Corps de l’article

Open innovation (OI) encompasses the evolving challenges, norms, and practices of innovation processes (Audretsch & Belitski, 2023). It has become a key innovation strategy (Hsieh et al., 2018; Kobarg et al., 2019) as small and large firms, startups, and incumbents promote OI activities, deepening and broadening their portfolio of activities with innovation partners (Roper et al., 2017; Audretsch et al., 2023). Universities now act as “entrepreneurial universities,” extending their mission beyond teaching and research by forging closer links with the market (Spender et al., 2017; Stal et al., 2016). To facilitate this, universities have established university business incubators (UBIs) that offer valuable resources and activities, such as consulting, IP licensing, and spinout company creation to student entrepreneurs (Spender et al., 2017).

UBIs function as integrated OI networks within universities, providing services that foster startups creation and growth (Spender et al., 2017; Robaczewska et al., 2019). Some authors classify UBI services into three categories: infrastructure, business support, and networking (Bergek & Norman, 2008; Bruneel et al., 2012). By involving students in OI practices that are collaborative activities performed inside and outside incubated start-ups (Audretsch & Belitski, 2023; Sohail et al., 2023), UBIs promote collaboration and innovation and serve as intermediaries connecting large companies with start-ups for value creation (Dutt et al., 2016; Dahlander & Gann, 2010). They also facilitate global collaboration and exchange of ideas, knowledge, research, and best practices among startups, researchers, and industry experts from different countries (Fernández Fernández et al., 2015; Blackburne and Buckley, 2019) through partnerships with other universities and institutions (Mian et al., 2016; Grilli & Marzano, 2023).

OI practices offer significant potential for start-ups incubated in UBIs, providing valuable access to academic resources, fostering multidisciplinary collaboration, and exposing students to real-world and global challenges (Stal et al., 2016; Briggs et al., 2019). However, student entrepreneurs face challenges such as limited resources for networking, the need to balance academic commitments with entrepreneurship, and limited industry experience (Colombo & Piva, 2012; Stal et al., 2016). Overcoming these barriers requires tailored approaches and effective adoption of OI practices within UBIs.

Despite the limited attention given to student entrepreneurs’ management of OI practices and the impact on the performance of university-incubated start-ups (UISs) (Vanderstraeten et al., 2020; Spender et al., 2017), recent studies have highlighted the mediating role of OI practices in innovation performance (Roh et al., 2021) and the importance of investigating OI motives and their impacts (Lee et al., 2019). Moreover, an overemphasis on networking services in current literature overlooks the potential influence of other incubation services, such as infrastructure and business support, leaving a considerable gap in understanding their role (Nijssen & Van der Borgh, 2017). This reveals an urgent need for a more comprehensive exploration of various incubation services, especially within the university context, and their implications on startup performance (Eveleens et al., 2017; Hughes et al., 2007). Furthermore, from an empirical point of view, more studies focus on large groups and SMEs, while only a few studies consider how startups, particularly, incubated ones, use OI (Spender et al., 2017).

To fill these gaps, this study aims to comprehensively examine the performance of UISs by assessing the use of OI practices, the underlying OI motives, and the role of UBI services, including infrastructure, business support, and networking services, by student entrepreneurs. We propose a model to demonstrate the mediating effect of the use of OI practices on the relationship between OI motives and the performance of UISs and that of the use of UBI services on the relationship between OI practices and performance. To validate our model, we conducted a quantitative study of Latvian UBIs, considering the country’s involvement in OI initiatives and the importance of exploring non-developed contexts (Slaidins et al., 2015; Bigliardi et al., 2021).

Our research offers substantial contributions to both OI and incubation literature. Focusing on UBIs and student entrepreneurs, we elucidate the mediating role of OI practices in the relationship between OI motives and UBI performance. We ascertain that inbound OI practices are pivotal and effectively facilitated by the UBIs, while outbound OI practices receive less attention owing to resource constraints. Our findings also highlight the significance of networking services in enhancing UBI performance, underscoring the dynamic position of student entrepreneurs in leveraging services for success. Lastly, our findings offer valuable insights for student entrepreneurs, UBI managers, and policymakers, underlining the essence of supporting UBIs, their services, and their OI initiatives to enhance startup performance.

Theory and Hypotheses

Mediating role of adopting Open Innovation practices

Entrepreneurial motivation plays a crucial role in entrepreneurship theory (Shane et al., 2003), as it is a driving force and an antecedent for student entrepreneurs to initiate and foster the growth of their UISs (Van Gelderen et al., 2015; Murnieks et al., 2020). While traditional economic theories (Schumpeter, 1934; Hébert & Link, 1988) predominantly associated entrepreneurship with pecuniary motivations focused on financial gains, recent empirical studies have revealed the significant influence of non-pecuniary factors, such as independence, contribution, and innovation (Block et al., 2015; Yitshaki & Kropp, 2016). In the context of OI, student entrepreneurs are motivated by a blend of both pecuniary and non-pecuniary motives (Dahlander & Gann, 2010). Pecuniary OI motives involve introducing innovations to the market to secure financial support through public subsidies, venture capital, and partnerships with established companies, consultants, and research institutions (Mian et al., 2016; Hughes et al., 2007; Hansen et al., 2000; Garcés et al., 2018). By contrast, non-pecuniary OI motives inspire student entrepreneurs to seek knowledge about new technologies, market opportunities, and product development within UBIs (Dahlander & Gann, 2010; Garcés et al., 2018).

These OI motives significantly impact UISs performance (Dahlander & Gann, 2010). For instance, pecuniary OI motives drive student entrepreneurs to commercialize their innovations and acquire external technologies, thereby enhancing the innovative and competitive performance of their startups (Hughes et al., 2007; Garcés et al., 2018). Conversely, non-pecuniary OI motives encourage student entrepreneurs to collaborate with external partners, leveraging complementary knowledge and resources to contribute to the innovative performance of their UISs (Gassmann & Enkel, 2004).

UISs exercises strategic behaviors (Murnieks et al., 2020) in the form of OI practices to influence performance. From both static (RBV theory) and dynamic (e.g., dynamic capabilities) theories, the adoption of OI practices highlights the significant role of internal and external resources and capabilities in creating and maintaining a competitive edge (Vanhaverbeke & Cloodt, 2014;). To gain a competitive advantage and succeed (Pereira & Bamel, 2021), UISs use two OI practices to access these external and internal resources, including assets, attributes, knowledge, and information. Inbound OI practices involve forming strategic partnerships with customers, suppliers, research centers, and even competitors to enhance internal innovation, accelerate new product development, and access market opportunities and technologies (van de Vrande et al., 2009). Outbound OI practices encompass activities such as commercializing unused licenses or technologies; licensing technology to larger companies; and outsourcing to leverage knowledge, skills, and cost-sharing (Usman & Vanhaverbeke, 2017; Yun et al., 2019; Gassmann & Enkel, 2004).

Having clear pecuniary and non-pecuniary OI motives guides startups in selecting and using appropriate OI practices. For instance, inbound pecuniary OI practices focus on acquiring knowledge for the innovation process, whereas non-pecuniary inbound practices focus on sourcing external knowledge without direct financial rewards (Dahlander & Gann, 2010). Non-pecuniary motives also drive outbound OI practices in which startups share internal knowledge to stimulate the emergence of new ideas and innovations (van de Vrande et al., 2009; Gassmann & Enkel, 2004). Pecuniary outbound practices involve selling products or knowledge through licensing to partners to commercialize innovation (Dahlander & Gann, 2010).

In addition, dynamic theories have attempted to explain the link between OI and performance (Vanhaverbeke et Clooodt, 2014); existing research indicates that both inbound and outbound OI practices positively impact the performance of large companies, with SMEs benefiting even more (Van de Vrande et al., 2009; Usman & Vanhaverbeke, 2017). While startups differ from SMEs in terms of liabilities and resource scarcity, they share common strengths and challenges due to their small and nascent nature (Usman & Vanhaverbeke, 2017). Therefore, it can be inferred that a positive relationship exists between the use of outbound and inbound OI practices and UISs performance.

In conclusion, OI motives are closely linked to the use of OI practices, which impact UISs performance. To enhance innovative and competitive performance, it is crucial that OI motives acknowledge the role of OI practices as a conduit. OI’s mediating role in performance is also confirmed in other organizational contexts (Roh et al., 2021), but it is neglected in the incubator context. Therefore, we propose the following hypothesis:

H1: The use of OI practices by student entrepreneurs mediates the relationship between OI motives and UISs performance.

Mediating role of the use of university business incubator services

OI significantly influences UISs performance through inbound and outbound practices (van de Vrande et al., 2009, Yun et al., 2019, and Usman & Vanhaverbeke, 2017). UBIs act as OI intermediaries (Spender et al., 2017), and the connection between OI and startups performance depends on UBI services. UBIs offer three primary service categories: (1) infrastructure services, which include office spaces and shared facilities (Bruneel et al., 2012; Mian et al., 2016); (2) business support services, encompassing professional training and counseling (Soetanto & Jack, 2016); and (3) networking services, providing access to internal and external networks for interaction with fellow incubatees, potential customers, suppliers, technology partners, and investors (Bergek & Norman, 2008; Scillitoe & Chakrabarti, 2010). Strategic use of these UBI services by student entrepreneurs significantly influences various aspects of UISs performance. The mediating role of UBI services, theoretically supported by studies like Ayad et al. (2022) and Jiang et al. (2022), demonstrates how OI practices enhance performance through specific incubation support services.

In OI environments, UBIs utilize coworking spaces as infrastructure services to facilitate physical proximity and encourage collaboration (Tremblay & Scaillerez, 2020). Student entrepreneurs leverage this spatial closeness to enhance innovation (Lichtenstein, 1992; Mian et al., 2016; Bruneel et al., 2012). They also harness this spatial proximity, as emphasized by Lichtenstein (1992), to actively participate in OI practices, resulting in positive impacts on startup innovation performance. Additionally, OI contexts necessitate UBIs to provide infrastructure services like advanced technologies, support for internationalization, and knowledge-intensive product development (Sharma and Blomstermo, 2003). These services enable students to concentrate on core business activities and OI practices, leading to efficient resource allocation, cost reduction, and economies of scale, ultimately enhancing competitiveness (Bøllingtoft & Ulhøi, 2005). This efficiency reduces startup expenses, facilitating investments in R&D and market research, resulting in enhanced innovation and competitiveness (Grilli & Marzano, 2023; Bøllingtoft & Ulhøi, 2005; Aerts et al., 2007; McAdam & Marlow, 2011).

Therefore, OI practices’ impact on performance is intricately mediated by strategic infrastructure service use, as emphasized by Hansen et al. (2000), Bruneel et al. (2012), and Mian et al. (2016).

In the realm of business support services, OI environments empower student entrepreneurs through essential training and coaching sessions to develop dynamic capabilities for effective OI practices. These capabilities involve sensing (exploring the internal and external UBI environment), seizing (identifying business and OI opportunities), and transforming opportunities into tangible products and services (Teece, 2020; Bogers et al., 2019; Hutton, 2021). Developing these capabilities through business support services positively influences innovation and competitiveness (Teece, 2020). Thus, student entrepreneurs should optimize their use of these business support services to engage in OI practices and enhance performance. OI contexts also compel UBIs to accumulate invaluable repositories of specific information and knowledge, providing insights into products offered by external collaborating firms and details about their resources and capabilities (Zhang et al., 2016). This resource utilization acts as a competitive catalyst, offering industry-centric insights (Rubin et al., 2015) and leads to innovation performance enhancement (Lin-Lian et al., 2021). UBIs further augment these knowledge repositories by organizing mentorship programs, connecting startups with experienced entrepreneurs and industry veterans, enriching their ecosystem, and boosting their competitive position (Hackett & Dilts, 2004; Engelman et al., 2015). Therefore, the impact of OI practices on UISs performance depends on the extent to which student entrepreneurs utilize the business support services provided by UBIs.

In the OI context, UBIs play a central role in broadening UISs’ search horizons, connecting them with a diverse array of companies, organizations, and industries, and nurturing extensive socio-economic networks through networking services (Lin-Lian et al., 2021; Mair et al., 2012). Networking services, facilitated by UBIs, enhance the likelihood of engaging in OI practices (Hansen et al., 2000; Hughes et al., 2007). UBIs support both inbound and outbound OI for UISs by establishing internal and external networks (Bergek & Norrman, 2008). These networks are pivotal in the internationalization of UISs, granting access to crucial resources, knowledge, and opportunities (Kuryan et al., 2018). Furthermore, these networks equip UISs with the agility to adapt to market changes, identify new opportunities, and enhance their competitive positioning (Sedita et al., 2019; Hackett & Dilts, 2004). The use of networking services is crucial for knowledge exchange and transfer, particularly for knowledge-scarce UISs from emerging economies. It aids in developing their absorptive capacity and participation in international value chains, ultimately enhancing innovation performance (Gao et al., 2021; Hansen et al., 2000). Therefore, student entrepreneurs assessing the positive impact of their OI involvement on UISs performance should utilize the networking services offered by UBIs.

Taking all the above points into account, we can deduce that the impact of the use of OI practices can positively influence the performance of UISs through the use of UBI services by student entrepreneurs:

H2: The use of UBI services by student entrepreneurs mediates the relationship between the use of OI practices and the performance of UISs.

In conclusion, our theoretical examination underscores the significance of OI practices and UBI services for the prosperity of UISs. Specifically, we illuminate the critical role of OI practices in bridging the gap between the motives behind OI adoption and startup performance (see Figure 1). Moreover, the services provided by UBIs emerge as critical in mediating the relationship between the adoption of OI practices and the performance of UISs (see Figure 1).

Figure 1

A conceptual model illustrating the dual mediation role of OI practices and UBI services in influencing UISs performance

A conceptual model illustrating the dual mediation role of OI practices and UBI services in influencing UISs performance

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Methods

Latvian UBI context and OI practices

To test our research model, we conducted a quantitative survey across five UBIs that were actively operating in Latvia and were affiliated with the following universities: RISEBA University of Applied Sciences (RISEBA), TURïBA University (TURIBA), University of Latvia (LU), Riga Technical University (RTU), and the Banku Augstskola School of Business and Finance (BA). Although Latvia’s education system mainly focuses on preparing individuals to become skilled professionals in specific areas rather than entrepreneurs, notable initiatives are underway in the country to establish and implement UBIs (Bikse et al., 2016). These five UBIs offer various programs and courses designed to encourage entrepreneurship among students: TURĪBA UBI’s Practical Entrepreneurship Learning Course; LU’s Student Entrepreneurial Spirit program; RTU’s Entrepreneurship Economics course, which adopts an opportunity-oriented, problem-based learning model; and RISEBA’s entrepreneurship study program in audio-visual media arts and architecture, developed in collaboration with creative industry professionals.

Regarding OI and its importance in the university context, DEMOLA represents a prominent platform and serves as a collaboration model for UISs to develop new products and services (Slaidins et al., 2015). Teams of students address the challenges posed by companies and other organizations.

Sample and data collection

The study sample comprises startups incubated at Latvian UBIs across three incubation sessions from 2013 to 2016, with each session corresponding to an academic year. Therefore, the sample drawn for this research pertains to startups from UBIs that have completed their incubation periods, with 121 startups incubated during this timeframe. We reached out to the five active UBIs in Latvia—RISEBA, TURĪBA, LU, RTU, and BA—as they have offered a representative cross-section of the population of incubated startups. The survey was launched in June 2016 using the Webropol survey platform in Latvia. The questionnaire was initially pretested with 20 UBI startups participating in the research project. We reviewed, retested, and read the questions aloud to managers and academics to enhance clarity and avoid any potentially ambiguous phrasing. Ultimately, 24 responses from the total pool of 121 were eliminated due to non-response or incomplete answers to some questions, resulting in a final sample of 97 student entrepreneurs.

The University of Latvia (LU) contributed the most respondents (40), while TURĪBA supplied the fewest (2). This discrepancy can likely be attributed to the fact that TURĪBA transitioned from an incubation model to a coworking space concept, rendering the student entrepreneurs from the 2013–2016 incubation sessions unreachable. Table 1 presents the distribution of startup respondents by university.

The survey was conducted anonymously, although the participants had the option of providing contact information if they wished to receive a personalized assessment of the progress report. Nearly 75% of the participants shared their private contact details, indicating an interest in learning more about their performance. The data collected through the questionnaire generally adhered to participant data protection standards.

To ensure sample homogeneity, we conducted a descriptive analysis based on the sociodemographic characteristics of 97 student entrepreneurs. The results indicated a well-balanced gender distribution, with 57.73% (N = 56) male and 42.27% (N = 41) female participants. Age, a significant factor influencing the use of business incubator services (Mian et al., 2016), was also analyzed. The majority of student entrepreneurs in our sample fell into the age range of 22–30 years (73.2%, N = 71), with the remaining startups created by students over 31 years of age (10.31%, N =10) and those under 21 years of age (16.49%, N= 16).

Table 1

The distribution of respondents per UBI

The distribution of respondents per UBI

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Regarding the respondents’ educational level, 37.12% of the student entrepreneurs possessed only a bachelor’s degree (N = 36) during the incubation period, whereas the remainder had a higher education level (N = 61). The descriptive analysis further indicates that 54.63% (N = 53) of the respondents specialize in technical fields (engineering, computer science, audio and visual media, and design), while 45.36% (N = 44) have non-technical and managerial specializations (economics, law, business administration, and entrepreneurship).

Research methods

Developing the measurement model

Our research incorporates both dependent and independent variables to form these constructs. Each construct is multifaceted and comprises a minimum of two dimensions. The dependent variable, “performance of UISs”, was evaluated through two dimensions using the scale of Hughes et al. (2007). These dimensions encompassed a total of 17 items, with 11 items assessing “innovativeness”, which included radical, incremental, and organizational innovation. The remaining 6 items evaluated “competitiveness”, focusing on competitive ability and competitive performance.

The independent variables, “Use of OI practices”, which also function as a mediator, was represented by “inbound practices” (seven items) and “outbound practices” (five items), as suggested by Usman and Vanhaverbeke (2017); “OI motives” was measured through two dimensions “pecuniary motives” (four items) and “non-pecuniary motives” (four items), in line with Dahlander and Gann (2010).

Lastly, the “Use of UBI services” acts as a mediator variable and is linked to three dimensions of services (Bruneel et al., 2012; Bergek & Norman, 2008): “business support services” (eight items), “infrastructure services” (four items), and “networking services” (fourteen items). To accurately capture the variance across different respondents (UBI start-ups), we used seven-point Likert-type measurement scales (from 1 = strongly disagree to 7 = strongly agree).

Data analysis method

The partial least squares structural equation modeling (PLS-SEM) method was used for data analysis. This method was chosen for two reasons. First, the PLS-SEM method is associated with complex models that rely on small sample sizes (Hair et al., 2020; Latifi et al., 2021). Given our sample size of 97 observations, the advantage of this method is its ability to account for factor indeterminacy and avoid invalid solutions (Chin et al., 2020).

Second, PLS-SEM is commonly used for reflective-formative multidimensional constructs. This technique effectively handles models that deviate from multivariate normal distributions (Venaik et al., 2005). Our conceptual model integrates first-order reflective measures (identified by the relationship between items and appropriate dimensions) and second-order formative measures (emphasized by the connections between principal constructs and their dimensions).

Data quality analysis

Appendix 1 includes the factor loadings of the model. According to Hair et al. (2020), the correlation strength between items and their corresponding dimensions should exceed 0.6. Our data confirm this standard, with all items associated with various dimensions displaying loadings that exceed this threshold.

However, we had to eliminate three items related to the dimensions of “networking services” (two items) and “business support services” (one item) to enhance the internal consistency of our main constructs, namely OI motives, performance of UISs, use of OI practices, and use of UBI services.

The uni-dimensionality of the reflective measures was confirmed for the different constructs using Student’s t-test, which yielded a significance level of 1% for all items in the conceptual model. Furthermore, we assessed the reliability (Cronbach’s alpha), internal consistency (composite reliability), and convergent validity of the constructs using the average variance extracted (AVE). As presented in Appendix 1, the performance of UISs, OI motives, use of OI practices, and use of UBI services had Cronbach’s alphas (composite reliability) of 0.851 (0.889), 0.915 (0.931), 0.838 (0.871), and 0.916 (0.939), respectively. This result is consistent with that of Hair et al. (2020). Subsequently, we tested the convergent validity of the constructs through the AVE, which measures the average amount of variance a construct captures from its indicators relative to the measurement error (Latifi et al., 2021). As indicated in Appendix 1, the AVE ranged from 0.704 for motivation to 0.931 for incubator services, suggesting satisfactory convergent validity. To test multicollinearity, we employed the variance inflation factor (VIF) for the first-order model. The VIF values were well below the threshold value of 0.5, indicating no collinearity issues (Hair et al., 2020) (see Appendix 1).

Table 2 confirms the divergent validity of the models. The Fornell and Lacker criterion shows that the square roots of the AVE for all constructs are greater than the correlations between the constructs. Additionally, based on the HTMT criterion, all HTMT indicators were below the threshold of 0.85.

Results

To test the two mediating hypotheses, we used SmartPLS v.3.2.3 software. Following Hair et al. (2020), we employed 5,000 bootstrap samples and 97 bootstrap cases and applied the “no-sign changes” option. Table 3 presents the analysis results obtained in the three steps. First, we present analyses of the Path model, followed by that of the mediating variables. Finally, a third level of in-depth analysis is presented to examine the mediation of the components. The standardized root mean squared residual (SRMR) value, which assesses the model fit when PLS-SEM is used, was between 0.068 and 0.079, indicating a good fit for all tested models.

Table 2

Divergent Validity Analysis

Divergent Validity Analysis

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To produce generalizable results, the validity of the model was measured using two indicators: the explanatory power of the model R² and the predictive power Q² (Hair et al., 2020). In our analysis, all tested models have acceptable explanatory power (R²) ranging from 29.7% to 32.8% for the dependent variable “performance of UISs” and from 14.6% to 17.5% and 9.6% to 17.1% for the mediating variables “Use of OI practices” and “Use of UBI services”, respectively. For predictive power, the Q² tests had a value greater than zero, indicating that the exogenous constructs had predictive relevance for the endogenous construct considered concerning the other data. Moreover, the predictive power of the dependent variable “performance of UISs” was higher than that of the mediating variables.

Path and global mediating analysis

To assess the mediating effect statistically, a concise three-step approach using the PLS-SEM method is commonly applied (Zhao et al., 2010; Carrión et al., 2017; Svensson et al., 2018; Hair et al., 2020). This approach encompasses the examination of direct and indirect effects between variables as well as the confirmation of the significance of the mediating effect via a bootstrapping analysis.

The results indicate that the use of OI practices by student entrepreneurs has a positive indirect effect on the performance of UISs (β = 0.336, t = 3.406, p = 0.001). Moreover, the model supports the significance of the direct effect between OI motives and the performance of UISs (β = 0.194, t = 2.156, p = 0.031). The analysis further confirms that the indirect effect between the OI motives and the use of OI practices by student entrepreneurs is positive and significant (β = 0.413, t = 4.435, p = 0.000).

Besides the significant positive and direct effect between the use of OI practices and the performance of UISs (see the paragraph above), our findings reveal that the use of UBI services by student entrepreneurs has a significant positive and indirect effect on the performance of UISs (β = 0.207, t = 2,392, p = 0.017). Furthermore, the indirect path between the use of OI practices and UBI services by student entrepreneurs was significant (β = 0.418, t = 3.696, p = 0.000).

In our bootstrapping analysis, which consists of a simulation of n = 5.000 resamples, we find strong evidence supporting the mediating role of student entrepreneurs’ OI practices in the association between OI motives and the performance of UISs. We confirm H1 based on the observed indirect effect (β = 0.174, t = 3.080, p = 0.002), with intervals indicating a bias of 0.002, a lower bound of 2.5% = 0.071, and an upper bound of 97.5% = 0.293. Additionally, the use of UBI services by student entrepreneurs serves as a mediator in the relationship between the use of OI practices and the performance of UISs, thus supporting our hypotheses (see Table 3). The model supports H2 (β = 0.087, t = 1,993, p = 0.046) with the following intervals bias = 0.002; 2.5% = 0.011; 97.5% = 0.183.

To assess the strength of the mediation effects, we calculated the variance accounted for (VAF) (Hair et al., 2020). Our findings reveal a partial mediation of the use of OI practices (VAF = 44,37%) and UBI services (VAF = 49.86%).

Components mediation analysis

To separately test the different components associated with the mediating variables, namely, the use of OI practices and UBI services, we employed a second-order model (higher-order model) resembling a regression analysis. This second-order formative model utilizes multiple OLS regressions to estimate the partial regression relationships, as outlined by Hair et al. (2020).

The results in Table 3 provide insights into the mediation analysis based on the components. Regarding the use of UBI services, networking services (β = 0.107, t = 1.821, p = 0.069) were identified as the sole mediators of the relationship between OI practices and the performance of UISs. Additionally, the findings indicate that both outbound practices (β = 0.152, t = 2.684, p = 0.007) and inbound practices (β = 0.141, t = 2.951, p = 0.003) mediate the relationship between OI motives and the performance of UISs.

Table 3

Summary of results

Summary of results

Table 3 (suite)

Summary of results

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Discussion

Our results confirm the validity of H1, while H2 is only partially validated, with ‘networking services’ demonstrating a mediating effect on the relationship between OI practices and UISs performance. The direct and indirect effects complement our understanding of the role of adopting OI practices and UBI services by student entrepreneurs in increasing the performance of UISs. The adoption of OI practices positively influences startup performance, showing that this relationship is sustained not only for SMEs (Bigliardi et al., 2021; Vanhaverbeke et al., 2018) but also for UISs. In addition, adopting OI practices increases the use of UBI services (Hughes et al., 2007; Yun et al., 2019). This finding aligns with dynamic theories (i.e., dynamic capabilities, absorptive capacity, and desorptive capacity), which elucidate how startups strategically use, manage, and leverage resources and knowledge internally and externally, thereby overcoming learning barriers (Capron & Mitchell, 2000; Karim & Mitchell, 2000) and enhancing revenue generation through the externalization of their know-how (Lichtenthaler &Lichtenthaler, 2009). Our study further confirms that OI motives have a positive influence on the adoption of OI practices and have beneficial effects on performance (Dahlander & Gann, 2010). Furthermore, the positive influence of the use of UBI services on the performance of UISs was revealed, as estimated in the previous literature (Bruneel et al., 2012; Bergek & Norman, 2008).

We further explain the dual mediating effect of the use of UBI services and OI practices to better contribute to the understanding of the performance of UISs and meet the objectives of this study. Our results demonstrate that the exploitation of infrastructure services does not mediate the relationship between OI practices and the performance of UISs. The inconsequential finding concerning infrastructure exploitation aligns well with the recent dynamic incubation theory (Hughes et al., 2007; Mian et al., 2016). UBIs offer student entrepreneurs numerous opportunities for success. They provide access to a diverse array of facilities that not only augment their growth and development (Bøllingtoft & Ulhøi, 2005) but also accelerate their progression towards rapid internationalization (Sepulveda & Gabrielsson, 2013). Nevertheless, the key to their success within the incubator lies not only in the exploitation of these facilities but also in their timely usage (Hughes et al., 2007). Similar to innovative ideas, the services provided by incubators continuously develop and co-evolve in tandem with the incubator-incubatee relationship. This time-to-time transformation culminates in the gradual formation of a facilitative mechanism. As a result, some existing and innovative services are maintained, while others become obsolete (Eveleens et al., 2017).

Additionally, the lack of a significant mediating effect of infrastructure use is consistent with previous theory, as OI principles emphasize the importance of not only being open but also actively engaging with a diverse range of partners, both within and outside the boundaries of the incubator (Gassmann & Enkel, 2004; Yun et al., 2019). These OI principles are better embodied in the configuration of open and coworking spaces, which offer multiple avenues for startups to access resources, engage with customers, and promote innovation (Tremblay & Scaillerez, 2020). The proliferation and popularity of open coworking spaces have attracted entrepreneurs, fostering their creativity, initiative, and sense of community (Howell, 2022). These spaces intentionally promote interactions, enabling the exchange of skills, networking, and shared expertise (Fabbri, 2016; Tremblay & Scaillerez, 2020). Despite the benefits of coworking spaces for OI, the use of incubator infrastructure in our case does not seem to be a critical factor in adopting OI or the success of startups nurtured within the UBI. From an empirical point of view, as far as our results are concerned, these infrastructure services are not vital for Latvian student entrepreneurs, as not all UBIs in Latvia were equipped with appropriate coworking spaces that were important for OI at the time of the survey because of the difficulties encountered by UBIs in financing these facilities (Matt & Schaeffer, 2018). Therefore, the use of incubator infrastructure services may not be a critical factor in the relationship between OI adoption and the performance of UISs.

Contrary to expectations, our study further reveals that business support services do not mediate the relationship between OI practices and the performance of UISs, despite their recognized role in facilitating learning among student entrepreneurs within the incubator (Bruneel et al., 2012). This result can be explained from different perspectives. First, the effectiveness of mentorship and coaching in UBIs is contingent upon the varying levels and mechanisms of support provided, which range from “strong intervention’ to ‘laissez-faire’ approaches (Bergek & Norrman, 2008). Inadequately targeted support mechanisms can lead to the development of an “entrepreneurial academic” pattern in which student entrepreneurs prioritize their research interests over rapid business establishment and growth (Meyer, 2003). Moreover, if students only engage in a passive learning process without using the learning content in practice, this will not promote the relationship between adopting OI practices and the performance of UISs considerably. Previous theories have demonstrated that passive learning influences performance less than active learning through practice (Murillo-Zamorano et al., 2021). Furthermore, the younger generation highly values learning by doing (Ang et al., 2021), implying that UBIs should move beyond traditional approaches and embrace technology-enabled incubation models that focus on experiential learning, thereby fostering an entrepreneurial mindset and promoting campus entrepreneurship (Chan et al., 2022). In light of this, UBIs are encouraged to prioritize the enhancement of innovation capabilities among their incubated startups (Autio et al., 2018) and establish effective mechanisms for promoting collaboration, learning, and the exchange of ideas, both domestically and internationally (Gao et al., 2021). Finally, another explanation for this finding is rooted in the contextual factors of Latvia as a former communist country undergoing a transitional economy. During the communist era, education was centrally controlled by the state, and individuals had limited autonomy in choosing their educational paths as it was primarily driven by the state’s interests (Dimitrova, 2014). Presently, the younger generation of students has greater ownership over their education and tends to prioritize learning and counseling services that are personally relevant and beneficial.

Networking services emerged as the only mediator in the relationship between the use of OI practices and the performance of UISs. It can be inferred that students are inclined to seek valuable information and resources through UBI incubator partners rather than relying on business support services. Networking services are integral to the OI process as they require collaboration with different partners to improve performance (Usman & Vanhaverbeke, 2017; Nijssen & Van der Borgh., 2017; Hughes et al., 2007). This finding is consistent with network theory, which emphasizes that firms are deeply embedded within social networks and that economic behavior is influenced by the relationships (Nohria & Eccles, 1992) among actors from both internal and external networks (Hughes et al., 2007; Eveleens et al., 2017). Managing these relationships can be facilitated through various mechanisms such as trust, contracts, trust-based contracts, and contract-based trust mechanisms (Han et al., 2022). Moreover, the better use of these networking services can also be understood by a certain contextual openness of the younger Latvian generation that is more tempted to seek opportunities for collaboration (McPhillips & Licznerska, 2021) compared to the older generation, especially those who lived under communism. During the communist era, individuals and their will were not considered and were censored by the system, which determined individualistic behaviors, avoidance strategies, and a closed mentality (Dimitrova, 2014).

Our results also indicated that inbound practices have a stronger mediating effect on the relationship between OI motives and the performance of UISs; student entrepreneurs seem motivated to collaborate with external partners to boost their internal innovation. This corroborates previous literature that has shown market-related motives, such as improving product quality, adapting to market changes, and meeting customer demands, as the primary drivers for engaging in OI, with the ultimate goal being increased growth, improved financial performance, or greater market share (van de Vrande et al., 2009; Pustovrh et al., 2020). Adopting inbound practices also empowers student entrepreneurs who face challenges due to their small size and novelty (Usman & Vanhaverbeke, 2017). By forming partnerships with other organizations, collaborating with research institutions, and accessing external knowledge, these entrepreneurs gain access to valuable resources and expertise that can accelerate their innovation processes (Usman & Vanhaverbeke, 2017; Grilli & Marzano, 2023). This can also be explained by the younger generation’s ability to connect through social networks for personal and professional purposes (Gentina et al., 2021). Many students reported that social networks could be a source of problem-solving behavior (Albert et al., 2021).

Furthermore, we found that the relationship between the motivation to engage in OI and the performance of UISs was mediated less by outbound practices. This result can be attributed to several factors, including limited resources for scaling up production, the early-stage nature of many UISs, and a focus on building core competencies and establishing a market presence before pursuing outbound strategies (Usman & Vanhaverbeke, 2017). In Latvia, many universities emphasize fostering fundamental research and often lack a practical focus on commercializing this research (Lavrinenko et al., 2016). The limited prior experience of student entrepreneurs in commercializing business ideas, forming teams, securing financing, and establishing partnerships, compared to that of startups led by founders with previous entrepreneurial experience, has been identified as a negative factor that impacts performance (Prohorovs et al., 2019). The lack of collaboration between universities and companies further contributes to the absence of specialization in innovation competencies (Lavrinenko et al., 2016). These factors collectively explain the weaker relationships observed in outbound practices.

Implications and Conclusion

Our results contribute to both OI and incubation literature. For the OI domain, compared to previous literature that focuses on large groups and the organizational level of analysis (Vanhaverbeke et al., 2018; Bigliardi et al., 2021), we examined the context of UISs and the individual level of analysis by considering student entrepreneurs and the impact of their OI practices on the performance of UISs. Our results demonstrated the impact of the use of OI practices on performance and their role as mediators in the relationship between OI motives and the performance of UISs. The OI motives of student entrepreneurs positively influence the performance of UISs, but this relationship depends on the use of OI practices. In particular, our results highlighted a better mediator for inbound practices, which seems to be more important for student entrepreneurs than for outbound practices. We explain our findings by highlighting the significance of inbound practices, such as leveraging external knowledge, collaborations, and funding, in supporting startups and overcoming constraints (Usman & Vanhaverbeke, 2017; Grilli & Marzano, 2023). Our analysis also emphasizes the role of UBIs in facilitating these practices through knowledge transfer, local and international partnerships, and resource access (McAdam & McAdam, 2008; Colombo & Piva, 2012). However, due to limited resources and a primary focus on commercialization competencies, outbound practices such as commercialization and licensing receive lower priority in UBIs (Usman & Vanhaverbeke, 2017).

Our results also contribute to incubation theory, specifically complementing dynamic incubation theory (Hughes et al., 2007; Mian et al., 2016) and resourcing theory (Keating et al., 2014), by demonstrating that the relationship between the use of OI practices and the performance of UISs is influenced by the way student entrepreneurs use UBI services, especially those related to networking services. These networking services become essential in OI; thus, their use becomes important during OI involvement. Infrastructure services do not appear to play a critical role in OI, similar to business support services, which may not be crucial for OI and the performance of UISs. Student entrepreneurs prefer to use incubator networks instead of incubator business support services to find the resources and knowledge they need. This indicates the dynamic behavior of student entrepreneurs who analyze the content of services and decide to use network services more than other UBI services to influence their performance.

Our study also provides an empirical contribution, as we considered Latvia, a country with medium-level innovation (Slaidins et al., 2015), contrary to previous literature that focused on developed contexts with good innovation performance (Wanhaverbeke et al., 2018).

Our findings have significant implications for various stakeholders, including UBI managers, policymakers, universities, and student entrepreneurs (Hughes et al., 2007; Mian et al., 2016; Nijssen & Van der Borgh, 2017).

UBI managers play a crucial role in promoting OI practices and improving the performance of UISs. They should prioritize networking services to facilitate knowledge exchange and transfer both internally and externally (Sohail et al., 2023; Bergek & Norrman, 2008). The effective communication and dissemination of the positive impact of OI practices by UBI managers are essential for increasing stakeholder interest and encouraging local and international contributions of knowledge and resources (Bruneel et al., 2012). Moreover, UBI managers should review and develop digital collaboration initiatives and infrastructure to facilitate networking and maintain connections (Elia et al., 2022). Coworking spaces should be utilized instead of traditional office spaces as they stimulate creativity, foster initiative, and create a sense of community among entrepreneurs (Capdevila, 2015; Howell, 2022).

Policymakers should focus on supporting interventions that foster collaborative and OI-friendly startup ecosystems. They should recognize the innovation capabilities of UISs and student entrepreneurs and encourage partnerships and the adoption of OI practices by existing companies (Elia et al., 2022). Moreover, embracing coworking spaces to foster OI can contribute to the growth and success of entrepreneurial ventures, promote knowledge transfer, and stimulate economic development in the region (Garrett et al., 2017).

Universities hosting UBIs should reorganize their services to provide onsite and online networking and comprehensive business support (Hughes et al., 2007; Mian et al., 2016; Nijssen & Van der Borgh, 2017). The mentoring and expert advice offered by UBIs is crucial in bridging the gap between theoretical knowledge and practical application for student entrepreneurs (Wonglimpiyarat, 2016). UBIs also provide valuable connections with local and international academic institutions, research centers, and faculty members, granting student entrepreneurs access to specialized knowledge and industry expertise (Fernández Fernández et al., 2015).

Student entrepreneurs are encouraged to express their needs to UBI managers, be proactive in networking, and seek appropriate training to develop competencies in both inbound and outbound OI practices (Mian et al., 2016; Lavrinenko et al., 2016; Gassmann & Enkel, 2004; Yun et al., 2019). UBIs act as relationship enablers and knowledge diffusers, facilitating connections among potential partners and providing startups with resources, capabilities, knowledge, learning, and social capital (Sedita et al., 2019).

This study had some limitations. First, we considered only a small number of variables due to the limited sample size, which may have reduced our comprehensive explanation for the performance of UISs of student entrepreneurs. In addition, we considered Latvia, a former communist country and a transition economy context, which certainly influenced the results. To ensure the generalizability of our model, we invite further research to test it in other contexts. We also propose that future research should consider the incubator governance team to better understand the impact of the governance of OI intermediaries on the use and performance of OI. Moreover, longitudinal qualitative studies could provide an understanding of the multiple interactions among the variables that characterize the complex dynamics of startups in an OI context (Spender et al., 2017).

Latvian UBIs have changed since the survey. New business models have emerged in incubators, such as Design Factory and Open Lab. Furthermore, new UBIs were established at the EKA University of Applied Sciences. Grants supporting student innovation have facilitated such developments. Further research is required to explore these interventions and trends in Latvian UBIs.

While acknowledging these limitations, we believe our study is insightful for student entrepreneurs. The results illuminate the essence OI practices corroborated using incubator services. Additionally, UBI managers can find motivation in our findings to support the implementation of OI and further develop UBI services, thereby impacting the performance of UISs.