Entrepreneurial perspectives on biotechnology firms support in Sweden

The biotechnology sector has emerged as a strategic domain in the knowledge-based economy, widely recognized for its potential to drive innovation in health care, life sciences and sustainability. Biotechnology firms operate under conditions of long development timelines and high scientific and regulatory uncertainty. In Sweden, biotechnology has been actively promoted as a priority sector, supported by universities, public innovation agencies such as Vinnova, and a network of incubators and science parks. As a result, Sweden is home to a large number of biotechnology firms, many of which originate from academic research environments and aim to commercialize cutting-edge scientific discoveries. However, despite the strong institutional backing, biotechnology ventures continue to face significant developmental challenges. These include high levels of scientific uncertainty, long product development timelines, regulatory complexity and access to both technical and managerial resources (Sridhar et al., 2013).

Many studies have examined how incubators, accelerators, and related support actors help new ventures overcome early-stage development by providing resources, knowledge, and network access (Assenova, 2020; Del Sarto et al., 2020; Pauwels et al., 2016). However, much of the literature is grounded in digital and general technology sectors, where product development cycles are shorter and few regulatory hurdles. In such contexts, support mechanisms differ from those in the science-based sectors such as biotechnology, where firm development is slower, more uncertain and dependent on long-term collaboration and staged financing. Despite the growing interest in entrepreneurship support systems, there remains a critical gap in understanding how biotechnology entrepreneurs perceive and engage with incubators and other support actors as their firms evolve. Existing research has not sufficiently examined the staged nature of resource needs or the shifting value of institutional support across the early and development phases of biotechnology firm growth. While some studies acknowledged that the entrepreneurial journey is dynamic and path-dependent (Cohen, 2014; Lin and Lekhawipat, 2023), few studies have explored how support actors adapt or fail to adapt to these changing demands from the perspective of the entrepreneurs themselves (Clarysse et al., 2016; Galvão et al., 2019). Thus, the voice of entrepreneurs remains underrepresented in empirical research, particularly in the science-based sector where founders’ experiences are deeply shaped by the intersection of science and entrepreneurship.

There is also a limited understanding of how different forms of support connect to underlying resource and capability dynamics inside biotechnology ventures. Prior work has drawn on the resource-based view (RBV), dynamic capabilities and social capital theory to explain how firms leverage internal assets, adapt to uncertainty and access resources through networks. Yet these theoretical perspectives are often treated separately, and their joint relevance for explaining stage-specific processes in science-based entrepreneurship remains underexplored. In particular, we know little about how entrepreneurs orchestrate internal and external resources, develop capabilities in interaction with support organizations as ventures move from foundational development to growth.

This study addresses these gaps by examining how Swedish biotechnology entrepreneurs navigate resource challenges and interact with incubators and other support actors across different stages of firm development. We adopt an integrated theoretical lens that combines RBV, dynamic capabilities and social capital theory. In our approach, RBV highlights the importance of accessing and mobilizing critical resources. Dynamic capabilities illuminate how entrepreneurs sense opportunities, seize support and reconfigure their resource base over time. Social capital explains how ties to incubators, investors and other support actors enable or constrain these processes. Thus, we investigate two research questions:

Empirically, we draw on structured interviews with 16 biotechnology entrepreneurs operating in Sweden, 8 representing early-stage ventures and 8 representing more mature development-phase firms. Sweden was chosen as the research context due to its large number of biotechnology startups, many of which have successfully transitioned from academic research to commercial application (Innovation, 2020). Sweden’s well-established support infrastructure, including universities, public innovation agencies such as Vinnova, and a network of incubators, provides a strong basis to examine how support mechanisms align with the evolving needs of science-based entrepreneurial firms. The data were analyzed thematically to identify stage-specific patterns in perceived challenges, resource mobilization, and support experiences.

Our findings make three key contributions. First, it extends the RBV by demonstrating how biotechnology entrepreneurs orchestrate external resources, particularly through incubators, universities, and public agencies, during different stages of firm evolution. Second, it contributes to dynamic capabilities theory by highlighting how biotechnology entrepreneurs develop and reconfigure managerial and strategic capabilities in response to uncertainty and growth. Third, it deepens understanding of social capital in biotechnology entrepreneurship by demonstrating how entrepreneurs’ reliance on bonding ties to institutional support gradually shifts toward more selective, bridging relations with investors and partners.

Ultimately, this research contributes to ongoing scholarly discussions in innovation studies, entrepreneurship theory and policy design by offering empirical insights into how support systems can be better tailored to the distinct trajectories of science-based firms. It also provides practical implications for incubator managers, policymakers and funding agencies seeking to design more adaptive, phase-specific support infrastructures for knowledge-intensive entrepreneurial ventures.

Biotechnology entrepreneurship has been characterized as a science-based, capital-intensive and highly uncertain form of venturing, where firms face long development cycles, complex regulatory processes and high failure risks. Biotechnology startups often emerge from academic or clinical environments and must simultaneously manage scientific development, regulatory compliance and commercialization under conditions of limited internal resources. In this setting, incubators, accelerators and related support actors are frequently portrayed as key intermediaries that help ventures overcome early-stage liabilities by providing access to infrastructure, expertise, legitimacy and financing opportunities.

However, much of the empirical work on incubation and entrepreneurial support focuses on digital or general technology sectors, where product cycles are shorter and regulatory burdens less severe. Evidence on how biotechnology entrepreneurs themselves experience and use support mechanisms across different stages of firm development remains limited, particularly in institutional contexts such as Sweden that combine strong public support with persistent financing and capability gaps. To analytically capture how entrepreneurs mobilize and combine support over time, this study draws on three interconnected strands of literature: the RBV and resource orchestration, dynamic capabilities and social capital.

The RBV provides a foundational framework for understanding how firms achieve and sustain competitive advantage through the strategic use of internal and external resources. According to the RBV, firms gain a competitive advantage by possessing resources that are valuable, rare, inimitable and nonsubstitutable (Barney, 1991; Wernerfelt, 1984). Subsequent research has further developed this perspective by emphasizing how firms structure and leverage resources over time (Sirmon et al., 2007). Although RBV was initially developed to explain competitive advantage in established firms, later research has extended the perspective to entrepreneurship by highlighting the role of entrepreneurial cognition, opportunity recognition and resource recombination in venture creation (Alvarez and Busenitz, 2001). In entrepreneurial contexts, startups often lack the full suite of resources needed for survival and growth, such as specialized technical infrastructure, managerial talent and financial capital. As a result, entrepreneurial success depends not only on internal assets but also on the ability to identify, access and combine resources from the surrounding environment. In the case of biotechnology startups, internal resources are often insufficient or underdeveloped in the early stages. Consequently, firm survival and growth depend not only on internal capabilities but also on the ability to access, mobilize and reconfigure external resources (Ahn and York, 2011). This underscores the importance of resource orchestration, the deliberate process of structuring, bundling and leveraging resources across firm boundaries, which serves as a critical strategic activity in entrepreneurial ecosystems. Empirical literature emphasizes that external partnerships and alliances are essential channels through which resource-constrained biotechnology firms acquire critical capabilities. For example, a previous study demonstrates that collaborations with established pharmaceutical companies enable smaller biotechnology ventures to access financial capital and technological expertise, enhancing their innovation capacity (Gopalakrishnan et al., 2008). This external acquisition of resources aligns with RBV logic, which posits that even resources controlled by partners can be strategically leveraged to build a firm’s competitive advantage.

While RBV focuses on resource possession, dynamic capabilities theory addresses how firms adapt, reconfigure and renew their resources for uncertainty and rapidly changing environments (Grandclément, 2015). This theory extends the RBV, positing that the ability to integrate, build and reconfigure both internal and external firm resources is essential for maintaining a competitive advantage in the dynamic biotechnology environment (Ngigi and R., 2019; Teece, 2017). Biotechnology firms often operate in highly volatile markets, necessitating quick responses to changes, where dynamic capabilities manifest prominently (Arokodare and Asikhia, 2020; Schilke, 2014). In the biotechnology context, dynamic capabilities consist of specific abilities that allow firms not only to respond to external pressures but also to proactively shape their strategic trajectories through innovation and collaborative practices (Alegre et al., 2013; Stezano and Oliver Espinoza, 2019). For instance, Lin and Lekhawipat (2023) argued that successful innovation strategies in the biotechnology industry significantly rely on the firm’s dynamic marketing capabilities, which enhance collaboration with external partners while managing complex customer relationships. This supports the notion that external innovation is contingent upon the internal competencies developed through dynamic capabilities.

Collectively, the dynamic capabilities perspective complements the RBV by highlighting the importance of timing and adaptability in resource utilization. Within the Swedish biotechnology sector, where firms operate under conditions of technological uncertainty, fluctuating access to capital and regulatory complexity, dynamic capabilities help explain how entrepreneurs adjust and redeploy resources to sustain growth across different developmental phases.

Social capital theory provides an additional lens to understand how biotechnology entrepreneurs access resources and information through relationships (Burt, 1992; Nahapiet and Ghoshal, 1998). More recent research further highlights the importance of networks and ecosystem relationships in facilitating resource mobilization, knowledge exchange and opportunity development in entrepreneurial settings (Hoang and Antoncic, 2003). This perspective is particularly relevant in biotechnology, where firms often rely on strategic alliances and partnerships to access complementary expertise and capabilities.

During the early stages of development, biotechnology startups often face substantial barriers, including informational asymmetries and difficulties in accessing capital markets. According to Hadley et al. (2018), these frictions can hinder investment decisions, thereby leading to underinvestment in critical areas such as research and development (R&D). The development of social capital becomes essential in overcoming these challenges. For instance, Nguyen et al. (2023) illustrate that startups in their initial phases can leverage social networks to enhance their digital transformation and operational capacity, key elements that directly influence their survival and growth amidst limited resources. As startups progress to the development stage, the relevance of social capital becomes even more pronounced. The evolution from reliance on informal networks to more structured engagements, such as partnerships with incubators or venture capitalists (VCs), can optimize resource utilization and capital access. Research by Zhou suggested that incubators play a crucial role in enhancing nascent startups’ performance by providing them with a platform to build and leverage their social capital to address survival challenges (Zhou, 2023). Moreover, Mukul et al. (2022) indicated that social capital aids in marketing efforts, thereby transforming relationships into tangible business opportunities. Thus, as startups mature, the networks established during their formative stages can yield significant strategic advantages that are difficult for competitors to replicate. The financial landscape of biotechnology startups is inherently complex, often requiring tailored investment strategies to meet their unique developmental needs. Singh and Mungila Hillemane (2023) noted that while early-stage startups typically rely on business angels, they progressively seek out venture capital as they scale, highlighting a transition wherein the importance of social capital shifts alongside the startup’s lifecycle. This transition underscores the necessity of maintaining robust social ties and networks, which not only facilitate access to funding but also enhance credibility and attract potential partners (Lo and Thakor, 2022).

In science-based entrepreneurship, resources, networks and capabilities do not operate as independent drivers of venture development. Instead, they function as a coupled system that evolves over time. We integrate the RBV, dynamic capabilities and social capital by proposing a temporal mechanism. First, social capital shapes access to critical resources (Hoang and Antoncic, 2003; Nahapiet and Ghoshal, 1998). Second, these resources provide inputs for capability enactment. Third, dynamic capabilities govern how resources and networks are recombined to meet changing stage-specific demands.

In an incubator context, entrepreneurs rarely possess all strategic resources internally. Instead, they mobilize external assets such as laboratory infrastructure, credibility, managerial expertise and finance, through relationships with incubators, universities and public agencies (Nicholls-Nixon et al., 2024). These relationships therefore function as both resource conduits and legitimacy channels, converting network ties into resource availability.

Dynamic capabilities then explain how entrepreneurs convert these accessed resources into progress under uncertainty (Teece, 2012). Through sensing, entrepreneurs interpret regulatory constraints, market signals and funding opportunities. Through seizing, they mobilize resources by assembling projects, applying for grants and engaging investors. Through transforming, they reconfigure resource bundles and activity systems as technical or commercial assumptions change (Teece, 2012). In early stages, incubators can scaffold these activities by providing templates, interpretation and brokerage, before such routines become internalized.

Finally, the mechanism involves a reinforcing relationship between capability development and network evolution. In early stages, entrepreneurs rely heavily on incubator- and university-based ties that provide legitimacy, guidance and access to initial resources (Nicholls-Nixon et al., 2024). As ventures gain experience and routines become established, entrepreneurs increasingly expand beyond these initial networks and form more selective relationships with specialized partners, investors and customers (Anderson et al., 2010). Network evolution, therefore, both supports and reflects capability development. Early networks enable resource access and learning, while growing capabilities allow entrepreneurs to reconfigure their network portfolios in response to changing resource needs. This perspective highlights how support ecosystems influence venture development not only by providing resources but also by enabling entrepreneurs to build the capabilities and relationships needed to recombine resources as ventures mature (Löfsten et al., 2023).

The research reported on here aimed to study the experiences and perceptions of biotechnology entrepreneurs in relation to early stages and more mature stages of development of the firm. To explore this, a qualitative approach was chosen, which entailed methods of data collection and analysis. The material analyzed consists of the experiences of the respondents as representative of 16 companies and should be seen as an exploratory contribution to earlier research.

Structured interviews were conducted with 16 entrepreneurs, 8 of them representing very early stages of development of the startups and the other 8 companies in a market and development phase. The selection of companies was made in a two-stage process. First, a review was made of biotechnology companies that met the two chosen criteria for inclusion in the study:

1. to be listed under Statistics Sweden’s SNI code for the sector; and

2. to be a member of the organization SwedenBIO or listed as a collaborating entrepreneurial biotechnology company by one of the incubators currently operating in Sweden.

A call was sent out to all 226 companies that met these criteria with a request for participation in interviews with the researchers. The companies were also asked to self-report on a representative suitable for answering the questions. Second, the researchers decided on a sample size of 16 respondents guided by the dominant idea that sample size in qualitative studies is generally not determined by statistical power calculations but by the depth of knowledge obtained and the possibility to reach saturation in new aspects given by the respondents (Guest et al., 2006). Two criteria were set for inclusion in the study:

1. the number of selected firms should be equal in the two groups: very early stage and development phase; and

2. the selection should be guided by maximizing the diversity within the sample regarding company characteristics.

Maximum variation sampling was chosen as a purposive sampling strategy (Patton, 2015). Table 1 presents the summarized characteristics of the 16 biotechnology companies selected. Their mean characteristics are representative of the full sample of the 226 firms, but the interview answers of the 16 companies are not to be regarded as representative of the population, as the specific conditions of each company might be very different. Instead, we emphasize the exploratory intention.

As shown in Table 1, the interviewed companies were started between the year 1991 and 2020. They had between 0 and 24 full-time employees and between 0 and 4 part-time employees. The level of technology was self-rated by the firms on a scale from (1) known technology to (7) at the frontline of new technology. The firms interviewed rated their level of technology as being between 3 and 7. We also asked the respondents about the company turnover for the years 2020–2022. This varied between SEK 0 and SEK 38,000.000. Some firms had zero turnover all three years, while others started with 0 and then developed a smaller turnover.

The interviews were all based on a structured interview guide consisting of 42 questions. Eight initial questions focused on company characteristics (year of start; number of full-time and part-time employees at the time of interviewing; company turnover per year for the three following years 2020–2022; level of technology; main origin environment; and owner categories; and phase of development) and were used as the basis for inclusion of cases in the sample.

Eighteen questions focused on the respondents’ experiences in the earliest stages of development of the firm. A sample question is “Refining a project idea can include developing the idea so that it becomes clearer, can be communicated more easily, has a more attractive packaging, etc. To what extent have different actors contributed to the development during the earliest development phase in this regard in your case?”. The respondents were asked to reflect on the questions and follow-up questions were asked if needed to clarify which actors had contributed and in what way according to the respondents.

Eight questions focused on the relationship with an incubator, and a sample question is “Are there any other services that the company would have wanted/needed but were not offered?” Eight questions were asked on the financing of the start-up, and a sample question is: “Do you think that venture capital companies generally have an understanding of the need for continued biotechnology research in young companies?” The respondents were asked to reflect on each question and share their experiences.

The questions were partly inspired by an earlier study by Lindstrom and Olofsson (2001) conducted in Sweden with the aim of analyzing contributions of different support actors to the development of the early stages of companies within the tech industry. Questions on the firm’s relationship with and experiences of incubators were inspired by Scillitoe and Chakrabarti (2010) as well as Vaz et al. (2022).

The interviews were all conducted in Swedish following the 42 questions in the interview protocol to ensure consistency between interviews. The interviews lasted between 40 and 80 min and were anonymized before the analysis.

Based on the request of the respondents’ direct quotes were used for the researchers’ analysis of the material, but not used as illustrations of the themes in the presented text (result section). All interviews were transcribed in full and analyzed by both authors. The analysis of the 16 interviews was conducted jointly by the two authors and was carried out as a content analysis in a two-phased procedure. The first phase was an inductive data-driven process inspired by the thematic analysis developed by Braun and Clarke (2006). In this process, the two authors coded the material based on the utterances of the respondents. The material was read and discussed by both authors. The second phase was theoretically guided by the frameworks chosen for the study (described in Section 2) and is therefore a deductively structured coding. This second phase enabled an interpretation of the full material relating to the two different stages of the entrepreneurial company. As we are interested in entrepreneurial perspectives on biotechnology startups, the two-phased mixed inductive-deductive approach gives an opportunity to keep close contact with the empirical material while developing more theory-driven knowledge. The two different types of coding gave the possibility to explore the experiences of the entrepreneurs as well as relate them to higher-order concepts. The two types of readings of the material fitted well and this is interpreted as indicating a good fit also with earlier findings. Examples of material coded in the first phase but not taken into consideration in the deductive coding are the respondents’ own stories on emotional or relational aspects of the different phases and on activities within the firm. As this was not the focus of the main study, the material is not considered here.

As described in Sections 3.1 and 3.2, the study was guided by a qualitative approach to research and the main task was to gather information on the experiences of biotechnology entrepreneurs. The study is based on a small sample size, and we would like to comment upon the experiences made in relation to the saturation of information received. The in all 16 interviews conducted reached saturation in terms of the answers given to the questions. However, we would like to underline the exploratory intention of the study.

In qualitative research, dependability, credibility and transferability are quality criteria often used in a discussion of the empirical work of the researchers. Here, the sample is small, the aim is explorative and therefore there is a need to justify all decisions made by the researchers during the process of collecting and analyzing the material. This is done in Section 3 with the ambition of showing how the study was designed and undertaken, how the respondents were chosen and treated and what questions were asked. The format of the analysis is also described to make it possible for other researchers to replicate the study.

We should also briefly comment upon the ethical aspects of the undertaken study. All respondents were asked to sign their consent to participate and could decide to leave at any time during the interview. All 16 participants chose to stay on during the full interview.

The respondents were guaranteed anonymity for both the individual and the company name, as they represented companies well known and for some in early stages of development. Some of the material was considered sensitive by the respondents and we therefore decided not to use any direct quotes in the results section.

This section presents the findings from structured interviews with entrepreneurs of biotechnology firms at the different stages of development, which are companies in the early-stage development phase and those in the business development phase. The results are divided into two sections: early-stage development of biotechnology firms and the role of incubators in supporting biotechnology firms.

The structured interview findings reveal nuanced and evolving challenges faced by biotechnology firms in Sweden. By comparing companies in the early-stage and those in the development phase, several patterns emerge regarding types, severity and persistence of common barriers. The results are organized thematically around three core areas, including customer acquisition and market orientation, access to technical and managerial expertise and capital resources and scientific uncertainty.

Customer acquisition and market orientation challenges varied significantly between firm stages. Early-stage entrepreneurs generally did not perceive customer acquisition as the most pressing obstacle during the very first years. Several respondents reported clear unmet needs or existing scientific networks generated initial interest in their ideas. In contrast, entrepreneurs from development phase firms expressed more moderate but consistent challenges in customer acquisition. Many entrepreneurs reflected on the time and effort required to generate serious interest from customers in the beginning, even if there was a clear idea of the demands from customers. When it came to deciding which market the firms should target, early-stage companies reported significantly more difficulty and uncertainty. They thought that it was not obvious who would need their solution most. Development-phase companies exhibited greater clarity in market orientation. However, several entrepreneurs still adjusted their strategies due to real-world market feedback, regulatory challenges or evolving customer behaviors, indicating that strategic flexibility is essential throughout the firm’s growth.

Viewed through an RBV lens, the findings suggest stage-specific constraints in value creation: early-stage firms face a primary challenge in building and aligning market-oriented resources (e.g. customer definition, value proposition articulation and regulatory/commercial pathway knowledge) to make their scientific resource base deployable, whereas development-phase firms concentrate on leveraging and refining more clearly defined market positions to improve repeatable customer acquisition.

A plausible interpretation is that early-stage entrepreneurs place less emphasis on customer acquisition because of a sequencing logic, rather than a lack of market awareness. From an RBV perspective, ventures in the earliest phase focus on assembling the basic resources needed to establish viability. Scientific legitimacy, initial funding and a credible development pathway are particularly important at this stage, because without them the venture cannot yet mobilize broader market-oriented resources. Entrepreneurs, therefore, rely heavily on academic and scientific networks to build credibility and attract early support.

As ventures move into the development phase, the challenge shifts from assembling initial resources to using and reconfiguring them for commercialization. In terms of dynamic capabilities, the task becomes not only identifying opportunities but also adapting to market and institutional requirements. Evaluation criteria increasingly focus on regulatory alignment, partner readiness and customers’ willingness to engage, making customer acquisition a more visible and persistent challenge.

This transition occurs through practical routines such as identifying the relevant decision-makers, adjusting how the value proposition is presented in regulated markets, and refining go-to-market assumptions based on feedback from external stakeholders. Overall, the findings suggest a shift from market ambiguity, understanding who needs the innovation and through which pathway, to market execution, which focuses on securing repeatable engagement in markets characterized by long development cycles and regulatory complexity. This also explains why strategic flexibility remains important even in later stages.

Access to specialized technical expertise, including laboratory resources and R&D capabilities, was viewed as challenging at both stages, but to different degrees. Early-stage firms often relied on proximity to universities and research institutions to mitigate infrastructure constraints, using provided laboratory and collaborations to progress R&D. Development phase firms generally experienced fewer difficulties in this issue, having secured more stable technical arrangements or in-house capabilities.

Managerial expertise emerged as more challenging for both stages. Early-stage firms struggled to attract experienced leadership due to limited financial and reputational capital. Conversely, firms in the development stage noted improvement in accessing networks that facilitated the recruitment of skilled managers and mentors.

The findings suggest that managerial capacity tends to scale with organizational maturity and resource acquisition, consistent with an RBV view in which human capital and managerial experience are key strategic resources that must be gradually built and orchestrated.

The stage difference suggests that “technical expertise” and “managerial expertise” follow different development mechanisms. Technical capability constraints are partially buffered early by ecosystem substitutability, e.g., proximity to universities and research institutes allows ventures to borrow infrastructure and expertise instead of owning them, reducing fixed costs and enabling progress before stable internal teams exist. Managerial capability, however, is a nonsubstitutable bottleneck because it depends on credibility, cash, and governance capacity – assets early-stage firms typically lack. The improvement reported by development-phase entrepreneurs is consistent with a threshold effect: once ventures accumulate visible progress (e.g., clearer milestones, funding and partnerships), they become more attractive to experienced managers and mentors, and entrepreneurs can shift from “founder-centric improvisation” toward more formal role structures. At the micro level, this shows up in routines such as clarifying decision rights, building mentor/advisory relationships, professionalizing fundraising communication and delegating specialized tasks, changes that gradually transform managerial expertise from an external dependency into an internalized capability.

Access to capital resources and scientific uncertainty are pervasive and persistent challenges across both stages. In the absence of early grants or seed capital, many firms were unable to advance beyond the proof-of-concept stage. Early-stage entrepreneurs frequently reported significant challenges in persuading investors to support unproven biotechnology concepts, particularly those without immediate revenue prospects. Although firms in the development phase had generally succeeded in raising some rounds of funding, the pressure to secure capital remained strong. The expansion of business operations, initiation of clinical trials and fulfillment of regulatory requirements necessitated increasingly substantial and sustained financial resources.

Although technical progress and early market validation occasionally enhanced the firms’ attractiveness to investors, considerable challenges remained, particularly when seeking larger funding rounds or securing longer-term financial support. The uncertainty of technological development also followed a stage-based pattern. Early-stage entrepreneurs often described the R&D process as filled with uncertainty and scientific risk. Several entrepreneurs acknowledged that they proceeded with only partial assurance that their R&D efforts would lead to viable outcomes. The findings noted that technical feasibility was still being tested during product development. In contrast, founders of companies in the development phase expressed greater initial or eventual confidence in their technological foundations. Many described starting with strong scientific premises or early proof-of-concept data, which gave them more certainty as they progressed. These findings suggest that biotechnology entrepreneurs are required to continuously orchestrate and reconfigure financial and knowledge resources across venture stages in response to persistent funding and scientific uncertainty, aligning with RBV and dynamic-capabilities perspectives on staged resource reconfiguration.

A plausible interpretation is that the co-occurrence of financing pressure and scientific uncertainty reflects a coupled risk mechanism. Because the technical feasibility of new scientific approaches is not yet fully known, investors face high information asymmetry, while entrepreneurs must secure funding to finance the learning process itself. Early-stage accounts suggest that the main challenge is therefore not simply a lack of money, but the difficulty of making an unproven project understandable and credible to investors whose decision criteria often emphasize clearer traction and timelines. In this context, public funding and early private investors, such as angel investors, often function as bridging capital. These sources help finance proof-of-concept activities and strengthen the venture’s legitimacy, which can subsequently facilitate access to additional resources. However, firms in the development phase frequently describe a second resource threshold. Activities such as regulatory work, clinical validation and scaling typically require substantially larger and more sustained funding than earlier stages. In response, entrepreneurs develop practical routines for managing financing under uncertainty. These include identifying funders whose investment criteria match the scientific and regulatory risks, structuring applications and pitches strategically, and adjusting development plans when funding constraints require the re-sequencing of milestones.

Thus, these dynamics help explain why technical uncertainty may gradually decrease while financing pressure remains high as ventures progress through later development stages.

The second theme concerns how different support actors contributed to firm development and capability-building and how their perceived importance shifted over time.

Universities were consistently described as the most critical source of technical and scientific support, particularly during the early stage of firm development. Entrepreneurs emphasized universities’ provision of domain-specific expertise and access to research infrastructure, which were essential for advancing R&D activities. Research institutes such as RISE, a Swedish state-owned research institute, were also identified as important facilitators of technical development and collaborative projects. Across both the early and development stages, these organizations were perceived as underpinning firms’ ability to maintain scientific quality and progress product development.

From an RBV, universities and research institutes can be understood as external knowledge and infrastructure resources that firms draw upon to compensate for limitations in internal capabilities, particularly in the early stage. From a dynamic-capabilities perspective, engagement with these actors appears to support firms’ ability to recognize and refine technological opportunities and to adjust their scientific development trajectories as projects evolve over time.

This suggests that universities and research institutes do more than provide basic inputs such as laboratory access and scientific expertise. They also function as translation infrastructures that help make early biotechnology work credible and understandable to investors, partners and other stakeholders. Entrepreneurs’ accounts indicate that technical collaboration supports important sensing activities, such as interpreting signals about technical feasibility, clarifying what types of evidence are needed next and aligning development choices with scientific standards that investors and partner’s trust.

This stabilizing role remains important across venture stages because it reduces uncertainty about what counts as meaningful progress. In turn, this clarity helps entrepreneurs justify funding requests and engage more effectively in partnership discussions. The continued importance of these actors in both stages suggests that capability development in science-based ventures is not only an internal learning process. Rather, it also involves learning with institutions that shape how problems are framed and how the venture’s development trajectory gains legitimacy.

Entrepreneurs identified incubators and Vinnova as key contributors to company formation and early development. Incubators provided infrastructure, mentorship and strategic guidance, particularly in business development, grant applications and initial investor contacts. Vinnova was viewed as the most consistent and accessible public funder across stages, offering not only grants but also legitimacy and visibility that could unlock further financing opportunities. Several entrepreneurs noted that affiliation with incubators or receipt of Vinnova funding enhanced their company’s perceived credibility within the innovation ecosystem.

These actors also played a crucial role in building dynamic capabilities. Through mentoring, feedback on proposals and assistance in structuring projects, these actors supported entrepreneurs in identifying funding opportunities, refining business ideas and adapting development plans in response to feedback and evolving requirements.

Thus, incubators and public funding from Vinnova can be interpreted as important supports for early capability formation. Incubators provide practical business templates and iterative feedback that help entrepreneurs translate scientific projects into ventures that are easier to communicate and finance. This includes structuring development plans, refining venture narratives and preparing for interactions with investors. Vinnova’s role is not only financial but also institutional. Receiving, or even credibly pursuing, this funding serves as a legitimacy signal that increases the venture’s visibility and makes it easier for other actors to engage. Together, these forms of support help clarify what constitutes credible progress in early stages, for example, how milestones should be defined, how risks can be framed and how the venture can be presented to investors and partners.

This helps explain why entrepreneurs perceive these supports as particularly valuable early on. They accelerate the development of routines that entrepreneurs later internalize as part of their own organizational capabilities.

This study investigates how Swedish biotechnology entrepreneurs perceive the role of various support actors in facilitating access to financial resources. Entrepreneurs identified a diverse set of actors, including public funding agencies, private investors and intermediary institutions, contributing to financial access across both early-stage and development-phase firms.

Support was received as direct financial aid as well as indirect assistance such as credibility, advisory services and network access. A prominent finding across both stages was the central role of Vinnova, which emerged as the most consistent and accessible source of public funding. Vinnova’s support was not limited to grants but also included facilitation of visibility and legitimacy, which many entrepreneurs linked to improved access to additional capital sources. Business angels were also frequently mentioned in both stages and were valued for their speed and flexibility, particularly in early funding rounds; this support often continued into the development phase as follow-on investment, suggesting longer-term relationships rather than short-term capital provision.

Stage differences were evident in which financial actors were perceived as involved and how financing was accessed. Financial actors such as VCs, banks and Almi were perceived as less involved in early-stage development, with entrepreneurs describing limited direct engagement from banks and VCs at that point. Early-stage firms more commonly associated funding with Vinnova and, to some extent, business angels, while universities and incubators were emphasized as providing indirect financial support through strategic guidance, assistance with funding applications and by strengthening credibility and visibility to potential investors. In the development phase, funding became more diversified and structured. Entrepreneurs emphasized the continuing relevance of Vinnova and business angels but also highlighted Almi and incubators as important supporters of financial access. These four actors together constituted the core sources of financing during the development stage. Universities were mentioned as supportive but less directly involved in financing, and indirect financial support from universities and incubators was perceived as less central than in early stages, suggesting that firms increasingly rely on formal financial channels as they mature.

From a dynamic-capabilities perspective, these patterns show how entrepreneurs co-develop sensing and seizing capabilities together with support actors. Early in the lifecycle, incubators, universities and public agencies help founders identify funding opportunities and design viable projects. Later, firms deploy more internalized capabilities to navigate a broader set of financing options, using external support selectively for specific challenges.

A plausible interpretation is that the stage pattern in perceived financial actor involvement reflects a misalignment of selection logics in the early stages. When a venture’s main assets are scientific and uncertainty remains high, banks and many venture capital firms tend to be less responsive because their evaluation criteria require clearer predictability and downside protection. As a result, entrepreneurs rely more on actors whose decision rules tolerate early uncertainty, particularly public agencies and angel investors. At the same time, incubators and universities provide indirect support by strengthening the venture’s credibility and improving the quality of funding applications and investor pitches.

As ventures move into later stages, financing tends to become more diversified. This shift occurs not only because more capital becomes available, but also because ventures become more legible to external financiers. Clearer milestones, more formal governance structures and external endorsements make risks easier to evaluate. However, the continued importance of Vinnova and the uneven availability of private financing channels suggest a potential boundary condition of the Swedish model. While public funding and institutional credibility can sustain early development, later-stage scaling may depend on whether specialized private investors and internationalization support are available or can be effectively accessed by the venture.

The third theme focuses specifically on how entrepreneurs engage with incubators over time.

Early-stage firms reported frequent and multifaceted interactions with incubators, ranging from regular meetings and workshops to one-to-one advisory sessions. In this phase, incubators were perceived as highly valuable partners that provided broad-based support across technical, commercial and strategic domains. Several entrepreneurs noted that incubator staff were closely involved in activities such as refining pitches, preparing funding applications and identifying initial partners.

Development-phase firms, in contrast, described more occasional and selective engagement. While they still valued incubators for specific advice or access to particular programs (for example, connections to Almi or tailored growth initiatives), the relationship had shifted from intensive collaboration to a more peripheral role once internal capabilities and networks had matured.

A plausible interpretation is that the shift from frequent to more selective incubator engagement reflects a change in the incubator’s role across venture stages. In the early stages, incubators often function as a temporary support structure, helping entrepreneurs compensate for missing routines. They provide regular interaction through meetings and workshops, structured feedback and practical guidance that helps translate scientific ideas into fundable projects and credible investor pitches.

In doing so, incubators support the micro-level routines through which dynamic capabilities are enacted, such as interpreting constraints, preparing funding applications and refining venture pitches. As ventures move into later stages, these routines tend to become increasingly internalized within the venture. Consequently, the role of the incubator shifts from continuous involvement to more targeted support, such as specialized programs, introductions to partners or investors or specific strategic advice. This helps explain why incubator engagement becomes less frequent over time without implying that incubators become irrelevant.

Early-stage entrepreneurs consistently emphasized the incubator’s role as a network broker. Many of their first significant contacts with industry stakeholders, potential customers and strategic partners were facilitated by incubator events, introductions and ecosystem activities. Incubators and universities were seen as primary sources of new business contacts and entry points into the broader innovation system.

Development-phase firms reported gaining fewer new contacts via the incubator. Instead, they increasingly relied on their own networks, accumulated over time, to access partners, customers and investors. This shift reflects a reconfiguration of social capital: early-stage ventures depend heavily on bonding social capital rooted in close ties to incubators and academic institutions, while later-stage firms place more emphasis on bridging social capital – targeted ties to investors, strategic partners and clients that enable scaling.

A plausible interpretation is that the shift from bonding to bridging ties can be explained by changing resource needs and credibility requirements across venture stages. In the early stages, ventures benefit from dense, trust-based networks because they provide rapid access to information, introductions and legitimacy that compensate for limited internal resources.

As ventures move toward scaling, however, the resources required become more specialized, such as growth capital, regulatory and commercial expertise, and access to customers or strategic partners. At this stage, dense institutional networks alone may no longer be sufficient if they do not provide connections to the relevant external markets. Entrepreneurs, therefore, begin to activate bridging ties more intentionally. Rather than relying on broad ecosystem exposure, they increasingly seek contacts who can connect them to specific investors, partners or clients.

This transition reflects a micro-level network capability that entrepreneurs learn not only to accumulate relationships but also to reconfigure them as the venture’s key bottlenecks change. As these capabilities develop, ventures become less dependent on incubators as network brokers.

Expectations toward incubator services also differed by stage. Early-stage firms generally expressed satisfaction and did not identify major shortcomings, aside from occasional comments about limited educational or sector-specific competence among incubator personnel. Development-phase entrepreneurs, however, voiced more structural critiques. They highlighted a lack of alternative financing options, insufficient support for internationalization and limited tailoring of services to the needs of scaling firms.

These findings point to a support ecosystem asymmetry. Universities and public funders, particularly Vinnova, appeared as consistently engaged actors across stages, whereas financial actors such as banks, VCs and some intermediaries were perceived as fragile or inconsistently involved, especially in early phases. Incubators and public institutions, therefore, played a compensatory role, bridging structural gaps in the Swedish biotechnology financing and support landscape.

Interpreted through a social-capital lens, the results suggest that incubators act as crucial sources of bonding social capital in early stages, providing dense, trust-based networks that substitute for missing internal resources and legitimacy. As ventures mature, entrepreneurs intentionally reconfigure their networks toward more bridging ties that enable access to growth capital and market opportunities, reducing day-to-day dependence on incubators while still drawing on them as occasional strategic partners.

A plausible interpretation is that the increase in later-stage critique reflects a misalignment between available support and evolving venture needs as firms approach scaling. In the early stages, entrepreneurs tend to evaluate incubators and ecosystem support based on general guidance, legitimacy and initial network access, areas in which these actors are generally well positioned to contribute. In the development phase, however, entrepreneurs increasingly judge support according to whether it helps address more specialized bottlenecks, particularly later-stage financing and internationalization. When the ecosystem lacks coordinated private finance or tailored scale-up pathways, entrepreneurs begin to perceive what may be described as an ecosystem asymmetry. Public agencies and universities remain consistently present, but market-oriented intermediaries, such as specialized investors or international market connectors, appear less reliable.

This pattern highlights an important implication for policy and ecosystem design. Replicating early-stage incubation structures alone is unlikely to generate later-stage outcomes unless complementary mechanisms also exist or are actively developed to connect ventures with specialized capital, strategic partners and international market channels.

This study examined how Swedish biotechnology entrepreneurs navigate resource challenges and interact with incubators and other support actors across the early and development stages of firm growth, using an integrated lens of the RBV, dynamic capabilities and social capital. The findings suggest a stage-dependent co-evolution mechanism between support systems and venture capabilities. In the early stage, biotechnology entrepreneurs typically lack established routines, legitimacy and access to specialized resources. Support organizations such as incubators, universities and public funding agencies therefore function as external scaffolding structures that temporarily compensate for these missing capabilities. Through repeated interaction with these actors, for example, preparing funding applications, refining development plans and receiving feedback – entrepreneurs gradually develop micro-level routines for sensing opportunities, structuring projects and mobilizing resources. As these routines become increasingly internalized, ventures rely less on intensive incubator involvement and engage more selectively with ecosystem actors. At later stages, however, the nature of resource constraints shifts. Firms increasingly require specialized financing, regulatory expertise and commercial partnerships to scale their technologies. This changes the role of the support system from capability scaffolding toward more targeted resource access. The evolution observed in the findings therefore reflects a co-development process in which entrepreneurial capabilities and ecosystem support structures mutually shape one another over time.

Conceptually, these findings point to two stage-linked developmental logics in science-based ventures: a foundational resource-acquisition logic in early stages and a strategic refinement logic in the development phase. In the first logic, entrepreneurs prioritize assembling a minimal configuration of resources and legitimacy, while incubators tend to be experienced as highly involved partners that provide embedded guidance, brokerage and credibility. In the second logic, resource demands become more specialized and growth-oriented, and incubator engagement becomes more selective as ventures internalize routines and expand their own networks. This trajectory unfolds within an ecosystem context in which universities and public funders, most notably Vinnova, appear consistently salient, while access to certain private financial channels is perceived as more uneven, particularly in earlier phases. Together, the model highlights how resource needs, capability formation and network configurations shift in tandem across the venture lifecycle. More specifically, incubators and university-mediated networks (social capital) enable access to critical external resources (RBV). Dynamic capabilities then govern how entrepreneurs repeatedly reconfigure and redeploy these resources and reconfigure their network portfolios as stage-specific demands change.

From an RBV and resource-orchestration perspective, the study shows that biotechnology firms rarely possess the full bundle of strategic resources needed for survival and growth at founding. Instead, entrepreneurs assemble a resource base by combining internal scientific knowledge with externally sourced assets from universities, research institutes, incubators and public agencies. In early stages, orchestration efforts focus on assembling a minimal configuration of resources, scientific legitimacy, access to laboratory infrastructure, basic funding and foundational managerial competence. As firms move into the development phase, resource needs shift toward growth capital, specialized regulatory expertise and advanced commercial capabilities. This stage-sensitive pattern underscores that resource constraints in science-based ventures evolve as firms progress, requiring entrepreneurs to repeatedly re-bundle internal and external resources rather than simply accumulate them.

The findings also underscore the co-development of dynamic capabilities with support actors. In early-stage firms, incubators and public agencies appear to play a central role in helping entrepreneurs sense opportunities and constraints, for example, by interpreting regulatory requirements, highlighting relevant funding schemes and challenging initial assumptions about markets and customers. They also support seizing, by assisting in grant applications, structuring development projects and preparing pitches for investors. In some cases, these actors facilitate transformation by encouraging adjustments to business models or development plans when initial approaches prove unfeasible. In the development phase, these capabilities become more internalized. Entrepreneurs increasingly rely on their own experience and organizational routines to scan the environment, select opportunities and reconfigure operations, while drawing on support actors more selectively for specific challenges. This temporal pattern suggests that dynamic capabilities in biotechnology ventures may be scaffolded by the support ecosystem early on and progressively embedded within the firm, rather than emerging exclusively from internal learning processes.

The third theoretical dimension, social capital, helps explain how resource and capability processes are embedded in evolving networks. In early stages, entrepreneurs rely heavily on incubator-mediated and university-based ties, which provide bonding social capital in the form of dense, trust-based relationships and repeated interactions with a relatively small set of institutional actors. These relationships perform several functions simultaneously: they signal legitimacy to external stakeholders, offer access to technical and managerial expertise and open doors to initial funding opportunities. As ventures develop, entrepreneurs increasingly cultivate bridging social capital, forming more selective and outward-oriented ties with investors, strategic partners and customers. The incubator’s role shifts accordingly – from a highly involved collaborator and broker of many early relationships to a more occasional strategic partner used for targeted introductions, specific programs or advice. Rather than proposing a completely new pattern of incubator evolution, the findings nuance existing accounts by showing how this shift is perceived and enacted by entrepreneurs in a science-based context and how it is intertwined with changing resource and capability needs.

Thus, these insights indicate that resources, capabilities and networks cannot be understood in isolation. Entrepreneurs’ ability to mobilize and recombine internal and external resources across stages depends on dynamic capabilities that are co-shaped through interactions with support actors, and on network structures that convey different forms of social capital as the firm evolves. Similarly, the roles of incubators, universities, public agencies and financial actors can be interpreted not simply as providers of discrete services, but as components of a broader support system that differentially contributes to resource orchestration, capability-building and network reconfiguration over time. By foregrounding entrepreneurs’ own accounts, the study offers a stage-sensitive, empirically grounded picture of how these processes unfold in the Swedish biotechnology ecosystem.

The findings also highlight boundary conditions for transferring the Swedish support model to other contexts. The ecosystem appears particularly effective in the early stages of biotechnology development, where ventures primarily require legitimacy, scientific guidance and early-stage financing. However, the findings suggest that later-stage scaling depends more strongly on access to specialized private capital, commercialization partners and international market channels. In contexts where these market-oriented mechanisms are weak or poorly connected to the public support system, entrepreneurs may experience a support–needs mismatch as ventures mature. Consequently, replicating the Swedish model elsewhere may require not only establishing incubators and public funding programs but also ensuring complementary mechanisms that connect ventures to specialized investors and global market networks.

Managerial expertise presents a similarly uneven landscape across countries. Swedish founders often lack deep experience in regulatory strategy, partnership development and market formation, capabilities that typically take years to accumulate. Comparable limitations are well documented in Japan, where many firms remain small, research-centric ventures with hybrid revenue models and limited commercialization experience (Fujiwara, 2016). In contrast, ecosystems such as Boston and San Diego benefit from repeated cycles of firm creation, failure and success, producing serial entrepreneurs and managers who carry accumulated expertise into new ventures. Universities in these regions play an active role not only in research but also in entrepreneurial training and industry bridging (Kagami, 2019; Majava and Rinkinen, 2024). This strengthens the local base of managerial and entrepreneurial expertise, a capacity that ecosystems like Sweden are still developing. The comparison shows that commercial and managerial skill gaps remain major constraints even when scientific resources are strong.

Across multiple countries, biotechnology development is shaped by the structure and availability of early-stage capital. In Sweden, heavy reliance on public grants and incubators provides an essential foundation, yet these mechanisms become less effective as firms progress into later development phases. A similar pattern is found in Japan, where strong scientific capacity contrasts with fragmented public programs, modest grant sizes and a limited venture-capital market. These structural constraints leave early-stage projects chronically underfunded (Okuyama, 2025a, 2025b).

By contrast, leading US clusters such as Boston and the Bay Area, although also science-intensive, benefit from dense investor–industry networks that actively shape business models, reduce uncertainty and sustain multi-stage financing (Kagami, 2019). The comparative gap illustrates how ecosystem maturity depends not simply on funding volume but on the presence of coordinated, specialized investors capable of managing scientific and regulatory risk.

Financial modeling reinforces these distinctions. Even in sophisticated markets, early-stage therapeutic ventures face high probabilities of negative returns, prompting investors to prefer diversified portfolios (Abouarab et al., 2023). Regions without specialized investors or syndication capacity, such as Japan, Australia, Brazil and Sweden, struggle to absorb this risk. Studies of Australia highlight persistent “network failure,” limiting firms’ ability to secure growth capital domestically (Gilding et al., 2020). These comparisons show how Sweden’s financing challenges reflect a broader pattern among mid-sized biotechnology ecosystems.

Support infrastructures also vary widely in their degree of coordination and strategic function. In Sweden, incubators provide essential early-stage legitimacy, infrastructure access and mentorship, yet their influence tends to diminish as firms mature. This diminishing involvement contrasts with the more integrated models observed elsewhere.

In the USA and China, leading incubators operate as network orchestrators, combining selective admission criteria with deep industry linkages, investor access and clear pathways toward scale-up (Wu et al., 2022). These incubators reduce coordination costs and play an active role in shaping commercial outcomes. Singapore offers a different but equally coordinated model, where public–private partnerships connect ministries, research institutes and multinational firms to support a continuous innovation pipeline (Lee and Vavitsas, 2021). Recent Japanese initiatives similarly aim to build larger “biocommunities” to strengthen translational capacity and attract international actors.

Relative to these orchestrated systems, Sweden’s support system appears more decentralized. While incubators and regional innovation actors provide strong technical and relational support early on, the responsibility for assembling investor, corporate and international networks falls more heavily on entrepreneurs. This divergence helps explain why early support is experienced as valuable yet insufficient for enabling later-stage commercialization.

Based on this integrated interpretation, the study makes three main theoretical contributions, while also suggesting practical implications.

First, the study contributes to RBV and resource-orchestration research in science-based entrepreneurship by documenting how biotechnology startups use external actors as extensions of their resource base in a stage-contingent way. Existing work recognizes that new ventures frequently rely on alliances and institutional support to compensate for resource shortages, but often treats such external resources in aggregate terms. By distinguishing between early and development stages and detailing how entrepreneurs prioritize different combinations of technical, financial and managerial resources over time, this study adds nuance to RBV-based accounts of resource scarcity. It shows that resource orchestration in biotechnology is not a one-off alignment problem but an ongoing process of reconfiguring access to externalized capabilities (such as university laboratories, public grants and incubator services) in response to shifting constraints and opportunities.

Second, the study refines dynamic-capabilities perspectives in entrepreneurial and ecosystem contexts by illustrating how sensing, seizing and transforming activities are co-developed with support actors in early stages and gradually internalized as firms mature. Prior work often conceptualizes dynamic capabilities as firm-internal routines that differentiate performance. The findings suggest a more relational and temporal view: in the formative years of biotechnology ventures, dynamic capabilities are scaffolded by incubators, universities and public agencies that provide interpretive frameworks, funding logics and development templates. As ventures progress, these externally supported processes become embedded in organizational routines, and entrepreneurs rely more on internally developed capabilities to steer growth and reconfiguration. This perspective complements existing theory by showing that dynamic capabilities can originate in, and later detach from, ecosystem-mediated practices, particularly in science-based sectors where public and institutional actors are deeply involved.

Third, the study advances social-capital research on startup support by detailing how entrepreneurs intentionally reconfigure their network structures and shift from reliance on bonding social capital to greater use of bridging social capital as their ventures evolve. The literature has long distinguished between dense, cohesive networks and more open, bridging structures and between incubator-centered and market-centered ties. The present analysis specifies how biotechnology entrepreneurs perceive and manage this transition, and how incubators’ roles change from being central brokers of most early relationships to becoming one among several nodes in a broader, more distributed network of investors, partners and customers. Rather than claiming that the movement from “high-intensity collaborators” to “occasional strategic partners” is itself novel, the contribution lies in clarifying why and how this shift occurs in the context of science-based ventures: it reflects entrepreneurs’ efforts to align their network portfolios with changing resource and capability requirements across developmental stages.

Beyond these theoretical contributions, the findings carry practical implications for the design of entrepreneurship support in science-based sectors. They suggest that incubators and policymakers should pay closer attention to the stage-specific nature of resource needs and capability gaps. Early-stage programs might prioritize integrated technical, business and funding support, recognizing their role in scaffolding dynamic capabilities and building bonding social capital. In contrast, development-phase support could focus more on facilitating access to growth capital, advanced regulatory and commercial expertise, and international networks, thereby complementing rather than duplicating the capabilities that firms have begun to internalize. The results also indicate that closer coordination between public funders, incubators and financial intermediaries could help address perceived gaps in later-stage financing and internationalization support.

While this study provides important insights into the experiences of Swedish biotechnology entrepreneurs, several limitations should be acknowledged. First, the cross-sectional nature of the data constrains our ability to capture how entrepreneurial perceptions and support dynamics evolve over time. A longitudinal study would provide deeper insight into how relationships with incubators, universities and funding actors develop and shift across the entrepreneurial journey. Second, the study is based on a small sample of entrepreneurs and relies on qualitative interview data. This design is appropriate for generating in-depth, contextually rich insights, but it limits the statistical generalizability of the findings. Future research could complement this approach with quantitative or mixed-methods studies that examine the prevalence of the identified patterns across larger samples of biotechnology ventures. Third, the analysis focuses primarily on the perspective of entrepreneurs. While this is central for understanding how founders perceive and use support, it does not capture the views of incubator managers, university representatives, investors or public agency officials. Comparative, multi-actor studies could shed light on how expectations and constraints differ across these groups and how they shape the design and delivery of support. Fourth, the study is conducted within a single national context. Replicating the analysis in other countries could test whether the stage-specific patterns identified here are generalizable across different institutional environments.

This study explored the perspectives of Swedish biotechnology entrepreneurs on the challenges and support mechanisms shaping firm development, with a particular focus on incubators and entrepreneurial ecosystems. By comparing firms in early and development stages, we show that entrepreneurs experience distinct patterns of resource needs, strategic focus and network dependency. Incubators, universities and public agencies such as Vinnova emerge as crucial actors in the early stage, offering access to expertise, credibility and initial networks. In contrast, development-stage firms increasingly depend on direct market engagement and structured financing mechanisms, suggesting a shift from ecosystem dependency to strategic self-sufficiency. The conceptual model developed in this study captures the dynamic nature of incubator engagement, showing how these institutions transition from high-intensity collaborators to occasional strategic partners in the development phase, offering more targeted support as firms increase their internal capabilities and expanding networks. Furthermore, the study contributes theoretically by integrating the RBV, dynamic capabilities theory and social capital theory to explain how entrepreneurs perceive the orchestration, sensing and seizing of external resources across stages.