the knowledge of the typology of incubators is of great interest because it helps in providing to the startups with resources and support adapted to specific needs. For example, technology-based enterprises (ETBE) often use incubators to control costs and access specialized resources. ETBTs change incubators as their needs evolve, showing the gap between needs and services (Yusubova, Andries, and Clarysse 2019). Incubators offer networking services, capital support and training programs, which are essential for the growth of startups. Besides, specialized incubators encourage knowledge exchange, strong networks and greater network communication capacity (Haider 2023). The type of innovation that business incubators generate also varies depending on the type of incubator. For example, university and private business incubators are more likely to foster technological product and process innovations Ramar and Magheswari 2023). Overall, the type of business incubator (whether commercial, social, university or other) plays a critical role in shaping the resources, support and innovation opportunities available to startups and, and, ultimately, it influences their development and success.
By classifying incubators based on the programs they offer, such as fit with program objectives, uniqueness of ideas, and standard selection tools, incubators can improve their performance and attract investors looking for specific attributes according to investment objectives (Kinya, Wanjau, and Odeyo 2021). This ranking can also serve as a certification of startup quality, increasing the likelihood that venture capitalists (VCs) will fund startups that have been incubated in highly prestigious programs (Manconi et al. 2022; Rukmana et al. 2023) Therefore, strategic ranking and networking of incubators not only improves their operational performance but also improves their funding prospects by adapting to the specific needs and expectations of investors and funding bodies.

4.3. Business Indications Characterization

In this phase, the typologies of incubators obtained based on the operational variables and graduation rate were characterized. When the clusters obtained were face up with the operative variables and those related to the success rate, differences between clusters were found.
Business incubators with better graduation results (Group 2 and 3) showed a higher level of network use, marking significant differences with those of Group 1. On the other hand, those incubators located in industrial and technological parks improve the results. On the contrary, those in the rural world or located in cities decrease their results. The determining factors of success in the startups hosted in the business incubators were linked to the success of the pre-Incubation phase, secondly, the dissemination of the entrepreneurial spirit (Spreading) and the third factor was related to the basic Incubation (Phase 3).
Communication variable determined significant differences between clusters and the results. Communication was appropriate in Cluster 2 and Cluster 3, and poor in Cluster 1. Communication was related to the graduation rate. So, an improvement in communication has an impact on improving results and contributes to avoiding business failure (Nair and Blomquist 2019).
These results were in accordance with analysis of relational coordination (de Esteban Escobar 2020) and Tailored Capabilities Bastanchury et al. 2019). In this sense, this analysis is aligned to the study by Wu (2007), who related internal and external networks with the incubator and business growth. It has been reported a positive correlation between networks, performance, facilities offered by the incubator and link with the university (Kiran and Bose 2020).
The dimension showed significant differences between clusters, so that the small incubators are located in Group 1, the medium ones in Group 3 and the large ones in Group 2. Likewise, the dimension was linked to the graduation rates. That is, the larger the dimension, the structure of the business incubators was modified, and the graduation rate was improved (Bastanchury et al. 2019; Oh, Chang, and Jung 2018). Normally, a larger business incubator will have more capital, more staff and possibilities to support a greater number of entrepreneurs, as pointed out by several authors (Alpenidze, Pauceanu, and Sanyal 2019).
The business incubators of Group 2 and some of Group 3 showed high success rates, and were significantly linked to positive values in: a) the existence of agreements for the installation of the company outside the incubator Breivik-Meyer et al. 2020; b) after-sales service or the regular maintenance of contact between the incubator and the enterprise, once it leaves the incubator (Zapata-Guerrero et al. 2021); c) the existence of training and monitoring actions for graduates (Chaves-Maza and Fedriani 2022; Lian 2020); d) the percentage of ventures financed with private funds (Alpenidze, Pauceanu, and Sanyal 2019); e) high rates of continuity of activity once the incubators were abandoned (Díaz Macías and Mora Macías 2019; Paredes, Peñaloza, and Rivera 2020; de Esteban Escobar 2020; Gaytán Cortés 2020).
Classifying business incubators into different categories helps to understanding the evolution of their social and economic relevance. Furthermore, business incubators have demonstrated important social benefits, especially in the context of the COVID-19 pandemic, by promoting business initiatives that contribute to sustainable economic development and social well-being (Ramar and Magheswari 2023). The role of incubators as social innovations is also highlighted by their ability to support business development with a social purpose, as seen in the case of Kompleks Industri Makanan MARA (KIMAR) in Malaysia, which promotes socio-economic empowerment among indigenous populations (Adham et al. 2019).Furthermore, the potential of business incubators as educational and training centers is emphasized, preparing young people with the skills necessary to thrive in the global knowledge economy and addressing the urgent need to create employment (Diawati and Sugesti 2023). Finally, the economic and social profitability of business incubators is recognized, with studies showing that society recovers its investment several times over through taxes, underscoring their financial viability and the need for continuous improvement in their operations. (Sentana et al. 2018). Therefore, typifying business incubators not only helps optimize their functions but also highlights their multifaceted impact on economic growth, social innovation and educational development.
The efficiency of business incubators is closely linked to their ability to adapt to a changing environment. Incubators should consider multi-sector approaches, provide extensive financial and educational support, and align with the digital economy. Furthermore, the organizational model highlights the need for robust information management systems to support decision-making and strategy to improve process efficiency.

5. Conclusions

This research has allowed us to deepen in the knowledge of business incubators during the startup incubation phases.
The first research problem was the high diversity of business incubators and heterogeneity in the organizational process. In this sense, the application of multivariate analysis (Principal components and cluster) in the four startups incubation phases (Spreading, Pre-incubation, Basic incubation, and Advanced incubation) reduced the dimensionality and grouped 74% of the variance into 8 factors. Main components analysis Factor 1 was positively related to Pre-incubation variables, factor 2 was linked to training and collaboration variables within the Spreading phase. Factor 3 was called monitoring and control of activity and was related to variables from phase 3 or basic incubation.
The second goal was to build a typology of business incubators in Spain. The typology favoured the grouping of the business incubators into three clusters. Cluster 1 (16% of the total) collected incubators with great deficit in phase 2, phase 1 and phase 3.
Finally, the types of business incubators were characterized. Small size incubators were predominant, frequently located in rural areas or cities, and have a low graduation rate. Cluster 2 (30%) incubators with a very high graduation rate, and strongly positive values in factor 1 and 2. In this group factor 3 was positive although susceptible to improvement. They are the largest size incubators and were usually located in industrial and technological parks. Cluster 3 (54%) is the majority, with intermediate values. They showed intermediate size and their begins with factors 1 and 2. In this group there are some very efficient incubators, with medium size and high graduation rate.
The results of this work will serve to focus business incubators on the variables and activities that have a greater contribution to the successful development of the entrepreneurs advised and the companies incubated or hosted therein optimizing the resources used and maximizing their profitability.
Furthermore, knowing which group each business incubators is located favors the development of specific public policies according to their problems. Simple factors were also identified that could be modified and lead to an improvement in the graduation rate.
In subsequent studies, it is recommended to deepen in: a) the quantitative knowledge of the relationship between incubation phases and graduation rates b) The relationship between different businesses incubation phases. Both goals could be addressed with statistical techniques such as Structural equation modelling by the partial least squares method (PLS-SEM).

Author Contributions

Conceptualization, A.A. and A.G.; methodology, A.A. and C.D.-P.-H.; software, F.B.; validation, C.D.-P.-H., A.A. and A.G.; formal analysis, A.A.; investigation, A.A., A.G., C.D.-P.-H.; data curation F.B. and A.A.; writing—original draft preparation, A.A., A.G.; writing—review and editing, C.D.-P.-H., A.A.; supervision F.B. and C.D.-P.-H. All authors have been involved in developing, writing, commenting, editing, and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable

Data Availability Statement

This is not applicable as the data are not in any data repository of public access, however if editorial committee needs access, we will happily provide them, please use this email: pa1gamaa@uco.es.

Acknowledgments

This work is part of the results of a joint research agreement between the Rey Juan Carlos University and Funcas within the framework of the contract for the preparation of the report “The services provided by business incubators and incubators in Spain. Ranking 2022-2023”, thanks for all support. We also thank the OpenInnova High Performance Group at Rey Juan Carlos University, and ECONGEST AGR267 Group at Cordoba University for their support during the fieldwork stage, as well as the households in the three zones that shared valuable information about their livestock activities.

Conflicts of Interest

The authors declare no conflicts of interest

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Figure 1. Clusters of business incubators.
Figure 1. Clusters of business incubators.
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Figure 2. Newsletter frequency according to cluster.
Figure 2. Newsletter frequency according to cluster.
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Figure 3. Organizative staff size according to cluster.
Figure 3. Organizative staff size according to cluster.
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Table 3. Principal components (PC) loading matrix of rotated.
Table 3. Principal components (PC) loading matrix of rotated.
Items Loading Eigenvalue Explained variance (%) Acumulate PC
10.Shar_spac 0.61 8.00 32.01 32.01 1
13.Proj_nb 0.68 1
14.Proj_advice 0.82 1
15.Proj_mon 0.83 1
16.Proj_traing 0.65 1
8.Traing 0.62 2.47 9.91 41.91 2
9.Traing_Frec 0.77 2
26.Traing 0.70 2
32.Inc_disc 0.64 2
23.C_Frec 0.76 1.71 6.86 48.77 3
24.Ment_Frec 0.71 3
25.Mon_Frec 0.76 3
6.Channels 0.71 1.37 5.46 54.22 4
7.Publicat_Frec 0.67 4
22.Nt_Frec 0.72 4
19.Entry 0.84 1.36 5.43 59.65 5
20.Entry_crit 0.86 5
1.Advice 0.82 1.26 5.03 64.68 6
3.Advice_nb 0.63 1.13 4.52 69.20 7
18.PAE 0.80 7
11.Spac_free 0.91 1.07 4.27 73.47 8
Table 4. Scores of centroids for each cluster.
Table 4. Scores of centroids for each cluster.
PC Cluster
1 2 3
1 -7.4766 4.1812 -0.1573
2 -8.2974 3.9628 0.2054
3 -6.2827 1.4973 0.9989
4 -5.5285 2.8182 0.03716
5 -2.5355 2.4939 -0.6569
6 -3.5733 1.5787 0.1602
7 -2.7583 1.9444 -0.2835
8 -0.9970 0.4338 0.0485
Table 5. Incubator typology’s characterization according to qualitative variables.
Table 5. Incubator typology’s characterization according to qualitative variables.
Variables Cluster
1 2 3 p-Value1
43.Network (%) 0 10.53 5.26 15.79 *
1 5.26 25,00 38.16
44.Offices_nb 1 9.21 5.26 17.11 *
2 1.32 7.89 22.37
3 2.63 6.58 6.58
45.Newslett_Frec (%) 1 14.47 2.63 18.42 ***
2 0.00 0.00 0.00
3 1.32 1.32 1.32
4 5.26 5.26 5.26
46.Staff 1 1.32 1.32 1.32 ***
2 0.00 0.00 0.00
3 0.00 0.00 0.00
4 14.47 14.47 14.47
34.Grd_agree (%) 1 15.79 15.79 15.79 *
2 15.79 15.79 15.79
41.Grd_mon (%) 1 7.89 7.89 7.89 **
2 1.32 1.32 1.32
42.Grd_mon_act (%) 1 12.00 12.00 12.00 ***
2 4.00 4.00 4.00
3 24.00 24.00 24.00
4 2.67 2.67 2.67
5 2.67 2.67 2.67
1p-Value: *p < 0.05; ** p < 0.01; *** p < 0.001
Table 6. Incubator typology’s characterization according to quantitative variables.
Table 6. Incubator typology’s characterization according to quantitative variables.
Variable Clúster p-Value 1
1 2 3
37.Grd_com_Iv 2.67 a 3.30 b 3.46b *
40.Grd_com_fd_pr 1.75ab 2.30b 1.54a *
47.Expenses 25,056a 865,224b 156,342a *
48.Revenue 36,700a 801,381b 168,867ab *
1p-Value: *p < 0.05
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