University incubators performance through the lens of institutional theory

In the technological era, economic and social growth of a country is largely dependent on its ability to digitize and innovate in the economy. To support this digitization and innovate, incubation centers are established with the sole purpose to plan, create and support innovation in startups (Nair and Blomquist, 2019).

Incubation centers are equipped with the necessary infrastructure and have at their disposal human, financial and other resources to support innovation and entrepreneurship (Nair and Blomquist, 2019).

University incubation centers (UICs) have emerged as critical components of the entrepreneurial ecosystem in India. These centers, typically established within or affiliated colleges of the universities (Abhyankar, 2014; Jain and Nanoti, 2018).

They provide a nurturing environment for early-stage startups by offering essential resources such as funding, mentorship, networking opportunities and office space (Van Weele et al., 2017). The primary objective of UICs is to bridge the gap between academia and industry, facilitating the commercialization of academic research and fostering innovation-driven enterprises (Torun et al., 2018).

Institutional theory provides a valuable lens through which the development and performance of UICs can be examined (Dimaggio and Powell, 1983). This theory emphasizes the role of formal and informal institutions − such as regulatory frameworks, cultural norms and societal expectations − in shaping organizational behavior and outcomes (Hackett and Dilts, 2004).

In the context of UICs, institutional theory helps in understanding how these entities align with broader institutional environments, gain legitimacy and secure critical resources necessary for their operations (Sharma and Kumar, 2016; Desai and Gupta, 2014).

In India, the proliferation of UICs has been significantly influenced by supportive government policies, increasing academic focus on entrepreneurship and growing industry−academia collaborations. Government initiatives such as the Startup India campaign and funding schemes from the Department of Science and Technology (DST) and the Ministry of Human Resource Development (MHRD) have played a pivotal role in establishing and sustaining these centers (Kashyap and Shukla, 2023).

In addition, universities have increasingly recognized the importance of fostering entrepreneurial activities as a means to enhance their societal impact and financial sustainability (Krishnan and Jain, 2019).

Despite their potential, UICs in India face several challenges. These include bureaucratic hurdles, funding constraints, limited industry linkages and varying levels of institutional support (Basu and Dutta, 2017; Goyal and Kapoor, 2020).

Understanding these challenges through the framework of institutional theory provides insights into the necessary conditions for the success and sustainability of these centers.

Hence, the application of institutional theory to the study of incubators provides a rich understanding of how these entities navigate complex institutional landscapes.

By considering factors such as legitimacy, regulatory support and cultural alignment, researchers and practitioners can better comprehend and enhance the performance of university incubators.

Hence, the research provides directions for growth and development by understanding the barriers of UICs. Furthermore, the research paper is divided into following components literature review, conceptual framework, research design, analysis, discussion and future research direction in the domain of university incubation for entrepreneurial growth and development in the country.

UICs in India play a crucial role in fostering innovation and entrepreneurship by providing support to nascent startups. These centers offer resources such as funding, mentorship, networking opportunities and office space, which are essential for early-stage ventures. The literature on UICs in India highlights their impact, challenges and the institutional frameworks that support their operations.

UICs act as bridges between academia and industry, facilitating the commercialization of research and innovation. Sharma and Kumar (2016) emphasize that these incubators help in translating academic research into viable business models by providing necessary resources and support systems. Krishnan and Jain (2019) further elaborate on how technology business incubators (TBIs) within universities contribute significantly to the innovation ecosystem by nurturing tech-based startups.

The effectiveness of UICs is heavily influenced by institutional support and frameworks. Desai and Gupta (2014) highlight the role of government policies and university affiliations in providing legitimacy and resources to incubation centers. Similarly, Gupta and Sahay (2018) discuss how institutional factors such as regulatory support, industry norms and cultural beliefs play a critical role in shaping the performance of these centers.

Despite their potential, UICs face several challenges. Basu and Dutta (2017) identify issues such as bureaucratic hurdles, lack of funding and limited industry linkages as significant barriers to the effectiveness of university incubators. Goyal and Kapoor (2020) compare government and private incubators, noting that university-affiliated incubators often struggle with bureaucratic inefficiencies and a lack of flexibility compared to their private counterparts.

Empirical studies and case analyses provide insights into the functioning of UICs in India. For instance, Sharma and Mishra (2022) examine multiple case studies to understand how institutional factors drive innovation in university incubators. Chandra and Saxena (2021) explore the evolution of incubation models in Indian universities, highlighting the dynamic interplay between institutional environments and incubation practices.

Effective strategies for UICs include strong industry linkages, active alumni networks and robust mentorship programs. Jain and Singh (2019) discuss strategic positioning of university incubators, suggesting that partnerships with industry leaders and active engagement with alumni can enhance the effectiveness of incubation programs. Babu and Banerjee (2018) also emphasize the importance of embedding incubation centers within entrepreneurial ecosystems to leverage institutional support.

Policy recommendations often focus on enhancing institutional support, streamlining regulatory processes and fostering a culture of innovation within universities. Mishra and Singh (2018) suggest that government policies should aim to reduce bureaucratic red tape and provide direct funding to UICs to boost their performance. Mehta and Kumar (2021) advocate for aligning university incubator objectives with sustainable development goals (SDGs) to ensure long-term impact and relevance.

The above literature review highlights the ecosystem of an incubation center in India. However, there is a gap in literature as research needs more information with regard to the role of higher education in promoting innovation among the students and other stakeholders. To fill these gaps in existing literature, this research uses the institutional theory to study the effectiveness of the incubation centers for promoting innovation and entrepreneurship in higher education.

Institutional theory provides a framework for understanding UICs, focusing on how rules, norms and beliefs shape organizational behavior. It explains how these centers align with their environments, gain legitimacy and secure resources. Institutional theory posits that organizations are embedded in formal and informal institutions influencing their structure and behavior (DiMaggio and Powell, 1983). For university incubators, success depends on aligning with academic and entrepreneurial norms. Sharma and Kumar (2016) highlight this alignment as crucial for legitimacy and effectiveness in India.

Government policies and regulatory frameworks, such as Startup India and funding from the Department of Science and Technology (DST) and the Ministry of Human Resource Development (MHRD), play a pivotal role in supporting incubation centers in India (Gupta and Sahay, 2018). These initiatives provide financial resources and create a supportive environment for entrepreneurial activities.

Collaboration between academia and industry is essential, facilitating knowledge transfer and enhancing the commercial viability of academic research. Strong industry linkages are critical for TBIs, providing startups with market access, mentors and investment opportunities (Krishnan and Jain, 2019). Cultural support for entrepreneurship also positively impacts incubation centers, reflected in the increasing number of entrepreneurship courses offered by universities (Basu and Dutta, 2017).

Despite the supportive environment, UICs face challenges such as bureaucratic inefficiencies, funding constraints and limited industry linkages. Reducing bureaucratic red tape and enhancing direct funding can mitigate these challenges (Goyal and Kapoor, 2020; Mishra and Singh, 2018).

Empirical studies show how institutional factors drive innovation and performance in university incubators (Sharma and Mishra, 2022). Strategic positioning involves leveraging institutional support and developing networks with industry and alumni (Jain and Singh, 2019). Policy recommendations include strengthening institutional support, streamlining regulatory processes and fostering a culture of innovation within universities. Aligning objectives with SDGs and reducing bureaucratic hurdles are suggested to enhance performance and sustainability (Mehta and Kumar, 2021; Mishra and Singh, 2018).

UICs play a vital role in fostering entrepreneurship and innovation within academic environments by providing resources, mentorship and networking opportunities to startups, thereby bridging the gap between academia and industry (Sharma and Mishra, 2022). Institutional theory offers insights into how these centers operate by examining how institutions shape organizational behavior and strategies (Baraldi and Havenvid, 2016; Jones et al., 2021; Joshi et al., 2023).

The present study has chosen institutional theory as it focuses on organizational structure, norms and practices by considering external societal and regulatory forces. It also focuses on the legitimacy and conformity which is critical for understanding incubation centers operating in engineering colleges ecosystem.

Furthermore, theories which are more related to the study of incubation center are resource-based view (RBV) and social network theory. RBV focuses on the leveraging internal resources for a competitive advantage, social network theory emphasizes connections and collaborations for resource access, while institutional theory examines how external norms and regulations shape the centers’ practices and legitimacy which includes all the variables of RBV and social network theory.

In the context of engineering college ecosystem, institutional theory better explains how environmental factors shape organizational behavior in educational settings. It is more suitable for analyzing how institutional pressures drive the entrepreneurial ecosystem in these colleges.

UICs are entities within universities dedicated to nurturing startups and promoting entrepreneurial activities (Christy and Mingchang, 2021; Ravi and Janodia, 2022). Their aim is to commercialize university research, support the entrepreneurial ecosystem and enhance regional innovation (Mian, 1996; Rothaermel and Thursby, 2005).

These centers provide office space, technical resources, business mentorship and access to funding. UICs perform several critical functions: offering essential infrastructure, technology and business support (Mian, 1996). Facilitating connections between entrepreneurs, investors, industry experts and academics (Pauwels et al., 2016). Developing skills for startups (Clarysse et al., 2005). Providing financial resources, including grants, venture capital and angel investors (Rothaermel and Thursby, 2005).

Institutional theory examines how institutions − comprising rules, norms and routines − become established as authoritative guidelines for behavior (Scott, 2008). Key constructs include: (a) Coercive: pressures from other organizations and societal expectations. (b) Mimetic imitation of successful models in response to uncertainty. (c) Normative: influence of professional networks and standards.

UICs that conform to industry norms and standards achieve higher legitimacy and better performance outcomes (DiMaggio and Powell, 1983). Conforming enhances credibility, which is essential for attracting resources and support (Suchman, 1995). Empirical studies can explore how conformity to industry standards affects performance metrics such as the number of successful startups and funding secured.

UICs exhibiting mimetic isomorphism by adopting best practices from successful incubation centers attract high-quality startups (DiMaggio and Powell, 1983). Mimicking successful models reduces uncertainty and signals competence to startups and investors (Spence, 1973). Comparative studies can analyze the impact of best practices on the quality and number of startups attracted.

Balancing academic and commercial logics fosters a more innovative entrepreneurial ecosystem (Thornton and Ocasio, 1999). Integrating these logics leverages both academic knowledge and market practices, creating an environment conducive to innovation (Tushman and O'Reilly, 1996). Empirical research can examine the relationship between dual logics and innovation output, such as patents and new products developed.

Institutional entrepreneurs within UICs drive the evolution and adaptation of incubation practices by introducing innovative strategies and aligning them with institutional goals (Battilana et al., 2009). Case studies can explore the role of these entrepreneurs in developing and adapting UICs.

Integrating institutional theory with the study of UICs provides a comprehensive framework for understanding their operations within institutional environments. By examining legitimacy, isomorphism, institutional logics and institutional entrepreneurship, researchers and practitioners can gain valuable insights into factors influencing UIC effectiveness and impact.

This theoretical framework not only enhances our understanding of UICs but also offers practical implications for improving their performance and fostering innovation in the UICs. Based on the above discussion, we formulate the following hypothesis for the study.

Conforming to established norms and standards helps UICs gain legitimacy in the eyes of stakeholders such as potential entrepreneurs, investors, academic institutions and government bodies. Legitimacy refers to the perception that the UIC is a credible and competent entity capable of delivering value. High legitimacy can translate into better performance outcomes, such as increased funding, higher-quality startup applications and successful commercialization of research. DiMaggio and Powell (1983) suggest that organizations that conform to institutional norms (coercive, mimetic and normative isomorphism) are more likely to be accepted and supported within their field. According to Barney (1991), resources, including intangible assets such as legitimacy, can provide a competitive advantage:

When faced with uncertainty, organizations often model themselves after successful counterparts. By adopting best practices from established and successful UICs, new or less established UICs can reduce perceived risk and uncertainty among potential clients (startups). High-quality startups seek incubation centers that demonstrate proven success. Mimicking best practices can signal operational effectiveness and potential for high-quality support. DiMaggio and Powell (1983) describe mimetic isomorphism as a strategy for organizations to cope with uncertainty and achieve legitimacy. Spence (1973) posits that organizations send signals to reduce information asymmetry. Adopting best practices serves as a positive signal to startups about the quality of the UIC:

Institutional logics refer to the belief systems and practices that dominate within different institutions, such as academia and industry. Academic logic focuses on knowledge creation and dissemination, while commercial logic emphasizes market-oriented innovation and profitability. Balancing these logics allows UICs to leverage the strengths of both academia (e.g. cutting-edge research, intellectual rigor) and industry (e.g. market needs, commercialization skills), fostering an environment conducive to innovative ventures. Thornton and Ocasio (1999) discuss how institutional logics shape organizational actions and strategies. Tushman and O'Reilly (1996) suggest that organizations capable of balancing exploration (innovation) and exploitation (efficiency) are more successful in dynamic environments:

Institutional entrepreneurs are individuals or groups within organizations who leverage their positions to influence and change institutional norms and practices. These actors play a crucial role in the evolution of organizational fields by introducing and legitimizing new practices. The dynamic nature of entrepreneurship and innovation requires UICs to continuously adapt their practices to stay relevant. Institutional entrepreneurs drive this change by advocating for and implementing new approaches. Battilana et al. (2009) discuss how institutional entrepreneurs facilitate change within institutional fields. Appelbaum et al. (2012) outlines the role of leadership in driving organizational change and the importance of vision and advocacy:

By examining the concepts of legitimacy, isomorphism, institutional logics and institutional entrepreneurship, researchers and practitioners can gain valuable insights into the factors influencing the effectiveness and impact of UICs.

To evaluate our hypothesis, the study adopted the survey method to collect data while applying measures from previous studies. This study adopts a five-point Likert scale ranging from 1 being the lowest and 5 being the highest (1 = strongly disagree) (5 = strongly agree) (Flynn et al., 2010). The study develops questionnaire based on the recommendation of the experts in the field of incubation and startups. The experts were contacted and an email copy of the study instrument was sent to them for their suggestions. A total of 15 incubation manager and 10 startups consultants were considered for the pilot study.

The performance of UICs can be understood through various factors, each influenced by constructs derived from institutional theory. These constructs help in framing how institutional environments and support mechanisms shape the effectiveness of incubation centers. The respondents were requested to conduct a critical analysis of the scale and recommend a final scale. In Table 1, the details with regard to the key performance factors and corresponding institutional theory constructs relevant to the study are presented in the table.

Data for the study were collected from the incubation centers operating in engineering colleges in Karnataka, India. The Karnataka government had floated a scheme under its startup policy 2015 to create an ecosystem to promote innovation and entrepreneurship amongst the youth in the state.

The initial sample consisted of 350 startups derived from the database (https://startup.karnataka.gov.in/certified-companies). To improve our response rate, we contacted the incubation centers Chief Executive Officer (CEO) and Chief Operating Officer (COO) for information with regard to the effectiveness of the incubation centers. Furthermore, the data is collected from engineering colleges operating in Karnataka and funded by the scheme of Government of Karnataka named as the New Age Innovation Network (NAIN), this scheme provides comprehensive support for project development, direct industry engagement and focus on fostering entrepreneurship within engineering colleges. Unlike broader schemes such as Atal Innovation Mission (AIM) and Startup India, NAIN specifically targets engineering students, providing a tailored environment for innovation and skill-building. In addition, compared to Technical Education Quality Improvement Programme (TEQIP) and the Smart India Hackathon, NAIN’s emphasis on practical application, funding and interdisciplinary projects makes it more suited for fostering a startup culture among future engineers.

The data was collected though five-point Likert scale, as this scale provide information on the relationship between the UICs through the lens of institutional theory (Awang et al., 2016; Hong et al., 2024).

The survey questionnaire was sent through Google Forms to the CEO and COO of the incubation center. Furthermore, a Google Form was also sent to the startups operating under these colleges for their inputs.

This study adopted stratified sampling, which divides the population into distinct subgroups (strata) and randomly samples from each. This method ensures that each subgroup is well-represented, resulting in more accurate and generalizable findings. It was chosen to represent incubation centers across locations and years of establishment effectively.

The research was conducted in the month of January 2021 and by September 2023 we were able to collect 197 completed responses from the startups. The demographics of the respondents are provided in Table 2.

The study tested for nonresponse bias by comparing the responses of the first and last respondent of the study participants (Flynn et al., 2010). A t-test conducted between the first and the last respondent indicated no difference between them and the p-value was less than 0.1.

The study used the Warp partial least squares (PLS) 6.0 software to conduct path analysis (Kock, 2017). The traditional PLS are measured as a combination of indicator weights without the inclusion of measurement errors (Henseler et al., 2014). Kock (2017) opines that without looking at the errors of estimating the use of compounds instead of factors leads to other known sources of bias.

Cronbach’s alpha was applied (Cronbach, 1951) to check the reliability of the constructs. Results, shown in Table 2, indicate that the values were more than 0.6. The composition of the technique was tested by means of material analysis (exploratory factor analysis) using Varimax (Flynn et al., 2010). The study adopts nine constructs in all and all are used in the final analysis. The total loading of each element (λi) was greater than 0.5, and the coefficients of the combined reliability scale were higher than 0.7 (Flynn et al., 2010).

Results of data based on self-reported response from the respondents are prone to common method bias, hence, to account for bias (Podsakoff et al., 2003), the study conducted confirmatory factor analysis (CFA), loading all items in one item, and evaluating relevant indicators. Results from CFA analysis showed the following results (χ²/df = 8.21; root mean square error of approximation= 0.432; non-normed fit index = 0.074; comparative fit index = 0.142 and root mean square residual = 0.423) and Δχ²/df (6.54; p < 0.001). Results indicate that there was no bias in the study results.

Table 3 presents reliability and validity metrics, including standardized composite reliability (SCR) and average variance extracted (AVE), which assess the internal consistency and convergent validity of latent variables. For instance, coercive and startups performance show strong reliability and validity with high SCR and AVE values.

The results from Table 4 indicates correlation summary, the results show that coercive has negative relationship with mimetic, normative, startups performance, monitoring and assistance and resource munificence. While results with regard to mimetic factor of institutional theory has positive relationship with normative, startups performance and resource munificence. The results with regard to normative factor of institutional theory show that positive relationship between startups performance and resource munificence and negative relationship with monitoring and assistance. The overall correlation summary indicates that monitoring and assistance requires directions for developing innovation in incubation centers.

The results from the model fit and quality indices (refer to Table 5) show average path coefficient (APC) (0.211, p < 0.001), average r-squared (ARS) (0.316, p < 0.001), average variance inflation factor (AVIF) (0.233, p < 0.001) and TenehausGoF (0.432). The values show that all indices are in the acceptable range of p < 0.05 and TenehausGoF is large, if ≥0.36. Hence, the model fit summary is acceptable to the study.

The bootstrapping process was conducted on the feedback provided by the respondents to check for common errors (SEs) (Tenenhaus et al., 2005). The PLS method coefficients and p-values are displayed in Table 6. Measurement coefficients are interpreted as beta coefficients.

The results from the model fit and quality indices (refer to Table 5) show APC (0.211, p < 0.001), ARS (0.316, p < 0.001), AVIF (0.233, p < 0.001) and TenehausGoF (0.432). The values show that all indices are in the acceptable range of p < 0.05 and TenehausGoF is large, if ≥0.36. Hence, the model fit summary is acceptable to the study.

The bootstrapping process was conducted on the feedback provided by the respondents to check for common errors (SEs) (Tenenhaus et al., 2005). The PLS method coefficients and p-values are displayed in Table 4. Measurement coefficients are interpreted as beta coefficients.

The results show that H1: (mimetic → resource munificence) (β = 0.317, p = <0.001) and H2: (normative → monitoring and assistance) (β = −0.236, p = <0.001) are supportive with regard to mimetic factors to the performance of the startups. While the results also include that normative factors are positively related to resource mobilization.

The results with regard to hypothesis testing is presented in Table 6. These results from H1 indicate that startups operate in the uncertain situations yet they conform to industry norms and standards to achieve higher legitimacy and better performance in the startups ecosystem.

Furthermore, the same was indicated in the hypothesis (H2) with regard to entrepreneurs from UIC who advocated new norms of managing the startups have better chances of innovation and attracting high-quality startups.

The results have indicated negative relationship with regard to H3 (coercive → startups performance) (β = 0.130, p = 0.032) and H4 (normative → resource munificence) (β = 0.012, p = 0.434).

This indicates that H3, university startups needs to understand the norms, policy and procedures to enhance the startups performance. There UICs need to balance academic and commercial institutional logics that will foster a more innovative entrepreneurial ecosystem.

Likewise, H4 which indicated negative relationship between normative factors and resources munificence, which indicate that information flow and professionalism influence in using the resources of the startups. Therefore, institutional entrepreneurs within UICs who actively advocate for new practices and norms will drive the evolution and adaptation of incubation practices.

The moderating factor of location of incubation center and its impact on the performance has shown negative influence with regard to (location-coercive → startup performance β = −0.103, p = 0.071; location – coercive → resource munificence β = −0.116, p = 0.049; location-normative → startup performance β = −0.088, p = 0.106; location-normative → resource munificence β = –0.001, p = 0.494; location-mimetic → resource munificence β = 0.106, p = 0.066; location-mimetic → resource munificence β = 0.006, p = 0.468); this indicates that these factors do not have any impact on the location of these centers on the performance of the incubation centers.

While the results with regard to the location of the incubation centers have shown positive relationship with regard to location-coercive → resource munificence β = 0.512, p = <0.001; location-normative → resource munificence β = 0.481, p = < 0.001 and location-mimetic → startup performance β = 0.714, p = <0.001; this indicates that these factors have an impact on the performance of the incubation centers.

The results of this study provide valuable insights into the dynamics of UICs and their influence on startups performance, viewed through the lens of institutional theory. Each hypothesis sheds light on different aspects of institutional influences and their implications for UIC operations.

The positive relationship between mimetic factors and resource munificence supports the notion that startups conforming to industry norms and standards achieve higher legitimacy and better performance within the startups ecosystem. This finding aligns with DiMaggio and Powell’s (1983) concept of mimetic isomorphism, where organizations mimic successful models to reduce uncertainty and enhance their legitimacy. This is crucial for startups operating in uncertain environments, as adherence to established norms signals competence and reliability, thereby attracting resources and support.

The results indicate a significant negative relationship between normative factors and monitoring and assistance. This finding suggests that while normative factors are positively related to resource mobilization, they may negatively impact the effectiveness of monitoring and assistance provided to startups. Entrepreneurs from UICs who advocate for new norms in managing startups have better chances of innovation and attracting high-quality startups. This aligns with the idea that professionalization and adherence to norms within an industry can enhance resource mobilization but may require balancing to avoid excessive rigidity in monitoring and assistance processes.

The negative relationship between coercive isomorphism and startups performance highlights the complexity of regulatory pressures. This suggests that while understanding and adhering to norms, policies and procedures is essential, excessive coercive pressures can hinder startups performance. UICs need to balance academic and commercial institutional logics to foster a more innovative entrepreneurial ecosystem, aligning with the ambidexterity theory proposed by Tushman and O'Reilly (1996).

The nonsignificant negative relationship between normative isomorphism and resource munificence indicates that normative factors alone do not significantly influence resource munificence. This finding suggests that information flow and professionalism play a crucial role in how resources are used by startups. Institutional entrepreneurs within UICs who advocate for new practices and norms are essential for driving the evolution and adaptation of incubation practices, aligning with Battilana et al.’s (2009) concept of institutional entrepreneurship.

The findings offer several practical insights for UIC managers and policymakers.

UIC managers should prioritize adopting best practices from successful incubation centers to enhance legitimacy. This involves benchmarking against industry leaders, implementing standardized procedures and obtaining relevant certifications. Enhanced legitimacy not only attracts high-quality startups but also increases the likelihood of securing funding and institutional support.

While normative isomorphism is crucial for resource mobilization, UICs should be cautious about the potential negative impact on monitoring and assistance. Managers need to strike a balance between adhering to professional standards and maintaining flexibility in providing support to startups. This can be achieved through tailored mentorship programs and adaptive monitoring processes.

UICs must navigate coercive pressures effectively to enhance startups performance. This involves understanding regulatory requirements and aligning them with the center’s strategic goals without stifling innovation. Policymakers should consider providing guidelines that are supportive rather than restrictive to foster a conducive environment for startups.

UICs should empower institutional entrepreneurs who can drive innovation and adaptation within the center. These individuals can introduce and advocate for new practices and norms, ensuring that the UIC remains agile and responsive to changes in the entrepreneurial landscape. Providing resources, autonomy and platforms for experimentation can support these entrepreneurs in their efforts.

The moderating effect of the location of incubation centers shows mixed results. While certain factors such as coercive pressures positively impact resource munificence (β = 0.512, p < 0.001), other factors such as normative influences on startup performance show no significant impact (β = −0.088, p = 0.106). This indicates that the location of UICs can influence resource availability but may not directly correlate with startup performance. Policymakers should consider the strategic placement of UICs in regions with supportive regulatory environments and access to resources to maximize their impact.

This study provides a nuanced understanding of the role of institutional pressures − mimetic, normative and coercive − in influencing the performance and resource acquisition of startups within UICs.

Startups that adopt successful industry norms and practices are more likely to attract resources, thereby enhancing their performance in the startup ecosystem. This highlights the importance of legitimacy and strategic conformity in uncertain environments.

The professionalization and adherence to established norms significantly impact the support provided to startups. Entrepreneurs advocating for new norms within UICs can drive innovation and attract high-quality startups, although there is complexity in how these pressures are enacted. Coercive pressures related to regulatory requirements can negatively affect startup performance, indicating the need for UICs to balance compliance with flexibility and innovation. The direct relationship between normative factors and resource acquisition is not significant, suggesting that professionalism and information flow are more critical in resource utilization rather than acquisition. The impact of institutional pressures on UIC performance is influenced by the location of the incubation centers. While location alone does not determine success, it can amplify the effects of mimetic and normative practices.

These findings underscore the importance of integrating institutional factors into the strategic planning and operations of UICs. By doing so, UICs can enhance their legitimacy, foster innovation and improve their overall effectiveness.

Future research could explore the impact of institutional pressures across different geographic regions and cultural contexts. Understanding how local norms, regulations and industry practices influence UIC performance could provide more tailored insights for incubation strategies. Longitudinal studies could examine the long-term effects of institutional pressures on startup performance and resource acquisition. This would help in understanding how the impact of these pressures evolves over time and their sustained influence on UIC effectiveness.

Investigating how institutional pressures affect startups in specific sectors (e.g. technology, health care, social enterprises) could yield sector-specific strategies for UICs. Different industries may respond differently to institutional pressures, and understanding these nuances could enhance sector-specific incubation practices. The role of digital technologies in shaping institutional pressures and UIC practices is an emerging area of interest. Future research could examine how digital transformation influences mimetic, normative and coercive isomorphism within UICs and their impact on startup success. Further studies could delve into the policy implications of institutional pressures on UICs. Understanding the role of government policies and regulatory frameworks in shaping UIC operations could inform more supportive and flexible policy environments for entrepreneurship and innovation. Exploring the characteristics, roles and strategies of institutional entrepreneurs within UICs can provide deeper insights into how new norms and practices are introduced and institutionalized. This could also shed light on the mechanisms of change and adaptation within incubation ecosystems.

By addressing these areas, future research can build on the findings of this study to develop a more comprehensive and contextually nuanced understanding of the dynamics within UICs, ultimately contributing to more effective incubation practices and entrepreneurial success.