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Article

Mentoring Patterns in Business Incubators: A Typology and Organizational Maturity Model from Spain

by
Ana Asensio-Ciria
1,
Carmen De-Pablos-Heredero
2,*,
Jose Luis Montes Botella
3 and
Antón García Martínez
3,4
1
Department of Business Organization, Faculty of Economics and Business Sciences, Somosaguas Campus, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
2
Department of Business Administration (Administration, Management and Organization), Applied Economics II and Fundamentals of Economic Analysis, Rey Juan Carlos University, Paseo de los Artilleros s/n, 28032 Madrid, Spain
3
Department of Applied Economics I and History and Economic Institutions, Rey Juan Carlos University, Paseo de los Artilleros s/n, 28032 Madrid, Spain
4
Animal Science Department, Rabanales Campus, University of Cordoba, 14071 Cordoba, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(11), 5407; https://doi.org/10.3390/su18115407
Submission received: 9 April 2026 / Revised: 20 May 2026 / Accepted: 22 May 2026 / Published: 28 May 2026
(This article belongs to the Special Issue Entrepreneurship, Innovation, and Sustainable Work and Organizational Practices)
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Abstract

This research mapped mentoring typologies implemented by business incubators in Spain and examined the role of these typologies in fostering sustainable entrepreneurship. Using a quantitative multivariate approach, this study identified and classified mentoring models on the basis of 28 variables related to the mentoring process. The analysis drew on data from the Funcas 2025 survey of Spanish business incubators, which provided detailed information on mentoring practices across the participating incubators (initial responses: n = 100; final analytical sample after listwise deletion of missing values: n = 93). Principal component analysis was applied to extract the main latent dimensions underlying mentoring activities, and cluster analysis was subsequently used to group incubators into homogeneous mentoring typologies. The analysis identified three distinct mentoring profiles: (i) advanced mentoring, characterized by formalized programs with systematic evaluation, rigorous mentor selection, and continuous training; (ii) moderate mentoring, defined by partial integration into incubation services and the use of basic monitoring and evaluation mechanisms; and (iii) incipient mentoring, grounded ad hoc interactions, low formalization, and the absence of structured evaluation systems. Incubators with structured, continuous, and expert-driven mentoring systems were associated with higher entrepreneurial survival rates and stronger contributions to sustainable business development. From a public policy perspective, the findings highlighted mentoring as a strategic policy instrument for advancing Sustainable Development Goals related to Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), and Sustainable Cities and Communities (SDG 11). The proposed mentoring typology provided an evidence-based framework to support differentiated incubation policies, improve the targeting of public resources, and design stage-specific mentoring interventions. By moving beyond uniform policy approaches, public authorities can more effectively strengthen entrepreneurial ecosystems and promote resilient, innovative, and sustainable territorial development.

1. Introduction

Entrepreneurial mentoring has been widely recognized as a strategic component of startup support programs implemented by incubators and accelerators worldwide [1]. In environments characterized by high uncertainty, resource constraints, and steep learning demands, access to experienced mentors provides entrepreneurs with tacit knowledge, strategic guidance, professional networks, and external legitimacy while also supporting more informed decision-making. As a result, mentoring is no longer viewed as a complementary service but rather as a core element of incubation and acceleration programs aimed at improving startup survival, growth, and scaling capacity [2,3,4].
Empirical research consistently highlights the positive effects of mentoring on entrepreneurial learning, managerial capabilities, access to networks, and venture performance. Prior studies show that intensive mentoring improves managerial knowledge and firm performance among early-stage entrepreneurs [5]. Other research indicates that structured mentoring significantly increases the likelihood of obtaining external financing [5,6]. Longitudinal evidence further suggests that formal mentoring is associated with higher business survival rates, particularly among entrepreneurs with limited experience or restricted access to networks [7]. These effects can be interpreted through multiple complementary theoretical frameworks, including social capital theory, absorptive capacity, effectuation theory, entrepreneurial ecosystem approaches, and signaling theory, all of which are further developed and integrated in Section 2.
Despite this growing body of research, important gaps remain. First, existing studies show a strong geographical bias: most empirical work has been conducted in Anglo-Saxon ecosystems or countries with highly institutionalized accelerator programs such as Israel, Sweden, or Chile [8,9,10,11,12,13,14,15]. Second, there is limited understanding of how mentoring programs are structured across different ecosystems and which organizational models dominate in practice. Third, contextual factors that moderate mentoring effectiveness—incubator type, program design, and sectoral orientation—remain insufficiently explored [16,17]. These gaps are particularly consequential because mentoring programs exhibit substantial heterogeneity across incubation systems [18,19,20]. They differ in formalization, session intensity, mentor selection mechanisms, and evaluation systems, and their effectiveness depends largely on how they are designed and implemented [4,21].
The Spanish ecosystem follows a mixed incubation model in which public, university-based, private, and corporate incubators coexist, and is characterized by pronounced territorial decentralization: regional governments hold substantial influence over entrepreneurship and innovation policy, producing marked interregional disparities in institutional support, program availability, and ecosystem maturity [22,23,24,25]. Despite this institutional complexity, academic research on mentoring within Spanish incubators remains limited, with existing studies focusing mainly on aggregated impact analyses rather than on the organizational diversity of mentoring practices [26,27,28,29,30,31].
The Spanish incubation system is embedded within a multi-layered National Innovation System (NIS) that shapes both the design and the heterogeneity of mentoring programs across incubators. In terms of overall innovation performance, Spain is classified as a Moderate Innovator in the European Innovation Scoreboard (EIS) 2024, ranking 18th among EU Member States with a performance at 89.9% of the EU average, an improvement of 9.4% since 2017, driven notably by growth in venture capital spending (114.9% of the EU average), public R&D expenditure, and sales of new-to-market innovations [32]. At the regional level, the picture is markedly heterogeneous: according to the Regional Innovation Scoreboard (RIS) 2025, four Spanish regions qualify as Strong Innovators: Catalonia (the highest-ranked Spanish region, scoring 110.7 and ranked 72nd in Europe), the Basque Country, the Community of Madrid, and Navarre, while the remaining thirteen regions fall into the Moderate or Emerging Innovator categories, revealing a persistent innovation divide within the country [33]. This intra-national disparity directly conditions the institutional density and sophistication of entrepreneurial support services, including mentoring, across regions. At the national level, the regulatory and financial architecture rests on three main pillars. The Centre for Industrial Technological Development (CDTI), under the Ministry of Science, Innovation and Universities, provides partially refundable grants and co-investment instruments for technology-based ventures, including the NEOTEC program specifically targeting early-stage startups. The National Innovation Company (ENISA) complements this through participative loans for innovative SMEs and, since the enactment of the Startup Law [34], serves as the official certifying body that accredits companies as empresas emergentes, granting them access to a specific fiscal, labor, and administrative regime designed to reduce barriers to creation and growth [34]. The Startup Law also established the National Entrepreneurship Office (ONE) as a single-window service for entrepreneurs, and introduced specific visas for founders and digital nomads, signaling a broader strategic commitment to positioning Spain as a leading European entrepreneurial hub. At the regional level, each of Spain’s seventeen autonomous communities has developed its own Research and Innovation Strategy for Smart Specialization (RIS3), a prerequisite for accessing European Regional Development Funds (ERDF). These strategies define sectoral priorities and co-finance incubation programs aligned with regional comparative advantages, producing marked interregional variation in the availability, scope, and maturity of entrepreneurial support services -including mentoring [35,36,37,38].
Beyond these three pillars, University-based incubators and Technology Transfer Offices (OTRIs) constitute a third institutional layer, connecting scientific knowledge production with early-stage venture creation [39,40]. In terms of mentoring professionalization, the Spanish ecosystem features several noteworthy intermediary actors. ANCES (Asociación Nacional de Centros Europeos de Empresas e Innovación/Spanish Association of European Innovation and Business Centers) comprises 32 Business Innovation Centers across Spain, integrated into the European EBN network, and promotes quality standards in incubation through instruments such as the Innovative Technology-Based Enterprise seal (EIBT). In the domain of mentor training and certification, the Fundación para el Conocimiento madri+d stands out as a pioneering institution: its Business Mentor madri+d certification program, active since 2006, has trained and certified close to 400 mentors across Spain and internationally through the Enterprise Europe Network [41]. The Foundation also coordinates the ESA BIC Comunidad de Madrid—the European Space Agency’s Business Incubation Center in the region, co-funded equally (50%) by the ESA and the Community of Madrid. Since 2015, it has supported over 50 deep-tech startups across five university nodes, integrating mentoring through the madri+d mentor network, business angel access via BAN madri+d, and technical expertise from the European space program. This program illustrates how supranational agencies complement national and regional NIS instruments in co-designing specialized incubation ecosystems within Spain. At the national associative level, AMCES (Spanish Association of Mentoring and Coaching for the Social Economy) constitutes the largest community of registered mentors in Spain, with over 3000 members, and serves as the national representative body within the European Mentoring and Coaching Council (EMCC). At the local level, the Madrid City Council launched in April 2025 the Red de Mentores de Madrid Emprende/Mentoring Madrid Emprende Network, a free program connecting over 160 experienced mentors, 84% of whom are themselves entrepreneurs, with startups and SMEs across 18 sectors, reflecting a growing trend towards institutionalized, publicly funded mentoring at the local level [18,27]. This multilevel institutional architecture helps explain both the diversity of mentoring configurations identified in the present study and the territorial asymmetries in mentoring quality documented in prior research [22,42].
Despite the expansion of the Spanish incubation ecosystem, there is still no systematic quantitative characterization of mentoring programs implemented by incubators and accelerators, nor are there empirical typologies that capture the diversity of mentoring models operating within this ecosystem [22,42]. This lack of evidence limits both theoretical development and the design of evidence-based policies and management practices.
Recent quantitative studies have begun to examine entrepreneurial mentoring using factor-analytic or clustering techniques. Recent work on Spanish incubators and accelerators [22,31] has shown that the presence and broad characteristics of mentoring programs are associated with better ecosystem functioning, using mean comparison tests, chi-square tests and clustering to distinguish groups of ecosystems with and without mentoring. Other contributions apply principal component or exploratory factor analysis to validate mentoring-related constructs or outcomes at the individual entrepreneur level, such as studies on mentoring, training and financing as drivers of entrepreneurial performance, or on mentoring outcomes and ecosystem factors in African and Asian contexts. However, these works either treat mentoring as one latent factor within broader support structures or focus on individual-level outcomes; they do not develop a mentoring-specific latent structure and typology of incubators, nor do they propose an organizational mentoring maturity model.
To address this gap, the present study provides a systematic analysis of mentoring programs implemented by Spanish incubators and accelerators based on survey data from 100 organizations [18]. Unlike previous studies that primarily focused on entrepreneurial outcomes, this research aims to characterize the organizational models of mentoring operating within the Spanish ecosystem. Using multivariate techniques, the study identifies patterns of mentoring implementation and analyzes the heterogeneity of mentoring practices across incubators.
Building on the identified gaps, this study is guided by three research questions:
RQ1: What distinct typologies of mentoring programs can be identified among Spanish business incubators according to their organizational configuration?
RQ2: Do the identified mentoring typologies represent progressive levels of organizational maturity in terms of formalization, professionalization, and results orientation?
RQ3: How are different mentoring configurations associated with indicators of entrepreneurial performance, particularly start-up survival and sustainable business development?
This study contributes to the literature on entrepreneurial mentoring and business incubation in three main ways. First, from an empirical perspective, it provides the first large-scale systematic characterization of mentoring programs in Spanish incubators, extending a literature largely dominated by studies conducted in Anglo-Saxon ecosystems. Second, from a theoretical perspective, this research connects widely used conceptual frameworks, such as social capital, absorptive capacity, effectuation, entrepreneurial ecosystems, and signaling theory, with concrete organizational configurations of mentoring programs. The findings offer practical insights for incubator managers and policymakers seeking to design more effective mentoring programs adapted to the characteristics of the Spanish entrepreneurial ecosystem; in contrast to prior work that apply principal component and cluster analysis to incubators or mentoring in a largely descriptive manner, this paper explicitly links the resulting configurations to a theorized maturity model, incorporates territorial control dimensions, and connects cluster membership to performance indicators and ecosystem-level implications.

2. Theoretical Background

The study of entrepreneurial mentoring in business incubators draws on five complementary theoretical frameworks, each capturing a distinct dimension of how mentoring generates value for incubated ventures and for the incubation system as a whole.
Social capital theory [35,43] provides the foundational relational lens: mentors act as brokers who connect entrepreneurs to scarce resources—investors, clients, strategic partners—that they cannot access independently. The quality of mentor–mentee ties determines the depth of bonding capital generated within the dyad, while the mentor’s network position enables bridging capital that extends the entrepreneur’s relational reach beyond their immediate social circle [36]. This framework directly informs the relational depth dimension captured in the study’s principal component analysis (PCA).
Absorptive capacity [11,12,21,44] explains how mentoring enables startups to identify, assimilate, and apply external knowledge. Mentors who possess sector-specific expertise and work within formalized interaction structures facilitate knowledge transfer more effectively than those operating on an ad hoc basis.
Effectuation theory [39,45] captures how mentors support entrepreneurs in conditions of high uncertainty. Rather than promoting causal planning, experienced mentors encourage iterative, resource-based decision-making, experimentation, and affordable-loss logic. This perspective helps explain why mentoring intensity matters most for early-stage ventures navigating ambiguous markets.
Entrepreneurial ecosystem approaches [25,46] situate mentoring within broader territorial systems of innovation. Mentoring programs do not operate in isolation: their effectiveness is conditioned by the density of available mentors, the connectivity of regional networks, and the institutional resources of the surrounding ecosystem.
Signaling theory [40,47] highlights that affiliation with recognized mentors or selective programs reduces information asymmetries for investors and external stakeholders. Incubators that develop advanced mentoring systems—with rigorous mentor selection, accreditation, and systematic evaluation—generate credible quality signals that benefit incubated ventures in fundraising and partnership development.
Taken together, these frameworks suggest that the organizational design of mentoring programs determines their capacity to generate value. Formalization enables absorptive capacity; relational depth activates social capital; ecosystem orientation amplifies signaling effects; and iterative interaction supports effectuation processes. This integrated reading motivates the configurational approach adopted in the present study: rather than examining mentoring effects in aggregate, the study identifies distinct organizational configurations and interprets their maturity levels through the combined lens of these complementary frameworks.

3. Materials and Methods

3.1. Population and Sample

Data used in this study were obtained from the Dataset compiled by the Fundación de las Cajas de Ahorros (Funcas) [18]. This report provided a ranking that evaluated the quality of incubation and acceleration services within the Spanish entrepreneurial ecosystem. The report systematically collected information on the organizational characteristics, services offered, and business outcomes of the incubators surveyed, including specific aspects related to mentoring.
The analysis focused on the 419 incubators operating in Spain with an active mentoring program [18], from which 100 responses were obtained. The questionnaire used in the Funcas 2025 survey had been designed to capture the multidimensional nature of mentoring in incubators through four conceptual blocks: program design, mentor profile, perceived impacts, and implementation challenges. Following this structure, 28 variables related to mentoring practices and organizational characteristics were selected for the present study.
Observations with missing values were removed using the listwise deletion, resulting in a final analytical sample of 93 incubators with complete data after excluding questionnaires with missing data on one or more mentoring variables. Before conducting the principal component analysis (PCA), the suitability of the data was assessed using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. The results confirmed that the correlation matrix was appropriate for factor analysis (KMO = 0.748; Bartlett’s test: χ2 = 812.4, df = 378, p < 0.001).
The questionnaire included 28 variables related to the design of mentoring programs, mentor profiles, perceived impacts of mentoring, and challenges associated with its implementation. These variables were organized by the research team into four conceptual blocks. Table 1 presents the coding scheme and a detailed description of the variables included in each of these conceptual blocks.
In addition to the 28 mentoring variables, three contextual variables related to the incubators were included: years since the incubator’s creation, municipality size, and autonomous community. These variables were incorporated to capture territorial differences and levels of organizational consolidation (Table 1).
The variables were initially organized into four conceptual blocks during the design of the questionnaire and the Funcas report to capture different dimensions of mentoring within incubators, including program design, mentor profiles, perceived impacts on entrepreneurs, and implementation challenges. This preliminary classification reflected the multidimensional nature of mentoring identified in previous research on incubation and mentoring systems [3,18,21,28]. However, given the exploratory nature of the analysis, the final structure of the mentoring dimensions was determined empirically through the principal component analysis (PCA) rather than by the initial conceptual grouping of variables.

3.2. Statistical Analysis

The study adopted a cross-sectional quantitative research design. The empirical analysis was conducted in four sequential stages to identify mentoring patterns among incubators and subsequently relate them to an organizational maturity model of mentoring.
In the first stage, a set of 28 variables related to the design, implementation, and outcomes of mentoring programs in incubators was selected. These variables captured several dimensions of mentoring practices, including the degree of program formalization, the profile and training of mentors, the intensity of mentor–mentee interactions, and perceived impacts of mentoring on entrepreneurs and incubated firms. In the second stage, a Principal Component Analysis (PCA) was applied to reduce the initial set of variables and extract the main latent dimensions structuring mentoring systems in incubators. PCA is widely used in exploratory research to identify underlying structures in large datasets and to reduce dimensionality while preserving the maximum amount of variance [48,49]. Prior to conducting the PCA, the suitability of the data for factor analysis was assessed using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity [37,50]. The 28 variables included in the analysis were drawn directly from the Funcas 2025 survey instrument [18], which was specifically designed to capture the multidimensional nature of mentoring in Spanish incubators across four conceptual blocks: program design, mentor profile, perceived impacts, and implementation challenges. Their selection therefore followed the conceptual structure of the instrument. Components were retained based on the Kaiser criterion supported by inspection of the scree plot [38,48]. The sixth component (eigenvalue = 1.010) was retained because it captures a geographically distinct dimension, the autonomous community of location, that is theoretically relevant given the marked territorial heterogeneity of the Spanish incubation ecosystem.
In the third stage, the component scores derived from the PCA were used as input variables in a hierarchical cluster analysis to identify groups of incubators with similar mentoring configurations. Following established methodological recommendations for exploratory typology construction, clustering was performed using Ward’s minimum variance method combined with squared Euclidean distance [51,52]. Ward’s method minimizes within-cluster variance and is particularly suitable for identifying relatively homogeneous groups in organizational research. The optimal number of clusters was determined through examination of the agglomeration schedule, the dendrogram, and the relative increase in the clustering coefficient; this procedure yielded a stable cluster solution representing distinct mentoring models.
In the final stage, outcome and impact variables were used to characterize the mentoring patterns identified and to explore their association with indicators of entrepreneurial performance, particularly startup survival and the continuity of incubated firms. Based on these empirical results, the different mentoring patterns were interpreted as progressive levels of organizational maturity, which led to the proposal of an organizational maturity model of mentoring in business incubators.
Overall, this methodological strategy allowed the empirical identification of mentoring typologies and provided a systematic framework for interpreting the heterogeneity observed among incubators. By linking the clustering results with indicators of entrepreneurial performance, the analysis also offered insights into how different mentoring configurations may contribute to sustainable business development within incubation ecosystems.

4. Results

4.1. Sectoral Profile of Incubated Ventures

No systematic evidence was found on the sectoral profile of ventures housed in Spanish business incubators, as confirmed by recent reviews of the Spanish incubation landscape [18,20,31,53,54]. Given this gap, a secondary exploratory analysis was conducted using open data published by the Madrid City Council under the Empresas alojadas en los viveros (Companies hosted in business incubators) dataset corresponding to 1 June 2025 [55], which provided the most granular publicly available proxy for this dimension.
The dataset included 107 active ventures distributed across the six municipal incubators operating in the districts of Carabanchel, Moratalaz, Puente de Vallecas, San Blas-Canillejas, Vicálvaro, and Villaverde. Each venture was assigned to a section and division of the Spanish National Classification of Economic Activities (CNAE-2009) on the basis of its declared activity. This classification was complemented by an aggregated sector category and a knowledge and technology intensity typology that distinguishes among high-technology knowledge-intensive services (HT-KIS), market KIS, other KIS, less knowledge-intensive services, high and medium-high technology manufacturing, low and medium-low technology manufacturing, energy supply, and construction.
The sectoral distribution was heavily concentrated in advanced tertiary activities. CNAE section M (professional, scientific, and technical activities) accounted for 43.0% of incubated ventures (n = 46), and section J (information and communication) for a further 22.4% (n = 24). Together, these two sections represented two in every three ventures. Manufacturing followed at 8.4%, wholesale and retail trade at 6.5%, real estate and construction at 4.7% each, and administrative and auxiliary services at 3.7%. Hospitality, agriculture, and personal services were entirely absent.
Knowledge-intensive services accounted for 69.1% of the portfolio. Market KIS constituted the largest single category (37.4%, n = 40), encompassing legal, fiscal, and accounting advisory firms, management consultancies, architecture and engineering firms, marketing agencies, and translation services. HT-KIS represented 22.4% (n = 24), grouping ventures in software development, ICT consultancy, artificial intelligence, cybersecurity, digital platforms, and public R&D expenditure. The remaining KIS (9.3%) were distributed across audiovisual production, editorial services, training, and health and social care projects. Together with the five high and medium-high technology manufacturing firms, the HT-KIS block brings high-intensity technological activities collectively reached 27.1%. At the opposite end, traditional sectors—less knowledge-intensive services, low and medium-low technology manufacturing, and construction—accounted for 25.2% of the portfolio.

4.2. Mentoring Dimensions (Principal Component Analysis)

A principal component analysis (PCA) was performed on the 28 variables related to the design and implementation of mentoring programs in Spanish incubators. The objective was to identify the latent dimensions that structured these programs. The results confirmed that the correlation matrix was appropriate for component extraction (KMO = 0.748; Bartlett’s test: χ2 = 812.4, df = 378, p < 0.001). In the rotated component matrix, cross-loadings below 0.30 are shown in gray for readability. Inspection of the matrix confirmed that no item displayed a cross-loading above 0.40 on more than one factor, supporting the discriminant validity of the component structure.
Table 2 presents the main loadings for each component. Components were retained according to the eigenvalue greater than one criterion (Kaiser rule), complemented by visual inspection of the scree plot. Although the sixth component presented an eigenvalue close to the threshold (1.010), it was retained because it captured a distinct territorial dimension associated with the autonomous community variable (CCAA), which was considered theoretically relevant to the objectives of the study. The results showed that six components presented eigenvalues greater than 1. Together, these components explained 68.9% of the total variance.
Component 1 accounted for 41.6% of the variance, indicating the presence of a dominant dimension in the structure of mentoring programs. This first component grouped variables related to the formalization and professionalization of mentoring. These included the existence of a formal program, the type of mentoring offered, the frequency of sessions, mentor selection criteria, mentor competencies, program effectiveness assessment, and the importance attached to mentoring within the incubation process. Incubators with high scores on this component tended to operate structured, evaluated, and professionalized mentoring systems. Component 2 captured variables associated with processes of improvement and adaptation of the program. This component included support needs, program weaknesses, and proposed improvements. Component 3 reflected the relational intensity of mentoring, mainly through the duration of mentor-mentee relationships and the specific training provided to mentors. Component 4 was linked to the orientation towards mentoring networks and external collaboration, reflected in the interest in participating in mentor networks and accelerators. Component 5 captured a dimension of institutional consolidation, associated with the age of incubators and their territorial embeddedness. Component 6 mainly reflected geographical variations associated with the regional location of the Business incubators using the variables of age, size of the municipality, and autonomous community (CCAA) (Table 2).
Overall, the PCA revealed a clear and interpretable component structure. The first three components explained more than 53% of the total variance, capturing the strategic dimensions of mentoring: formalization, organizational improvement, and relational intensity.
In addition, six components were identified and interpreted according to their dominant organizational characteristics. Component 1 (Mentoring Formalization and Professionalism) was associated with absorptive capacity, whereas Component 2 (Adaptive Improvement Orientation) reflected dynamic capabilities. Component 3 (Relational Depth and Mentor Development) related to social capital processes, while Component 4 (Ecosystem Networking Orientation) captured ecosystem connectivity and external signaling dimensions. Component 5 (Institutional Consolidation) reflected accumulated organizational experience, and Component 6 captured territorial variation across autonomous communities.
Two negative loadings require additional interpretation: variable 12 (Percentage_mentored, loading = −0.768) loads negatively on Component 4 (Ecosystem Networking Orientation). This indicates that incubators with a stronger orientation toward ecosystem networking tend to mentor a lower proportion of their incubated entrepreneurs. One possible interpretation is that these incubators prioritize the quality and selectivity of mentoring relationships over broad coverage, concentrating intensive mentoring support on a subset of high-potential ventures rather than extending it uniformly across their portfolio. The variable Municipio (municipality size, loading = −0.597) loads negatively on Component 5 (Institutional Consolidation), indicating that older, more consolidated incubators tend to be located in smaller municipalities. This may reflect a more recent expansion of the Spanish incubation ecosystem that has increasingly concentrated in large urban centers, in line with the growth of metropolitan startup ecosystems, while incubators established in earlier phases show a more geographically dispersed pattern.

4.3. Typology of Mentoring

The hierarchical cluster analysis (Ward’s method, Euclidean distance) identified three distinct mentoring types. To assess the robustness of the cluster solution, an additional k-means cluster analysis with k = 3 was conducted using the PCA. The resulting classification showed strong agreement with the hierarchical Ward’s method (Rand index = 0.91; adjusted Rand index = 0.83), supporting the robustness of the three-cluster structure. The solution was further tested with k = 2 and k = 4; the three-cluster solution was confirmed as optimal according to the Calinski–Harabasz criterion (pseudo-F statistic). Cluster 3 is the largest (43.01% of incubators), followed by Cluster 1 (35.48%). Cluster 2 represents a specialized minority (21.51%), suggesting mentoring profiles that are less common but possibly differentiated in structure or intensity (Figure 1).
Cluster 1—Advanced comprehensive mentoring. Cluster 1 grouped 33 incubators (35.5% of the sample). These incubators had very high scores in the main components of the model. Incubators in this group operated highly structured mentoring programs that were observed, with formal mentor selection processes, frequent sessions, and systematic monitoring and evaluation systems. Mentors tended to exhibit combined competencies (technical, strategic, and relational) and to engage in longer mentoring relationships. This group represented the most professionalized and advanced mentoring model in the ecosystem.
Cluster 2—Moderate mentoring. Cluster 2 grouped 20 incubators (21.5% of the sample). These incubators showed intermediate scores on the main factors. The organizations in this group had operational mentoring programs, although with a lower degree of formalization or intensity than the incubators in the first group. Mentoring tended to be integrated into other incubation services, such as business training or technical advice. Some incubators in this group showed sectoral specialization or orientation towards specific entrepreneur profiles. This group represented operational but less structured mentoring model.
Cluster 3—Incipient mentoring. Cluster 3 was the largest group, with 40 incubators (43.0% of the sample). The incubators in this group presented negative scores in the main components, which reflected low levels of formalization and structure. Mentoring was most often provided informally or on an ad hoc within the incubation program. Mentor selection criteria were more flexible, specific mentor training was limited or non-existent, and mentoring evaluation systems were less frequent or less systematic than in the other clusters. This group represented basic or incipient mentoring model within the Spanish ecosystem.
Subsequently, a heat map was generated from the distances to the cluster centroids, computed from the component scores obtained in the PCA. This map visualized the relative proximity of each incubator to the three cluster centroids. It showed a clear separation among the three clusters, which supported the robustness of the clustering solution.
Incubators in Cluster 1 were located close to the centroid characterized by high scores on program formalization, rigorous selection of mentors and systematic mentoring evaluation (Figure 2). Incubators in Cluster 2 showed intermediate distances, reflecting mixed organizational configurations. Incubators in Cluster 3 were located close to their cluster centroid, which was characterized by low scores on the main mentoring dimensions, especially formalization, relational intensity, and program evaluation.

5. Discussion

The component structure identified in the analysis suggests that mentoring in incubators should be understood as a multidimensional organizational capability rather than as an isolated support activity. The components captured complementary dimensions associated with absorptive capacity [21,44], dynamic capabilities [56,57], social capital processes [35,58], ecosystem connectivity and organizational learning [25,46]. These findings indicate that the effectiveness of mentoring depends not only on the availability of mentors, but also on the organizational capacity of incubators to formalize, coordinate, and continuously improve mentoring processes over time. The presence of a distinct territorial component further highlighted the importance of regional ecosystem conditions and institutional context in shaping mentoring configurations across Spanish incubators.

5.1. Diversity Mentoring Programs

The results showed a marked heterogeneity in the structure of the mentoring programs in Spanish incubators, organized into three differentiated typologies in terms of formalization, relational intensity, and specialization. Unlike prior work focused on the effects of mentoring programs [31], this study adopts a configurational perspective, identifying distinct organizational patterns of mentoring across incubators. The cluster analysis identified a first group with advanced comprehensive mentoring (35.5%), a second with moderate mentoring (21.5%), and a third, largest, with incipient mentoring (43.0%). This distribution shows that a considerable number of incubators have structured mentoring systems. However, an even higher proportion operates with models that are poorly formalized or in early stages of development, a pattern consistent with the literature that documents the organizational diversity of incubators and their uneven impact on the performance of the incubated firms [59,60,61]. The coexistence of advanced and incipient mentoring suggests that mentoring continues to be a critical resource whose degree of professionalization varies substantially within the Spanish ecosystem [2,3,5]. These findings suggest that mentoring does not operate as a homogeneous service, but as an organizational capability whose structure and level of professionalization shape the generation of value in incubation processes.
The PCA reinforces this interpretation: Component 1 explains 41% of the total variance and groups variables associated with program formalization, the selection and continuous training of mentors, and systematic mentoring evaluation. In this sense, the organizational quality of mentoring constitutes the central axis structuring the observed models, and the results reinforce the idea that the formalization of mentoring acts as a structuring mechanism that transforms individual interactions into replicable organizational processes, in line with the literature on organizational capabilities. As reports that the combination of structured processes and highly trained mentors has been shown to increase learning, scalability, and profitability in disadvantaged contexts. Several studies [5,62] indicate that the effectiveness of mentoring in incubators depends on clear objectives, systematic feedback, and mentors’ professional experience.
Incubators with advanced mentoring show the highest scores on mentoring factors and performance indicators, while those with incipient mentoring systematically record the lowest scores, which supports the proposition that the organizational design of the mentoring program is a key factor associated with its effectiveness [63,64]. Advanced-mentoring incubators have greater perceived impacts on entrepreneurs’ confidence and skills, on attracting investment, and on the survival of incubated firms. These effects are especially pronounced when entrepreneurs start from low levels of human and social capital [7]. From this perspective, mentoring can be interpreted as a mechanism that amplifies the impact of other organizational resources, especially in such settings.
From a sustainability perspective, these results align with the literature on incubators oriented towards the Sustainable Development Goals (SDGs). This suggests that mentoring acts as a mediating mechanism that connects incubators’ organizational capacities with sustainability outcomes at the systemic level. Prior work [61] shows that incubators with an explicit orientation towards sustainability integrate environmental and social criteria into project selection, service design, and outcome evaluation. Although the sample is not limited to this type of incubator, the association between more structured mentoring models and better business outcomes suggests that strengthening mentoring contributes as an indirect mechanism progression in the SDGs related to decent work and economic growth (SDG 8), industry, innovation and infrastructure (SDG 9), and sustainable cities and communities (SDG 11) [65,66,67]. However, the large share presence of incubators with poorly structured systems (43.0%) poses a risk of segmentation: entrepreneurs in advanced mentoring environments receive more intensive and professionalized support than those in environments with less organizational capacity [5,31,65], which reinforces the need to move towards more structured models as a means of improving both the equity and effectiveness of entrepreneurial support. Therefore, heterogeneity in the quality of mentoring can become a factor that generates inequalities in access to opportunities within the entrepreneurial ecosystem [5,65]. These findings extend prior research by showing that mentoring in incubators should be understood not as a homogeneous support service, but as a heterogeneous organizational capability whose configuration systematically shapes entrepreneurial outcomes.
The typologies identified here converge with evidence from other national ecosystems. A systematic review confirms that incubators and accelerators differ substantially in organizational design and mentoring intensity [4]. Mentoring mechanisms nevertheless remain among the least studied dimensions of the incubation process. This convergence suggests that the variation in maturity identified in the present study reflects a structural pattern, not a Spain-specific anomaly.
Formalization and intensity consistently are associated with differential outcomes. Rechter and Avnimelech [9] found that founders engaged in intensive personal mentoring outperform those relying on ad hoc expert interactions across five dimensions: human capital formation, network expansion, fundraising capacity, legitimacy, and operational advancement. This is consistent with the difference between the advanced and incipient clusters, suggesting that organizational design—not merely mentoring presence—is associated with differential outcomes. European accelerators show the same pattern [3]: structured support is associated with reference [13] documents a comparable asymmetry in Latin America, and Start-Up Chile links structured support to significant improvements in venture performance and financing capacity [6].
The three structuring dimensions—formalization, relational depth, and ecosystem orientation—rest on frameworks with demonstrated international applicability [21,25,44,46,68,69]. The cluster distribution, however, is likely context-specific. The predominance of incipient mentoring (43.0%) may reflect Spain’s mixed public–private governance and territorial heterogeneity [22,23,31]. Thresholds, funding structures, and mentor network density will vary elsewhere [62,63,64,70].

5.2. Towards a Model of Mentoring Maturity in Business Incubators

The results of the cluster analysis allow a move from an empirical typology to the conceptualization of the Mentoring Maturity Model in Business Incubators (Figure 3), which graphically summarizes the progression across maturity levels. The three configurations identified: incipient mentoring (43.0%), moderate mentoring (21.5%), and advanced mentoring (35.5%), are interpreted as progressive stages in the institutional development of mentoring within incubators, characterized by increasing levels of formalization, professionalization and results orientation (Table 3).
This reinterpretation enables a shift from a descriptive typology to an explanatory model of the organizational development of mentoring. These results are consistent with the literature on organizational maturity models, which describes how certain capacities evolve from informal and reactive forms to more systematic and institutionalized configurations [68,69,71,72]. This conceptualization of organizational maturity refers to the extent to which organizations have developed and institutionalized structures, competencies, and routines that allow them to systematically manage and renew strategic capabilities [57,68,69]. This evolutionary pattern can be interpreted through the organizational capabilities’ perspective, according to which organizations progressively develop routines and competencies that allow them to create and sustain competitive advantages in complex environments [57,73,74]. In the context of incubators, the capability to design and manage effective mentoring programs is a strategic organizational capability that facilitates entrepreneurial learning, knowledge transfer, and access to support networks [33].
As incubators accumulate experience and institutionalize their practices, mentoring is transformed from an activity based on personal relationships to an organizational system of strategic support, in line with the studies on organizational learning and routine evolution [69,75,76,77,78]. Accordingly, mentoring can be conceptualized as a dynamic capability that evolves through processes of learning, experimentation, and progressive formalization.
Table 3 shows that the incipient level (43.0%) is characterized by predominantly informal and reactive (ad hoc) mentoring, with poorly structured mentoring selection criteria, little dedicated mentor training, and limited or nonexistent evaluation systems. At this stage, the effectiveness of the mentoring support depends more on the occasional availability of mentors than on a systematic organizational design. The moderate level (21.5%) shows a more defined integration of mentoring into incubation services, with some sectoral specialization and basic monitoring mechanisms, although evaluation and organizational learning remain partial. Finally, the advanced level (35.5%) corresponds to incubators that have developed highly formalized systems, with rigorous mentor selection criteria, continuous training, systematic monitoring of mentor-mentee relationships and regular evaluation of results through performance and satisfaction indicators. At this level, mentoring is fully integrated into the incubation strategy and managed as a core organizational capability aimed at improving the performance and survival of incubated startups.
This progression reflects an accumulation of organizational capabilities that may enable incubators to enhance the effectiveness and scalability of their mentoring programs. However, advancing through maturity levels is not automatic: it requires deliberate organizational investments and the activation of specific learning mechanisms at each transition point. The transition from incipient to moderate mentoring can typically be enabled by three mechanisms: (i) the introduction of basic formalization routines, such as defining minimum criteria for mentor selection and establishing a minimum frequency of mentor–mentee sessions; (ii) peer learning from more mature incubators through participation in national or regional incubation networks, which reduces the cost of developing in-house expertise; and (iii) the adoption of simple feedback instruments—session logs, satisfaction surveys—that generate the first systematic information about program quality. The transition from moderate to advanced mentoring would require more substantial organizational transformation: (i) the institutionalization of rigorous mentor selection and accreditation criteria linked to demonstrated competencies rather than availability or seniority; (ii) the development of structured evaluation systems capable of measuring mentoring impact on venture performance indicators such as survival rates, funding access, and revenue growth; and (iii) active participation in reference certification programs—such as the Business Mentor madri+d certification or the European Mentoring and Coaching Council accreditation—that provide external validation and access to leading practices. Sustaining advanced mentoring requires, in turn, embedding it as a core strategic capability through continuous mentor training, KPI-driven program management, and active orchestration of the surrounding entrepreneurial ecosystem. These transition mechanisms are presented as theoretical propositions grounded in Crossan, Lane and White’s organizational learning model [76], which describes how individual learning is progressively institutionalized through the four processes of intuiting, interpreting, integrating, and institutionalizing, and in the dynamic capabilities framework [68], in which sensing, seizing, and reconfiguring capabilities are developed incrementally rather than acquired instantaneously. Empirical confirmation of these transition pathways would require longitudinal data on incubator development, which is identified as a priority direction for future research.
The interpretation of these three levels finds additional support in two well-established theoretical frameworks in organizational theories. From the Resource-Based View (RBV), sustained competitive advantage depends on having resources that are both rare and difficult for competitors to imitate or transfer [77]. From this perspective, advanced mentoring programs can be understood as strategic organizational resources within business incubators. First, they are rare: the data show that only 35.5% of incubators in the sample have developed advanced mentoring systems, while 43.0% remain at an incipient level—a distributional asymmetry that suggests the rarity of fully formalized mentoring capability in the Spanish ecosystem. Second, they are imperfectly imitable and relatively immobile: the quality of advanced mentoring is embedded in tacit organizational routines, accumulated institutional know-how, long-standing mentor–mentee relationships, and a culture of systematic evaluation that competitors cannot easily replicate in the short term. Similarly, Barney [79] identifies as sources of sustained competitive advantage precisely those resources that enable organizations to implement strategies that competitors cannot easily duplicate. The maturity levels of mentoring reflect these differences in the endowment, quality, and inimitability of organizational resources—qualified mentors, structured programs, rigorous evaluation systems—that shape the ability of incubators to generate differential outcomes for their incubated ventures. From the perspective of dynamic capabilities, ref. [57] defines these as the ability of the organization to detect opportunities, take advantage of them and reconfigure assets in changing environments; capabilities that clearly distinguish incubators at the advanced level from entry-level ones. Recent reviews confirm that both approaches are complementary and particularly relevant in contexts of organizational transformation and competitive intensification [67,80,81,82]. Likewise, advancing toward higher maturity levels requires progressively greater organizational investments and coordination capabilities, consistent with the logic of increasing complexity and escalating capability requirements [57,83]. Thus, mentoring acts as a strategic resource whose quality and structuring condition the ability of incubators to generate competitive advantages in supporting entrepreneurship [67,81,82].
Therefore, the proposed Mentoring Maturity Model in Business Incubators (Figure 3) provides a diagnostic framework to assess the level of development of mentoring programs across different contexts and to identify areas for improvement in key dimensions such as mentor selection and training, interaction intensity, and monitoring and evaluation mechanisms [63,69]. Rather than prescribing a single developmental trajectory, the model supports the design of context-specific and differentiated interventions tailored to the institutional conditions of each incubator, in line with approaches emphasizing productive heterogeneity and adaptive policy design [63,69,84,85].
By interpreting the identified mentoring typologies as progressive stages of organizational development, the model provides a basis for comparing levels of maturity across incubators and territorial contexts [5,31]. As incubators accumulate experience and institutionalize their practices, mentoring evolves from an activity primarily based on personal relationships into a structured organizational system for knowledge transfer and entrepreneurial capability maturity [76,77,78,83]. This model contributes to the literature by providing a configurational and evolutionary perspective on mentoring, linking organizational design with the development of dynamic capabilities in entrepreneurial support systems.

5.3. Practical Implications: Improving Mentoring Across Maturity Levels

Based on the typology, practical actions are derived to improve the design and effectiveness of mentoring programs. They represent progressive organizational development trajectories. They are adapted to the maturity level of each incubator. This approach is consistent with prior literature on mentoring and organizational capabilities [4,21,86]. These actions can be synthesized into a set of best practices structured by mentoring maturity levels (Table 4). This structure enables the identification of progressive improvement pathways. The table below is derived from the empirical typology identified in this study and operationalizes the mentoring maturity model, interpreted in light of existing literature.
Recommendations are differentiated by actor and maturity level to facilitate operational application. At the incipient level, managers should introduce a minimum viable mentoring structure: a written mentor selection protocol, a session log, and a basic satisfaction survey applied at the end of each cycle. These instruments require minimal investment. Policymakers should condition a minimum share of public incubation funding—indicatively 5–10% of the annual operating budget—on the documentation of mentoring activities, creating a structural incentive for formalization without imposing prescriptive models. At the moderate level, managers should improve mentor–mentee matching through criteria-based selection (sector expertise, entrepreneurial experience, availability), establish bi-weekly sessions of at least 60 min, and implement process-based evaluation covering session regularity, mentor satisfaction, and mentee-reported learning outcomes. Policymakers should support regional mentor certification schemes and facilitate participation in national mentoring networks, using public procurement and grant conditions as levers. At the advanced level, managers should operate mentoring as a data-driven strategic capability, tracking KPIs such as session hours per venture, 12- and 24-month survival rates, funding secured, and mentor satisfaction scores, and maintaining active participation in international networks such as the Enterprise Europe Network or EMCC. Policymakers should recognize advanced maturity through differentiated funding instruments—performance-based grants tied to verified outcome indicators—and promote peer-learning programs between incubators at different maturity levels.
At the incipient level, actions focus on minimal formalization, and the introduction of basic routines is recommended. This stage reduces dependence on informal interactions and enables initial organizational learning [21,87,88]. At the moderate level, the focus shifts to improving mentor–mentee matching, increasing mentor professionalization, and implementing structured evaluation mechanisms. These actions enhance consistency and program effectiveness [4,87,89]. At the advanced level, mentoring becomes a core strategic capability. At this level, mentoring emphasizes impact evaluation, ecosystem integration, and specialization. These elements contribute to scalability, long-term performance, and sustainable entrepreneurial development [4,21,90].
Overall, mentoring improvement does not follow a single trajectory. It reflects differentiated paths of organizational development. As capabilities accumulate, incubators evolve from informal practices to structured and strategic mentoring systems, contributing to more sustainable and inclusive entrepreneurial ecosystems [57,70,84].
Several limitations of this study should be acknowledged. First, the cross-sectional design does not allow causal relationships between mentoring configurations and entrepreneurial outcomes to be established. Longitudinal panel data would be necessary to confirm the developmental trajectory proposed in the maturity model. Second, the analysis relies on self-reported information provided by incubator managers, which may introduce social desirability bias and limits the use of objective performance indicators [91]. Another important limitation is the absence of mentee-level information, such as the sector, size, or development stage of the incubated ventures. As a result, it is not possible to analyze whether the effectiveness of mentoring differs according to firm characteristics. Future research could address this issue by using matched incubator–startup datasets.
Future research should prioritize the following: longitudinal designs to track the development of incubators across maturity levels over time; comparative multi-country studies, particularly within Southern and Eastern European ecosystems sharing similar institutional conditions; mixed-methods approaches to capture the qualitative dynamics of mentor–entrepreneur relationships and the organizational processes underlying maturity transitions; and studies collecting matched incubator–startup data to enable analysis of differential mentoring effects by venture sector, size, and development stage.

6. Conclusions

The findings confirm the usefulness of multivariate analysis for understanding the organizational complexity of mentoring programs in business incubators. The combination of principal component analysis (PCA) and cluster analysis makes it possible to identify consistent and comparable organizational patterns within the Spanish entrepreneurial ecosystem. Overall, the findings provide empirically grounded insights into the configurations adopted by incubators and their implications for entrepreneurial support.
Regarding the first research question, the analyses identify three distinct and internally consistent mentoring typologies: incipient mentoring (43.0%), characterized by informal and ad hoc practices with limited evaluation and low ecosystem orientation; moderate mentoring (21.5%), featuring partial formalization and basic monitoring mechanisms; and advanced mentoring (35.5%), defined by rigorous mentor selection, continuous training, systematic evaluation, and full strategic integration within the incubation model. These configurations reflect clear organizational differences in the way incubators designed, managed, and structured their mentoring programs.
The typologies identified through cluster analysis are subsequently interpreted as progressive levels of the Mentoring Maturity Model, in which incubators evolve from informal and reactive practices toward more structured and strategically managed systems. This evolutionary interpretation, grounded in the dynamic capabilities framework and the organizational learning perspective, is supported by the distributional patterns observed across the clusters. The model offers a dual contribution. As a diagnostic tool, it enables incubators to assess their level of mentoring maturity and identify potential improvement pathways. As a comparative framework, it provides researchers and policymakers with a standardized basis for evaluating mentoring quality across different organizational and territorial contexts.
Regarding the third research question, the findings suggest that incubators belonging to the advanced mentoring cluster tend to report stronger entrepreneurial outcomes, particularly in relation to startup survival, entrepreneurial capability development, and access to networks and external financing. In contrast, incubators operating with incipient mentoring models show lower levels of formalization, weaker evaluation systems, and more limited mentoring intensity. The uneven distribution of mentoring maturity across the ecosystem also points to territorial and organizational differences in the quality of entrepreneurial support available to startups. Against this background, mentoring appears not only as a support activity within incubation programs but also as an organizational capability that may contribute to the resilience, competitiveness, and long-term sustainability of entrepreneurial ecosystems.

Author Contributions

Conceptualization, A.A.-C. and A.G.M.; methodology, A.A.-C., J.L.M.B. and C.D.-P.-H.; software, J.L.M.B.; validation, A.A.-C. and A.G.M.; formal analysis, A.A.-C. and J.L.M.B.; investigation, A.A.-C., A.G.M. and C.D.-P.-H.; resources, C.D.-P.-H.; data curation, A.A.-C.; writing—original draft preparation, A.A.-C. and C.D.-P.-H.; writing—review and editing, A.A.-C. and C.D.-P.-H.; visualization, J.L.M.B.; supervision, C.D.-P.-H. and A.G.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Rey Juan Carlos University (protocol code: V1025, date of approval: 24 March 2024).

Informed Consent Statement

In accordance with Regulation (EU) 2016/679 (General Data Protection Regulation [GDPR], 2016) and Organic Law 3/2018 on the Protection of Personal Data and Guarantee of Digital Rights (LOPDGDD, 2018), data processing was carried out lawfully, fairly, and transparently, for specific purposes, and in line with the principle of data minimization. Anonymised data do not qualify as personal data under these regulations and are therefore outside their scope or subject to less restrictive provisions in the context of scientific research, allowing their use without additional consent requirements.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

This work is part of the results of a joint research agreement between Rey JuanCarlos 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 2024–2025”. We thank these two institutions for their support. We also thank the OpenInnova High Performance Research Group at Rey Juan Carlos University and the 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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Sustainability 18 05407 g001
Figure 1. Dendrogram of Mentoring Programs in Business Incubator. The mentoring programs for each cluster are included in the red circle.
Figure 1. Dendrogram of Mentoring Programs in Business Incubator. The mentoring programs for each cluster are included in the red circle.
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Figure 2. Heatmap of cluster centroids.
Figure 2. Heatmap of cluster centroids.
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Figure 3. Mentoring maturity model in business incubators.
Figure 3. Mentoring maturity model in business incubators.
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Table 1. Mentoring program variables.
Table 1. Mentoring program variables.
VariableVariable Description
ExperienceYears since establishment
Municipality_sizeMunicipality size (1 = <20 k; 2 = 20–100 k; 3 = >100 k)
Region_CCAAAutonomous Community where the incubator is located
1_Formal_programFormal mentoring program (0 = No; 1 = Yes)
2_Type_programDegree of mentoring integration in the incubator services. (0 = None; 1 = Ad hoc; 2 = Integrated; 3 = Structured)
3_Type_mentoringMentoring type (0 = None; 1 = Other; 2 = Group; 3 = Individual; 4 = Specialized)
4_DurationDuration of mentoring (0 = None; 1 = Short; 2 = Medium; 3 = Long)
5_Session_frequencySession frequency (0 = None; 1 = On demand; 2 = Low; 3 = High)
6_Effectiveness_evaluationEvaluation methods (0 = None; 1 = Single; 2 = Two; 3 = Multiple)
7_Program_importancePerceived importance (1–5 scale)
8_Support_needsMain support needs (1–5 categories)
9_Desired_trainingDesired training areas (1–4 categories)
10_Mentor_networkInterest in participating in a mentor and accelerator network. Scale: 0 = No; 1 = Yes.
11_Total_menteesTotal mentees. Scale: 0 = No program/no data; 1 = 1–25; 2 = 26–50; 3 = 51–100; 4 = 101–500; 5 = >500.
12_Percentage_mentoredPercentage of entrepreneurs receiving mentoring. Scale: 0 = No data/no program/none; 1 = 0–24%; 2 = 25–49%; 3 = 50–74%; 4 = 75–99%; 5 = 100%.
13_Selection_criteria_mentorsMentor selection criteria. Scale: 0 = None; 1 = Availability; 2 = General experience; 3 = Specific fit; 4 = Formal/advanced criteria.
14_Competencies_mentorsMentor competencies. Scale: 0 = Not reported; 1 = Relational skills; 2 = General business experience; 3 = Sector-specific expertise; 4 = Advanced combination.
15_Training_mentorsMentor training provided. Scale: 0 = No; 1 = Yes.
16_Compensation_mentorsMonetary or non-monetary compensation received by mentors. Scale: 0 = No; 1 = Yes.
17_Impact_confidenceReported impact of mentoring on entrepreneurs’ confidence. Scale: 0 = No; 1 = Yes.
18_Impact_business_competenciesImpact on development of entrepreneurs’ business competences. Scale: 0 = No; 1 = Yes.
19_Impact_financing_accessImpact on entrepreneurs’ capacity to attract investment. Scale: 0 = No; 1 = Yes.
20_Impact_business_survivalImpact on business survival. Scale: 0 = No; 1 = Yes.
21_Impact_incubator_successPerceived effect of mentoring on incubator program success. Scale: 0 = No; 1 = Yes.
22_Impact_program_outcomesDifferentials in outcomes between mentored and non-mentored entrepreneurs. Scale: 0 = No; 1 = Yes.
23_Success_entrepreneursWhether mentored entrepreneurs have better results than non-mentored ones. Scale: 0 = No; 1 = Yes.
24_Success_casesExistence of concrete success cases where mentoring was key. Scale: 0 = No concrete example; 1 = At least one specific success case described.
25_Challenges_implementationMain challenges in implementing the mentoring program. Scale: 1 = Time and availability; 2 = Mentors (recruitment, matching, quality); 3 = Entrepreneur commitment, attitudes and receptiveness; 4 = Program design and management; 5 = Resources and fit with needs/results.
26_Weaknesses_improvementsAspects of the mentoring program that could be improved. Scale: 1 = Time, duration, number of sessions, availability; 2 = Mentors (number, profile, diversity, involvement, remuneration); 3 = Entrepreneur (motivation, trust, behavior, emotional aspects); 4 = Program design/management/methodology; 5 = External resources and results (funding, networks, link with outcomes).
27_Suggested_improvementsSuggested improvements. Scale: 0 = No improvements/no data/not applicable; 1 = Training/contents for mentors or entrepreneurs; 2 = Program design, structure and management; 3 = Mentors (number, profile, specialization); 4 = Networks, diffusion and external positioning; 5 = Financing/access to funds.
28_Critical_success_factorsOther factors considered critical for program and entrepreneurs’ success. Scale: 0 = No factor/no data; 1 = Training, capacities and human/soft aspects; 2 = Program design, management and follow-up; 3 = Mentors/mentor-mentee relationship/accompaniment; 4 = Networks, ecosystem, visibility and collaboration; 5 = Financing/economic resources/support.
Table 2. Rotated principal component loading matrix.
Table 2. Rotated principal component loading matrix.
ItemsLoadingEigenvalueExplained Variance (%)AccumulatePC
1_Formal_program0.839169.9856841.60741.6071
17_Impact_confidence0.837118
5_Session_frequency0.698648
13_Selection_criteria_mentors0.831112
2_Type_program0.82189
3_Type_mentoring0.817282
6_Effectiveness_evaluation0.758979
7_Program_importance0.755163
16_Compensation_mentors0.72161
14_Competencies_mentors0.808289
25_Challenges_implementation0.687708
24_Success_cases0.566377
11_Total_mentees0.566356
9_Desired_training0.6995641.608126.70148.3082
8_Support_needs0.693869
26_Weaknesses_improvements0.51306
4_Duration0.7032381.408475.86954.1763
15_Training_mentors0.831665
10_Mentor_network0.5890511.305745.44159.6174
12_Percentage_mentored−0.768334
Experience0.7121451.222385.09364.7105
Municipio−0.597475
CCAA0.9530351.010074.20968.9196
Table 3. Mentoring Maturity Model for Business Incubators.
Table 3. Mentoring Maturity Model for Business Incubators.
Maturity LevelFormalizationMentor ManagementProgram EvaluationExpected Results
Incipient MentoringInformal or ad hoc mentoring with no defined structureMentors selected based on availability; no formal trainingNo formal evaluation; based on informal perceptionsLimited impact on learning and network access
Moderated MentoringMentoring integrated into incubation services with basic structureMentors selected based on experience; some training providedBasic evaluation using surveys or session trackingModerate improvement in skills and networking
Advanced MentoringFormalized mentoring integrated into the incubator’s strategyRigorous selection and continuous mentor trainingSystematic evaluation using performance and satisfaction metricsStrong impact on growth, funding access, and survival
Table 4. Best Practices Management Proposal.
Table 4. Best Practices Management Proposal.
Key MeasuresGovernance Level 1Mentoring Maturity Levels
IncipientModerateAdvanced
Program formalizationMAd hoc/non-formal (✔✔✔)Partially formalized (✔✔)Fully formalized and integrated (✔✔✔)
Mentor selectionMAvailability-based (✔✔✔)Experience/fit-based (✔✔✔)Rigorous + accreditation (✔✔✔)
Frequency and intensityMSporadic and reactive (✔✔)Regular and planned (✔✔✔)Intensive and continuous (✔✔✔)
Mentor trainingM and PNone or occasional (✔)Basic training (✔✔✔)Continuous training (✔✔✔)
Matching mentor–entrepreneurMAd hoc (✔)Criteria-based (✔✔✔)Data-driven + expert validation (✔✔✔)
Evaluation systemsMInformal (✔)Process-based evaluation (✔✔)Integrated KPI system (✔✔✔)
Impact evaluationPNot measured (✖)Partial measurement (✔)Robust impact evaluation (✔✔✔)
Networks and ecosystemPLimited contacts (✖)Developing networks (✔✔)Orchestrated ecosystem (✔✔✔)
SpecializationPGeneralist (✖)Partial (✔✔)High (✔✔✔)
Strategic integrationM and PNot integrated (✖)Partial (✔)Core strategic capability (✔✔)
1 M, Manager; P, Policymaker; ✖ = Not applicable; ✔ = Low applicability; ✔✔ = Moderate applicability; ✔✔✔ = High applicability.
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Asensio-Ciria, A.; De-Pablos-Heredero, C.; Botella, J.L.M.; Martínez, A.G. Mentoring Patterns in Business Incubators: A Typology and Organizational Maturity Model from Spain. Sustainability 2026, 18, 5407. https://doi.org/10.3390/su18115407

AMA Style

Asensio-Ciria A, De-Pablos-Heredero C, Botella JLM, Martínez AG. Mentoring Patterns in Business Incubators: A Typology and Organizational Maturity Model from Spain. Sustainability. 2026; 18(11):5407. https://doi.org/10.3390/su18115407

Chicago/Turabian Style

Asensio-Ciria, Ana, Carmen De-Pablos-Heredero, Jose Luis Montes Botella, and Antón García Martínez. 2026. "Mentoring Patterns in Business Incubators: A Typology and Organizational Maturity Model from Spain" Sustainability 18, no. 11: 5407. https://doi.org/10.3390/su18115407

APA Style

Asensio-Ciria, A., De-Pablos-Heredero, C., Botella, J. L. M., & Martínez, A. G. (2026). Mentoring Patterns in Business Incubators: A Typology and Organizational Maturity Model from Spain. Sustainability, 18(11), 5407. https://doi.org/10.3390/su18115407

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