Structural Equation Models to Determine the Relationship Between Startup Incubation Stages and the Graduation Rate of Incubators in Spain

Abstract

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.

1. Introduction

Small and medium enterprises (SMEs) account for 99% of all companies in Organization for Economic Cooperation and Development (OECD) countries, according to an assessment by Corporation Malaysia in 2018, and generate between 50 and 60% of their added value. Consequently, microenterprises and SMEs’ growth and survival problems have become increasingly important to society [1]. To grow and position themselves strategically, startups need capital, access to the market [2,3], and an appropriate incubation ecosystem [4]. Business incubators are tools that support the creation of new companies—providing legal support, networks, support for their development, platforms to build a customer-based environment, and greater security—and create perceptions of lower risk, among other services [5].

There is a close link between BIs and sustainability since incubators are ecosystems that encourage the creation of startups and the promotion of sustainable practices [6,7]. BIs develop several sustainable actions, which include promoting sustainable businesses [8] (OIT 2021); improving efficiency by applying the principles of bioeconomy [9,10,11] and circular economy models [12,13]; the use of green financing [14]; the promotion of environmental values and sustainability principles [15]; and the development of innovations that contemplate the triple-R rule (reduce, reuse, and recycle) [16,17]. On the other hand, BIs contribute directly to the achievement of sustainable development goals (SDGs). They stimulate economic growth and reduce poverty in developing countries based on the endogenous resources of the area and the training of entrepreneurs (SDGs 1 and 8) [18]. The birth of startups in BIs is based on training (SDG 4) [19]) and open innovation (SDG 9) [20]. In addition, economic development based on the actors themselves constitutes a solid and effective tool to fight for gender equality (SDG 5), reducing inequalities and the exclusion of the vulnerable (SDG 10). According to [21], in developing countries, improving the climate, reducing poverty, and inclusion policies for the most vulnerable (SDG 10 and 13) are strongly linked to the development of local entrepreneurs [22,23].

In different countries, startup incubators showed great heterogeneity among themselves due to multiple factors such as sociodemographic context, size, orientation, etc., according to [24,25]. They were also differentiated by the services they offered startups, such as infrastructure, with shared office facilities designed to provide incubators with strategic intervention systems of added value for monitoring and business assistance [26]. Both educational and practical services are essential to allow the transformation of business ideas into concrete business projects, as explained. Additionally, networks of individuals and organizations available in business incubators are important, including incubator managers and staff with marketing specialists, venture capital accessibility, interactions with ecosystem companies and incubator employees, industry contacts, professional services, mentoring/coaching, local universities, volunteers, etc. In this sense, in Spain, SMEs face major challenges, such as a lack of financing and technical knowledge, and rely on incubators to overcome these obstacles [27]. Recently, the resources invested in promoting the creation, growth, and impact of emerging enterprises in startup incubators have been increasing [28,29]. These initiatives, according to [30], include business education and access to laboratories and office spaces, as well as technology transfer and business development services provided through government programs, incubators, and accelerators [31,32].

Startup incubation occurs among business incubators in sequence with different phases according to [33] and, subsequently [34,35,36,37]. According to [38,39,40], there are different phases that the entrepreneur goes through in the business incubator: entrepreneurial spreading (phase 1), preincubation (phase 2), basic incubation (phase 3), and advanced incubation (phase 4). In phase 1, doubts are resolved, the idea is presented, and the available resources are optimized. During the dissemination phase of the venture, entrepreneurs receive initial advice and have their first contact with the business incubator. According to [41], in phase 2, the objective of preincubation is for the entrepreneur to know the risks of the business and the main KPIs of their activity. During this period, the general capabilities of the budding entrepreneurs are improved, and the gap between academic knowledge and the necessary business skills is reduced. In this phase, the implementation of preincubation practices favors a reduction in the time needed for the development of the business plan [42]. In phase 3, the BI provides spaces (coworking), infrastructure, tools, resources, and contacts necessary for the creation and development of products and services [41]. In the advanced incubation phase [41], additional services are provided, such as training, networking, participation in events, connection of activities, mediation, and testing laboratories, among others.

Table 1. Summary of the literature review.

Phases	References
Entrepreneurial spreading (phase 1)	[7,43,44,45,46,47,48,49,50,51]
Preincubation (phase 2)	[42,52,53,54,55,56,57,58,59,60,61]
Basic incubation (phase 3)	[56,62,63,64,65]
Advance incubation (phase 4)	[56,66,67,68]

shows a summary of the literature review for each of the phases that were used as a theoretical framework for the proposal of the hypotheses of the research work, where an improvement in incubation phases will contribute positively to the success of the startup graduation rate [34,35].

In this sense, the authors of [69] indicated that the quality of business incubators and the services they offered had a positive impact on entrepreneurship. The effectiveness of business incubators is essential to guide startups toward survival, growth, innovation, and success [70]. The authors of [71] reported that technological business incubators had a significant positive impact on the success of startups and that factors such as coaching, business networking, digital social networks, and entrepreneur profiles facilitated the acquisition of competitive advantages. The existence of incubators for technological businesses influenced the existence of emerging entrepreneurships, and we observe superior levels of existence in those incubators of greater quality [72].

The literature referring to successful incubation is insufficient. The success of incubation can be measured by the graduation rate of those incubated [73], the number of jobs created, the ecosystemic connections created, and the rate of use of business support [74]. A commonly used metric is the startup survival rate, which indicates the percentage of incubated companies that remain operational after a specific period. This metric indicates the effectiveness of the incubator at a critical time to help startups: during their initial phases. Another indicator is the graduation rate, referring to the percentage of startups that successfully complete the incubation program and graduate. This is a crucial indicator in the field of incubators focused on social entrepreneurship and social impact and is related to influence in the community and the resolution of social problems. This rate is emerging as a fundamental indicator [75]. Finally, financial performance indicators encompass specific financial metrics such as return on investment (ROI), profitability, and other financial indicators that demonstrate the economic viability of incubated startups [76].

We also identified socioeconomic factors that differentiate the success rate between countries and types of incubators. In Pakistan, the National Incubation Center (NIC) in Lahore provides a comprehensive ecosystem for entrepreneurs, but challenges such as a lack of networks and financial support affect the experiences of female incubated graduates [77]. In China, the effectiveness of tech business incubators (TBIs) is highly dependent on the socioeconomic context of the city, with first-tier cities benefiting more from technical support and mentorship, which positively influences graduation performance [78]. In Eastern Europe and Central Asia, the importance of local context in assessing success is emphasized [79]. In Germany, data on the long-term performance of graduating companies from incubators indicate that graduation does not necessarily equate with sustained growth, which casts doubt on the hypothesis that incubation leads to long-term success [80]. Performance indicators used in Europe, such as the number of graduated companies, highlight the role of incubators in fostering entrepreneurship but also point to variability in rates of success (depending on services and sponsorship) [81]. The PROMETHEE method applied in Germany highlights the complexity of assessing incubator efficacy and suggests that graduation rates should be combined with other performance metrics [82]. In the USA, incubators significantly influence the success of startups through funding and mentorship [83]. A survival analysis of technology-based companies in Brazil indicated that while graduation is a milestone, the risk of failure after graduation remains high, highlighting the need for continued support beyond incubation [84]. Overall, while graduation rates are a useful indicator, they should be contextualized based on broader performance indicators and local conditions to accurately assess the success of business incubators [85,86].

In previous research, the authors of [87] quantified the relationships between the different phases of the business incubation process. The results obtained showed that the “entrepreneurship diffusion” phase had a significant and strongly positive influence on preincubation, basic incubation, and advanced incubation, although the impact of each phase on the success of the ventures was not evaluated. Therefore, deepening knowledge of the impact of incubation phases on the results of business incubators to evaluate the effectiveness of incubation programs is the main interest of this work. The objective of this research was to propose a model that quantitatively evaluated the different incubation phases (spreading, pre-incubation, basic incubation, and advanced incubation) and the success of the startup incubators (graduation) in Spain using structural equation modeling (SEM).

The relevance of our study lies in the proposal and validation of an empirical SEM model to determine the impact of each incubation phase on the startup’s success and, consequently, the usefulness of the programs developed by business incubators in Spain. In addition to providing information about the model per se, the results have great value for managers of business incubators, in terms of focusing their actions to increase the success rate. Given the heterogeneity of Spanish business incubators and the wide sampling range, the model could be extended to other BIs with similar contexts that behave within the sample range.

This article is structured as follows: after this introduction, where the role of business incubators, the link between startup incubation and the business incubator results, and the metrics frequently used to measure the success rate of the incubator are reviewed, the formulation of the hypotheses is finally proposed. In Section 2, the sources of information, the sample, and the data collection instrument used are described. In Section 3, the fit of the model is shown. Finally, the discussion and conclusions are presented.

Hypothesis Approach

In this research, our initial hypothesis was that there was a positive influence between the startup incubation phases and the incubators’ results. Each direct effect was quantified by a hypothesis (Figure 1). With this goal, a structural equation model (SEM) with latent variables was constructed [88,89]. According to [90,91,92,93], the use of the structural equation model is an appropriate methodology to deepen knowledge regarding the causal relationship between the indicators of business incubators.

The graduation rate was used as a response variable in the model (Figure 1). This construct incorporated nine observed variables. In this research, for startups to be considered graduated, three conditions were established: that they had overcome the incubation phases in the BI [94]; that they had gone through the critical period referred to as “the valley of death” [95,96]; and finally, that they were “live ventures”—that is, with sufficient economic activity outside the BI in the short term [97,98]. Generally, these were ventures with more than four years of seniority in the BI. Based on the theoretical framework, four hypotheses were established.

Hypothesis 1. 

The spreading entrepreneurship variable (Spread) has a positive impact on the graduation rate (Grad) in startups incubated in Spain. The promotion and dissemination of the entrepreneurial spirit in business incubator programs is a determining factor for those ventures that aspire to achieve a successful graduation, which, in turn, is essential for their survival [49,73]. The relationship between the diffusion of entrepreneurship and the graduation of enterprises has a multifaceted focus. Business incubators provide information, advisory services, and other services, such as free or low-cost spaces, which are essential for the creation and promotion of new sustainability businesses. In particular, university-linked business incubators are instrumental in promoting entrepreneurship among students, graduates, and staff by creating an enabling environment for the development of entrepreneurial skills needed in entrepreneurship, according to [39,99].

Hypothesis 2. 

The preincubation stage (Pre_Inc) has a positive impact on the graduation rate (Grad) of entrepreneurships in Spain. Preincubation prepares entrepreneurs to face market challenges, thus increasing the likelihood that their companies will successfully graduate by receiving support to transform their initial ideas into viable business plans, technical assistance, business management training, workshops and mentoring, development of strategic plans, etc., and, consequently, helping to minimize the risk of failure [75,94,100]. The researchers indicated that at this stage, entrepreneurs’ perceptions of the performance of incubators had significant influence on the startup’s viability, enabling conditions necessary for business success. Suitable support for the business incubator in this phase reduces the perception of risk for the entrepreneurs, promoting the development of a more solid business mentality and helping entrepreneurs face related challenges [95,96].

Hypothesis 3. 

The basic incubation (Incub) phase has a positive influence on the graduation rate (Grad) of startups. The incubation phase is critical in providing the necessary support that allows companies to meet the exit criteria and graduate successfully [34,35]. According to [97,98], the basic elements of incubation, such as the social capital of the incubator, business management skills, networks, and support in the administrative and fiscal discharge of the company, positively influence the graduation phase of the tenants of the incubators. In this stage, entrepreneurs are provided with advanced training, networks, and meaningful relationships that contribute to their development, and innovation processes are supported. Together, these attributes foster the development of an environment that supports the development; growth; and, ultimately, graduation of startups. According to [65], basic incubation produced relevant differences in the case of university incubators. The authors of this work applied dynamic capabilities to explain how universities create and capture value in the process of university technology transfer (UTT). The dynamic capabilities perspective provided a deeper understanding of the interactions between universities and their environments, on one hand, and the improvement in UTT outcomes.

Hypothesis 4. 

The advanced incubation stage (Ad_Inc) has a positive effect on the graduation rate (Grad). Researchers, such as the authors of [101,102], have studied the advanced incubation phase, characterized by comprehensive support services such as financing, technical assistance, and business mentoring in topics such as the internationalization of companies and the advanced mentoring service. Their work has shown that these services positively influence the graduation phase of incubated companies [35].

2. Materials and Methods

2.1. Population and Sample

This research was carried out based on the results of the questionnaire on business incubators in Spain conducted by Funcas in 2022/2023 [41,87]. Funcas is a think tank and a center for analysis dedicated to economic and social research and its dissemination, promoting interaction between the academic sphere and the real economy.

In Spain, the average income in 2021 by BI was EUR 18,126, an amount that rose to EUR 18,891 in 2022 in the case of women and to EUR 19,440 for men. When disaggregating the data by autonomous communities, we found great heterogeneity in per capita incomes (

Table 2. Distribution and ranking of the sample of business incubators in Spain by Funcas in 2022/2023.

Regions	Business Incubators	Sample	Percent (%)	No. of People (in Thousands)	Average Income, EUR (2021)
Andalucía	28	5	17.86	8472	14,769
Aragón	23	7	30.43	1326	19,381
Asturias	16	4	25.00	1012	17,968
Asturias	16	4	25.00	1012	17,968
Islas Baleares	6	1	16.67	1173	16,867
Canarias	29	4	13.79	2173	15,294
Cantabria	5	1	20.00	584	18,659
Castilla León	25	6	24.00	2383	18,181
Castilla La Mancha	34	10	29.41	2050	15,350
Cataluña	85	15	17.65	7763	20,927
Valencia	31	4	12.90	5058	16,474
Extremadura	12	2	16.67	1059	13,908
Galicia	31	6	19.35	2696	16,785
Murcia	14	7	50.00	1518	14,988
Navarra	8	1	12.50	661	22,786
País Vasco	30	4	13.33	2214	22,658
La Rioja	1	0	0.00	320	18,758
Ceuta	0	0	0.00	83	16,159
Melilla	1	0	0.00	86	18,259
Spain	412	88	21.60	47,385	18,126

). In four territories, the average income was exceeded by more than 15%; these territories were Madrid, Navarra, the Basque Country, and Catalonia. On the contrary, at the most disadvantaged end, we found that five autonomous communities had very low incomes—15% lower than the average Spanish income. These communities were Extremadura, Andalucia, Murcia, Castilla Leon, and the Canary Islands.

A total of 412 business incubators from Spain in 2022 were used as the population, as shown in Table 2. A random sample composed of 88 complete questionnaires was collected (21.36% of the total). Incomplete surveys and those that showed logical inconsistencies were deleted. The minimal sample size was calculated with a confidence of 95% (Z = 1.96) and an unknown expected proportion (p = 0.5). Once the information was collected, we observed that most of the regions were sufficiently represented (more than 10%), although three regions appeared without a response (La Rioja and the extra-peninsular cities of Ceuta and Melilla). Therefore, the sample was considered representative of the population of business incubators in Spain. The survey included 33 questions related to the phases of business incubators and 9 items related to the graduation rate (

Table 3. Business incubation phase variables.

Code	Variable
SPREADING ENTREPRENEURSHIP (Spread)
1. Advice	Provide information and advisory service to the general public.
2. Advice_free	Offering a service for free.
3. Advice_nb	Number of information and advisory services provided per year.
4. Events	Number of events per year.
5. Nb_y	Does the incubator hold events that aim to spread the entrepreneurial spirit?
6. Channels	There are 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_Inc)
10. Shar_spac	Existence of preincubator 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 preincubated projects per year.
14. Proj_advice	Having expert consulting sessions for preincubators.
15. Proj_mon	There is monitoring of preincubated projects.
16. Proj_traing	There are cross-sectional training workshops.
17. Proj_Mtime	Number of years spent in preincubation.
18. PAE	Is the incubator an entrepreneur care point (PAE)?
BASIC INCUBATION (Incub)
19. Entry	There are selection criteria for access to incubation.
20. Entry_crit	What 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 (Ad_Inc)
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	The 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.
GRADUATION RATE (Grad)
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, noncompliance 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 currently continue their activity abroad (1, <25%; 2, between 26 and 50%; 3, between 51 and 75%; 4, >76%)?
38. Com_dd	Percentage of companies that ceased their activity during their stay (1, >76%; 2, between 51 and 75%; 3, between 26 and 50%; 4, <25%).
39. Com_fd_Pb	Percentage of graduates who have obtained funds/public funding (1, <20%; 2, between 21 and 40%; 3, between 41 and 60%; 4, between 61 and 80%; 5, >81%).
40. Com_fd_pr	Percentage of graduates who have obtained funds/private funding (1, <20%; 2, between 21 and 40%; 3, between 41 and 60%; 4, between 61 and 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 carried out, or frequent contact with them is maintained; 2, survival and evolution tracking; 3, networking events between graduates and entrepreneurs/professionals of interest; 4, trainers of hosted enterprises; 5, networking meetings or events between graduates and hosted enterprises).

).

A survey was sent to all the BIs in Spain, applying the following work procedure: each survey was sent to the manager 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. On a regular basis, doubts and the interpretation of the items were also resolved. From there, a regular relationship usually arose, with constant feedback between the Funcas research team and the BI. As a consequence of this relationship, the questionnaire was improved, and the participation of BIs was increasing each year. Important information for both parties that should appear in the final reports was highlighted, among other details. Thanks to this relationship, it was possible to actively improve and maintain the Funcas questionnaire over time. The design of the questionnaire was widely described in [41,103] (Table S1: Business incubators survey).

In previous research [41], the observed variables or indicators were selected and validated. These indicators were grouped into homogeneous constructs, corresponding to each phase of incubation and the incubators results in Spain (spreading entrepreneurship, nine items; preincubation, nine items; basic incubation, nine items; advanced incubation, six items; and graduation, nine items) to deepen knowledge concerning the effects of the proposed model. The construct for the graduation rate (Grad) of the startup incubators was built from different indicators extracted from the bibliography. Subsequently, the research team, together with incubator managers, selected the most relevant indicators by consensus. Nine indicators were selected for the research (Table 3, items 34 to 42): ease of reaching agreements, survival and mortality rate of startups, compliance with the incubator’s objectives, number of graduated startups, graduation rate of entrepreneurships, % of enterprises with public or private financing, mentoring once graduated, and maintenance of links with the incubator. This research was performed based on the questionnaire that was carried out involving 88 business incubators in Spain. Based on the results of the questionnaire, a database was built, and a structural equation model was subsequently applied identifying the direct effects of incubation stages on the success rate of incubators in Spain.

2.2. Statistical Analysis

According to [87], an econometric model was built to quantify the relationships between business incubation phases and the graduation rate in Spain. PLS-SEM (partial least squares structural equation modeling) was employed to measure the association between the four phases of startup incubation and graduation rate. To test the posited hypotheses, we proposed a nonlinear model (Figure 2) with statistical estimates derived from PLS regression analysis [104,105,106]. The model was estimated using WarpPLS 8.0 software. The PLS algorithm was chosen according to the following criteria: the phenomenon investigated was relativity new; its modeling was at an emergent stage; and PLS had minimal recommendations concerning the sample size and prediction accuracy and comparatively low demands on data multinormality requirements [107]. The reliability was measured using the indicator reliability, and the internal consistency reliability was measured using Cronbach’s alpha and the internal composite reliability. The convergent validity was assessed through average extracted variances (AVEs), and finally, the discriminant validity was evaluated using criteria, as well as the cross-loadings matrix [108]. The following indices were used to test the hypotheses and assess the model fit’s average path coefficient (APC), average R-squared (ARS), average adjusted R-squared (AARS), average variance inflation factor of blocks (AVIF), average full variance inflation factor (AFVIF), Tennenhaus goodness of fit index (GoF), Simpson’s paradox ratio (SPR), R- squared contribution ratio (RSCR), statistical suppression ratio (SSR), and nonlinear bivariate causal direction ratio (NLBCDR). All quality indices met the recommended thresholds [109].

Next, the causal relationships between the startup incubators’ four stages and graduation rates were measured. To validate Hypotheses 1 to 4, a structural equation model was estimated using PLS. The criteria for choosing the algorithm were based on the novelty of the phenomenon, its modeling being in an emerging stage, and compliance with the minimum PLS recommendations regarding sample size, prediction accuracy, and relatively low requirements for data multivariate normality. The algorithm attempted to identify nonlinear functions between pairs of latent variables in structural equation models and to calculate their association coefficients, where path (beta) coefficients were normalized, using values between −1 and 1, to measure the strength and direction of the relationship.

3. Results

3.1. Description of Startup Incubators in Spain

According to Funcas (2022/2023) [103], since the first startup incubator was launched in Vizcaya in 1987, these entities have been proliferating increasingly in Spain, and there were 412 incubators in the country at the time of the survey. This increase remained constant even during the COVID-19 pandemic.

The incubators in Spain were small in size, according to Funcas (2022/2023) [103], with few staff (80% of them had fewer than five workers) and low capacity to host startups (37.84% had a capacity of fewer than 10 ventures). For 94.38% of the incubators, their objective was job creation and economic development, and 69.12% of them belonged to networks. The business incubators were mostly located in urban areas (78.65%). Regarding the target audience, most of the business incubators had a generalist approach (83.15%), and 49% of the incubators were managed by local public entities (city councils, provincial councils, and chambers of commerce). Although most of the business incubators had a generalist approach, a small percentage were specialized, whether in technology, agri-food, or another specific aspect. In terms of their management system, we find that public management predominated (60%) compared with private management. In addition to the incubators managed by public entities, 11% were managed by universities and 14% by private companies. Startup incubators in Spain are described below and are grouped and classified according to whether they constitute autonomous communities (

Table 4. Startup incubators according to their survival rate in Spain.

Communities	Cases	Mean 1
Cantabria	1	1 a
Islas Baleares	1	1 a
Canarias	4	1.75 ab
Andalucía	4	2.75 ab
Castilla y León	6	3.01 ab
Madrid	12	3.08 ab
Murcia	7	3.14 ab
Galicia	6	3.33 ab
Asturias	4	3.51 b
Valencia	4	3.50 b
Cataluña	15	3.53 b
Aragón	7	3.71 b
País Vasco	4	3.75 b
Castilla–La Mancha	10	3.8 b
Navarra	1	4.0 b
Extremadura	2	4.0 b

Notes: ¹ p-value; ^{a b} letters in superscript denote significant differences between autonomous communities; p < 0.01.

) and according to their survival rate. The survival rate was assessed as the percentage of startups generated in the incubator that remained active from the beginning to the present (Grd_Com_lv for Community). The results showed the existence of significant differences in survival rate by CCAA (p < 0.001), and through a multiple range test, three groups in the community were differentiated. The group with the lowest graduation rate was made up of incubators in the Balearic Islands, Canary Islands, and Cantabria, with values less than 1. On the contrary, the group with the highest graduation rate (plus 3.5 points) was made up of incubators in eight autonomous communities, highlighting the higher values in the communities of Extremadura and Navarra, with graduation rates greater than 75%.

3.2. Impact of Startup Incubation Stages on Performance

This study is relevant because it determined the relationships between startup incubation stages, presented in the theoretical framework, and success in business incubator programs in Spain. The effects of each one of the stages of startup incubators on the graduation rate were quantified using the structural equation model (SEM) proposed in Figure 1. A nonlinear structural equation model (SEM) was specified to assess the hypotheses of the theoretical model.

Figure 2 presents the results of the structural equation model with latent variables and illustrates the causal effects among the studied variables along with their respective p-values. The arrows indicate the direction of the proposed relationships, and the ovals represent latent variables that embody the formulated constructs: spreading entrepreneurship (Spread, nine indicators), preincubation (Pre_Inc, nine items), basic incubation (Incub, nine items), advanced incubation (Ad_Inc, six items), and graduation rate (Grad, nine items). The coefficients, on the arrows of the schematic model, were calculated on a standardized scale from −1 to 1, while in Figure 3, the shape of the nonlinear curve between both constructs is shown.

Table 5. Model fit and quality indices overview.

Index	Value	Value Interpretation
Average path coefficient (APC)	APC = 0.270, p = 0.002	Significant if p < 0.05
Average R-squared (ARS)	ARS = 0.715, p < 0.001	Significant if p < 0.05
Average adjusted R-squared (AARS)	AARS = 0.701, p < 0.001	Significant if p < 0.05
Average block VIF (AVIF)	AVIF = 2.010	Acceptable if ≤ 5, ideally ≤ 3.3
Average full collinearity VIF (AFVIF)	AFVIF = 2.505	Acceptable if ≤ 5, ideally ≤ 3.3
Tenenhaus GoF (GoF)	GoF = 0.530	Small ≥ 0.1, medium ≥ 0.25, large ≥ 0.36
Simpson’s paradox ratio (SPR)	SPR = 1.000	Acceptable if ≥ 0.7, ideally = 1
R-squared contribution ratio (RSCR)	RSCR = 1.000	Acceptable if ≥ 0.9, ideally = 1
Statistical suppression ratio (SSR)	SSR = 1.000	Acceptable if ≥ 0.7
Nonlinear bivariate causality direction ratio (NLBCDR)	NLBCDR = 1.000	Acceptable if ≥ 0.7

summarizes the model fit, quality ratios, and their interpretation, and Table S2 shows combined loadings and cross-loadings.

Table 6. Direct effects and significance level.

Hypothesis	Parameter Value (β)	p-Value	Acceptance/Rejection of the Hypothesis	Influence
H1: Spread vs. Grad	0.083	0.214	Rejected	---
H2: Pre_Inc vs. Grad	0.254	0.006	Accepted	Moderate positive
H3: Incub vs. Grad	0.200	0.025	Accepted	Moderate positive
H4: Adv_Inc vs. Grad	0.543	<0.001	Accepted	Strong positive

reports the results of hypotheses H1 to H4, specifying the direct effect of each construct, acceptance or rejection of the hypothesis, and the influence that the specified parameter had on the graduation rate of the incubators.

H1: Spread vs. Grad, Hypothesis 1: relationship between spreading phase and graduation rate; H2: Pre_Inc vs. Grad, Hypothesis 2: relationship between basic incubation phase and graduation rate; H3: Incub vs. Grad, Hypothesis 3: relationship between basic incubation phase and graduation rate; H4: Adv_Inc vs. Grad, Hypothesis 4: relationship between advanced incubation phase and graduation rate; β, regression coefficients; p-value, significance level.

The estimation results showed that H2, H3, and H4 were accepted (Table 3; Figure 3). Hypothesis 1 was rejected. Spreading entrepreneurship (Spread) did not have a significant effect on the graduation phase and did not exert a significant effect on other phases either. In addition, Hypotheses 2, 3, and 4 were accepted, and the three phases of incubation showed a positive effect on the startup graduation rate. As proposed in Hypothesis 2, preincubation (Pre_Inc) did have a moderate positive impact (β = 0.254) on the graduation phase (Grad) in startups incubating in Spain; in addition, this effect was significant (p < 0.01). Hypothesis 3 was also accepted, whereby the basic incubation constructor (Incub) had a positive influence on the graduation phase (Grad). The effect was moderately positive, with similar values in both hypotheses (H2 and H3). Hypothesis 4, related to the influence of an advanced incubation phase (Ad_Inc) on the graduation phase (Grad), was accepted. In this case, we found a high parameter (β = 0.543) and a significant value (p < 0.001). That is to say, advanced incubation had a strong positive effect on the startup graduation rate. The results obtained for Hypotheses 2, 3, and 4 were very promising because they all showed a strong relationship between the business incubators and the graduation of the startups (p < 0.01 and R2 = 0.71). This indicated that 71% of the success of startup incubators was due to the services and support provided by business incubators.

Figure 3 shows the best-fitting nonlinear curves (meaning utility) for the relationship between spreading entrepreneurship (H1), preincubation (H2), basic incubation (H3), and advanced incubation (H4), on one hand, and graduation rate. Hypotheses 2 and 3 were accepted and demonstrated similar effects, although the behavior of the relation between variables was different for each hypothesis. In the case of Hypothesis 2 (Figure 3b), with standardized values greater than −1.26, the relationship between Pre_Inc and Grad demonstrated constant growth. In the case of H3 (Figure 3c), regarding the relationship between basic incubation and graduation rate, an increasing relationship was demonstrated. That is, it was recommended to constantly improve good practices and actions in the preincubation phase since this generated a progressive improvement in the graduation rate. In the case of H3 (Figure 3c), regarding the relationship between basic incubation and graduation rate, a sharp curve, with decreasing and then increasing returns, was demonstrated. Thus, with negative standardized values of basic incubations, the returns decreased, while with values close to zero or positive, the returns increased (Figure 3c). Startups could not be located on the left, since deficiencies in basic incubation acted negatively on the graduation rate, and once a threshold was reached, any increase in advanced incubation generated increasing returns. In the case of Hypothesis 4 (Figure 3d), regarding the relationship between advanced incubation and graduation rate, an increasing relationship throughout all values of the dependent variable demonstrated.

4. Discussion

The results of this research showed that a structural equation model (SEM) was a useful tool for establishing the association between the business incubation programs offered and the results of the business incubators in Spain. These results agree with those obtained in [27,69,71,110]. The construct of the graduation rate of the startup incubators was built from nine success variables: survival rate, mortality rate, compliance with the incubator’s objectives, number of graduated startups, survival rate, percentage of entrepreneurships with public or private financing, mentoring once graduated, and maintenance of links with the incubator. The nine indicators showed their goodness of fit with the construct and were accepted within the model.

Three of the four proposed hypotheses that linked startup incubation phases in Spain with graduation rates were accepted. Surprisingly, Hypothesis 1 was rejected (regarding the positive effect of spreading entrepreneurship on the graduation rate), although the literature helped us understand this result. According to [93], the spreading entrepreneurship (Spread) phase had a strong positive effect on the preincubation [43,57], basic incubation [43,111,112], and advanced incubation phases [66,110,113,114]. In this sense, business incubators in Spain were mostly of small and very small sizes, with a reduced number of technicians, and were generalist and public in nature; in addition, they had limited accommodation capacity. These factors strongly restricted the access of startups to BIs. Therefore, the spreading entrepreneurship phase was a key phase and determined the incubation programs’ success. Once the entrepreneurship advanced in the incubation, other phases were the most relevant for the success of the entrepreneurship outside the incubator.

The three accepted hypotheses showed a moderate (H2 and H3) and strong (H4) positive association with the success of business incubators. The obtained results were aligned with the findings of [92,115], among others, indicating that the success rates of startup entrepreneurship were affected by the different phases of the business incubation process. The results also suggested that the fundamental factors that influenced the relationship between incubation phases and the graduation rate of emerging companies were external network relationships, incubator management and service quality, the use of public platforms, the support of interested parties, infrastructure, and the implementation of process activities [98]. In this sense, incubators that provided connectivity, resources, and support to companies were more likely to improve the survival of companies after graduation, while those that isolated companies from key dependencies could hinder their development and increase vulnerability to environmental demands. Additionally, stakeholder support and suitable processes were critical to the performance of business incubators, impacting the graduation rates of startups in these environments [116,117]. The authors of [118] analyzed the case of Andalusia, where the gender and nationality of the promoters of the ventures were relevant factors in the survival of startups [119,120].

The preincubation (second phase) and the basic incubation (third phase) phases demonstrated a moderate positive influence (β = 0.254 and β = 0.200, respectively) on the graduation rate. However, the behavior of the latent variable was uneven. In the case of preincubation (Figure 3b), after a first initial decreasing section, the curve demonstrated increasing returns, showing that support in this incubation phase contributed to an improvement in the graduation rate. On the contrary, the basic incubation indicator (Figure 3c) had two differentiated sections, the first with decreasing returns; once the variable reached positive values, the curve described increasing returns. Finally, advanced incubation, the fourth phase of incubation, had a strong positive influence on incubators’ results in Spain. The curve showed increasing returns throughout the curve (Figure 3d), illustrating the importance of properly concluding the entire incubation process as a guarantee of the success of the enterprises.

The direct effect of the startup incubation phases on the graduation rate was positive. Furthermore, following the method described in [41], we found a positive association between the four startup incubation stages. This promoted a strong indirect effect between business incubation and the graduation rate, explaining 71% of the success of the incubators. In this way, the results of [41] highlighted the strong positive influence, in a sequential manner, of the first phase of incubation (spreading entrepreneurship) on the remaining incubation phases. The advanced incubation phase, which concluded the activity in the incubator, strongly determined the success of the ventures and was of great strategic value for the incubation.

In Hypothesis 2, regarding preincubation vs. the graduation rate, the analysis of the business idea and the entrepreneurial legal form were of strategic importance for the feasibility of the idea to be achieved [121,122]. Financial and market training of startup managers characterized this phase and its advancement as determining factors in the graduation rate [24,68,123]. The preincubation process reduced the gap in knowledge and skills between the academic world and business incubators [42]. This phase contributed to training and orientation by accelerating product development and reducing transition times from projects to operational entities [124]. The integration of technology entrepreneurship education has produced encouraging results in guiding entrepreneurs from generating ideas to establishing companies with viable products, according to [125]. In addition, the presence of social capital and collaboration perspectives in business clusters and incubators can positively influence the survival rates of these companies [126].

Mentoring, whether it is conventional or digital, such as electronic mentoring, provides support, practical guidance, and knowledge transfer for aspiring entrepreneurs [127]. Technicians and mentors help startups understand their challenges more effectively, improve their performance, and prepare them for the demands of the sector [42,128], thereby helping startups overcome a shortage of prior experience and knowledge to expand their businesses effectively [129]. In this sense, understanding the motivations of mentors in accelerators is also crucial to optimize the mentoring process and improve the assistance provided to startups [130]. Finally, it should be remarked that the level of experience of the entrepreneurs had a positive influence on the relationship between an incubator’s mentoring program and the probability of the business surviving in the first year. This was because entrepreneurs with more experience demonstrated a higher capacity to take advantage of the mentoring program offered by the incubator [131].

In Hypothesis 3, regarding basic incubation vs. the graduation rate, the entrepreneur faces deviations that occur concerning the business plan. The support provided by the incubators’ technicians is a great help for entrepreneurs’ ability to learn how to deal with these disturbances in the future. The search for other sources of financing, the elaboration of agreements, access to subsidies, financial negotiations, and access to other technological developments are keys for the future [111]. Basic incubation programs provide a wide range of resources—both tangible and intangible—to entrepreneurs starting their projects. These programs foster an environment where meaningful interactions occur between peers, mentors, volunteers, and interns, promoting learning, collaboration, and credibility with external stakeholders [132]. In this phase, the positive influence of social capital on the survival of the venture was assessed. Collaboration with entities such as universities, industries, and government organizations was observed to improve the performance of graduating companies [126]. In this way, university incubators promoted a business mentality in students, faculty, and staff, transforming them from job seekers to job creators [133]. These studies highlighted the important role of incubation programs in motivating and supporting entrepreneurs from creation to graduation. According to [134], an effective incubator training management model improves the entrepreneurial skills of graduates, which reduces unemployment rates and accelerates job creation.

The relationship between advanced incubation and graduation rate constructors (with which Hypothesis 4 was concerned) was highly strong and positive. Advanced business incubation refers to the services and support offered by business incubators to facilitate the growth and successful graduation of new startups. In this phase, the startups accessed internationalization. For their journey, they used a dual system, with internal mentoring to the incubator and external mentoring with specialists such as law firms, business angel networks, venture capital experts, agreement specialists, etc., helping to obtain capital for the venture [113,135,136].

Access to financing represents a significant obstacle for entrepreneurs [137]. Business incubators play a double role in the financing of startups: they help them obtain financing [138,139], and they advise entrepreneurs on how to rationalize the financing processes of their companies as these companies seek to increase profitability and risk diversification [140]. In the case of South Africa, most incubators focus on supporting specific candidate profiles, which could lead to a neglect of other strategic needs of SMEs [5].

Challenges and Implications of Incubator Mentoring Programs

Below are different approaches that constitute challenges for improving business incubator programs in Spain and other countries that use similar business models.

(a)

Social focus. Currently, the social focus of incubators is increasing, seeking to increase the creation of sustainable value, although there remains an insufficient number of researchers linked to these business models [141,142]. In this sense, university incubators, such as C-Hub UGM, favor the development of social entrepreneurship capabilities [143]. Furthermore, the social dimension of business incubation, which is often undervalued, has an impact on the experiences of entrepreneurs in the incubator environment and on the social interactions between stakeholders [144]. On the other hand, the nature of a firm’s operations also impacts its ability to obtain capital; thus, those companies with a social focus are more successful in obtaining financial support from private investors and government entities [145]. The synergy of incubators with local authorities and social enterprises implementing corporate social responsibility (CSR) can lead to the creation of new certified social, ethical, ecological, and fair-trade products and services, benefiting the local economy [146]. There was a positive correlation demonstrated between the pattern of the relationship between environmental development strategies and the performance of SME technology business incubators within a CSR program; this emphasized the importance of CSR in improving the performance of incubators [15]. In the startup context as well, the relationship between CSR and strategic innovation is vital, as it influences the company’s reputation, employee motivation, and overall performance, highlighting the importance of integrating CSR practices with innovation for differentiation and competitiveness [147].

(b)

Training and monitoring. This section complements what was previously stated in Hypotheses 2 and 3. The implementation of an incubator training management model has proven effective in improving the entrepreneurial skills of graduates, thereby reducing unemployment rates [134]. However, the dynamics between incubated companies and university-based incubators can vary, impacting the viability of graduated startups [148]. Specifically, incubator graduation policies can significantly influence the performance of entrepreneurs during the initial stages of their incubated companies. However, implementing new post-incubation monitoring mechanisms and establishing a regulatory framework will be essential to foster stronger interactions with graduates [149].

(c)

Network business incubator. The creation of networked business incubators provides privileged access to a group of potential partners, which facilitates the acquisition of funds and other essential resources for growth [150]. Network incubators offer competitive advantages for both startups and entrepreneurs [101]. In this way, they provide new innovative environments, through a platform for collaboration and the exchange of ideas between colleagues, coaches, and external institutions, which positively influence the development of new ventures [114,151]. In the case of traditional producers and public tenders in Spain, networking helps companies overcome resource limitations and create partnerships [152]. Additionally, networking enables real-time information sharing, professional incubation services, risk assessment, and cost tracking, resulting in greater growth and success for businesses [153]. For the future programs of incubators, it would be of great interest to enhance the creation and effective consolidation of networks, since these associations and collaborations between different incubators improve their effectiveness and contribute to economic growth and local job creation [154].

(d)

Internationalization of incubators with a globalized approach. Incubation contributes positively to the internationalization and expansion of companies by facilitating the exchange of knowledge, networks, and business assistance [113]. The globalization of companies has a beneficial impact on the growth and sustainability of the world market.

BIs play a critical role in this process by providing the essential resources, networks, and knowledge for startups to grow internationally [155]. Furthermore, they promote the international expansion of startups by establishing global connections and knowledge networks, using knowledge integration and customer/market contact-seeking practices [155,156,157]. BIs offer a platform for the creation of international networks, comprehensive assistance, and specialized services that expedite entry into foreign markets. In this sense, incubation capabilities and project management are critical to optimizing globalization strategies, thereby improving regional innovation performance and fostering the sustainable expansion of companies. This is the case for India and Pakistan [158] and for Bangladesh and South Africa [159]. During the advanced incubation stage, companies acquire knowledge and engage in business network connections, allowing them to access new markets and establish new partnerships that support internationalization [160]. The use of project management models, such as the Life Cycle Canvas model, in the advanced incubation stage promotes the international growth strategy of incubated companies by prioritizing crucial areas and overcoming internal and external obstacles [161]. International incubators use efficient strategies to enter third-party markets as they provide a structure to merge market entry theories and incubation [162]. In this context, novel post-incubation monitoring mechanisms and a regulatory framework that encourages stronger engagement with graduated companies are needed [163]. The development of international incubators is incipient in Spain, but they favor the replication of the network model through the establishment of business communities after incubation [164].

(e)

Sustainability of startups. The sustainability of graduating companies is a critical area of research. Studies reveal that companies may face greater vulnerability and failure after incubation, depending on the level of support provided during the incubation period [164].

Study of the long-term effects is of interest for researchers who are trying to improve their knowledge of the relationship between sustainability and the growth of companies after incubation. There are both positive and negative opinions regarding these effects in the literature, and the literature demonstrates disparate results on this subject. While there may be an initial negative impact on business survivability and sales revenue, the long-term [165] performance during the incubation period and the resources available in the incubator play a crucial role in determining post-graduation success [149,166,167]. Furthermore, targeted policies and support mechanisms may significantly improve the performance of startups after graduation.

(f)

Transfer approach. Incubators play a key role in bridging academia and industry, facilitating knowledge transfer and providing business support within a sustainable ecosystem [15]. This is particularly pertinent if one considers that the transmission of knowledge (alongside teaching and research) constitutes the third pillar of universities. A case study conducted in Portugal highlights the need for innovative post-incubation monitoring mechanisms to improve interactions with graduates [168]. University-based business incubators help students develop digital businesses, but impediments such as limited technological, management, and leadership skills impede their advancement and growth [169]. University business incubators also encourage transnational entrepreneurship and the evolution of commercial and technical communication practices by facilitating the creation of companies by professors, students, researchers, and local entrepreneurs [170].

Finally, with the results obtained and discussion, a guide for good practices was built in each phase to improve the graduation rate (

Table 7. Best management practices (BPSs) for result improvement.

Incubation Phase	BMPs	Business Incubator Actions	Sustainability Actuations	Emerging Technology’s Role	Applications
Spreading entrepreneurship	Training and mentoring programs	Strategic planning.	Innovation in sustainability	Support to technical advisors	✓✓
		Create learning	Environmental and social awareness	Efficient search for information	✓✓
	Social and ecological practices	Promoting project recruitment	Promotion of sustainable business	Automation tasks	✓✓✓
Preincubation	Training and mentoring programs	Training in business plan and legal forms	Environmental and social awareness	Simplify business plan	✓✓✓✓
	Office space and technology support in BI	Sustainable development as strategic planning	Promotion of sustainable business models	KPIs and ratios	✓✓✓
	Networking	Regular meetings	Resource efficiency	Data analysis	✓✓✓
Basic incubation	Training of specialized technicians	Specialized BI and technicians	Innovation in sustainability	Immersive training (AR) and (VR)	✓✓✓✓
	Networking	Regular meetings	Resource efficiency	Cloud solutions	✓✓✓
	Knowledge and technology transfer	Connecting university with the market	Innovation in sustainability	Innovative activities	✓✓✓✓
Advanced incubation	High specialization of advisors	External support	Innovation in sustainability	Networked resources	✓✓✓✓✓
	Specialized network services	Internationalization agreements	Promotion of sustainable business	Security in transactions	✓✓✓✓
	Connecting	At the institutional level and universities	Resource efficiency	Hybrid connections	✓✓✓
	Global market focus	Specialization of technicians and mentors	Resource efficiency	Information just in time	✓✓✓✓✓

✓ Level of importance, the more ticks the higher the level of importance.

). The creation of this guide involved proposals for actions in the BI, performance that favored innovation, and the use of emerging technologies such as AI.

Although actions in the different phases of incubation were included in the guide, we proceeded to highlight those actions that would be applied in the advanced phase of incubation and that showed a greater effect on the graduation rate. Advisors and mentors are highly specialized, both by sector and in sustainable development, since in this phase, first, it may be necessary for startups to sign agreements with partners or investors, to sign shareholder agreements, and to internationalize their products or services; second, it may be necessary for startups to ensure that the management of intangible resources and social aspects and relations is supported by the incorporation of staff or through agreements with other entities that provide services, such as law firms or other specialists who mentor in specific aspects. The social approach and ecological practices should be promoted both in the management of BIs and in startups. After graduation, it would be very interesting to follow up on the companies graduated by BIs to monitor survival and progress and measure the performance of the incubator with each startup.

In order to improve sustainability practices, it is recommended to promote the creation of sustainable business models by supporting initiatives focused on sustainability in advice and mentoring, thus encouraging the development of products or services that address environmental, social, and economic challenges. This will also lead to raising environmental and social awareness in information sessions and during training. In the BI, resources and knowledge are shared that generate greater efficiency in their use and a reduction in costs and inputs. Likewise, the BI can promote access to green finance, specifically for sustainable projects [14].

Regarding the applications of emerging technologies in BIs, these can help both advisors and entrepreneurs. In phase 1 (dissemination of entrepreneurship), AI can help advisors to obtain information and market data in real time and can automate administrative processes. In phase 2, preincubation, AI simplifies the preparation of the business plan and the economic and financial plan, the preparation of the marketing plan, market research, etc. AI helps to streamline processes and automate administrative and operational tasks, allowing entrepreneurs to focus on strategic decisions. Validating ideas also helps: AI tools can analyze large volumes of data to identify market trends, consumer behaviors, and business opportunities. In phase 3 of basic incubation, AI can help startups prepare more accurate financial presentations and analyses, facilitate obtaining funding from investors and scale more efficiently, and automate the analysis of data and market trends and is useful for tracking the progress and success of startups. In phase 4, emerging technologies are useful for internationalization, generating contract and agreement models, drawing up the internationalization plan, analyzing data and KPI performance, exploring new markets, developing innovative products and improving existing ones, and increasing competitiveness in the market. Overall, these technologies make it possible to reduce costs and speed up the achievement of objectives at each phase [171].

Among the main limitations of the study, the following ones can be highlighted: (a) The sample was sufficient and broad but should be increased, with a greater number of BIs and private BIs and using other socioeconomic variables. This would allow the results to be extended to other contexts. (b) The graduation rate was evaluated in the short term after its implementation in the market. (c) An analysis of moderating variables that could be included in the study was missing. (d) We have information provided by the incubators that could be expanded with direct information from the startups—both the successful ones and those that failed.

5. Conclusions

The structural equation model (partial least squares) was a useful tool to quantify the relationship between startup incubation and the graduation rate of incubators in Spain. The construct used to measure the results of the incubator was referred to as the graduation rate and comprised nine indicators that were accepted within the model: survival rate, mortality rate, compliance with the incubator’s objectives, number of graduated startups, survival rate of companies, percentage of companies with public or private financing, mentoring once graduated, and maintenance of links with the incubator. Three hypotheses that linked startup incubation to the incubator’s success were accepted; these hypotheses were concerned with the relationship between preincubation and the graduation rate (Hypothesis 2), basic incubation and the graduation rate (Hypothesis 3), and advanced incubation and the graduation rate (Hypothesis 4). In all three cases, a positive influence was identified; for Hypothesis 4, the causal relationship was very strong. According to the nonlinear shape of the curve between variables, the relationship changed as the venture progressed through each incubation phase. This relationship adjusted to the curve of diminishing returns.

The direct relationship between advanced incubation and graduation rate variables (Hypothesis 4) was strong (β = 0.543) and significant (p < 0.001), and the indirect effect with the rest of the phases reached 0.71%. Improving the graduation rate requires an improvement in the advanced incubation services offered by startup incubators and is fundamentally related to the expansion of internationalization and to value creation.

In this context, the future challenges that determine the success of the incubator programs were analyzed; these include social focus, training and monitoring, network business incubators, internationalization of incubators with a globalized approach, sustainability of startups, and transfer approach. The inclusion of each challenge in incubation programs and the evaluation of its impact on the graduation rate could be areas for future research and may possess great interest for the academy and significance for the effective development of entrepreneurship. Furthermore, in subsequent studies, it is recommended to delve into the success of startups in the long term and their viability. It would be of great interest to have entrepreneur information housed in business incubators.