Structural determinants of effective academic–industry technology transfer in biomedical engineering

Abstract

Background

While macro-level factors influencing collaboration are well-studied, the daily operational processes between academia and industry remain unclear, especially in biomedical engineering, where coordination is complex and critical. This study fills this gap by analyzing decision-making, governance, and coordination practices from both perspectives to improve technology transfer and innovation outcomes.

Methods

This study employed a mixed-methods approach, including a structured questionnaire distributed among academic and industrial stakeholders, complemented by detailed case studies illustrating practical examples of collaboration in biomedical technology transfer.

Results

Findings reveal both converging and diverging motivations: academia values access to new technologies and societal impact, while industry prioritizes rapid market implementation and competitiveness. Key barriers include time constraints, conflicting publication and confidentiality demands, and challenges in intellectual property negotiations. Support mechanisms such as long-term funding, administrative support, and hybrid work models facilitate collaboration. Case studies demonstrate that successful partnerships hinge on clear regulatory frameworks and mutual understanding of objectives.

Conclusions

Future university-industry partnerships should prioritize clear communication, joint IP frameworks, and inclusive governance from the start. Involving clinical practitioners and end-users early improves product relevance, while proactive regulatory planning, supported by institutional guidance, helps avoid delays and compliance issues.

Keywords

Technology transfer biomedical engineering academic-industry collaboration innovation healthcare technologies

1. Introduction

The biomedical engineering sector occupies a unique position at the intersection of clinical need and technological innovation, requiring close and sustained collaboration between engineers, clinicians, and industry partners. This interdisciplinary field has generated transformative advances in medical devices, digital health solutions, and biotechnologies, yet the pathway from academic research to healthcare application is rarely straightforward.^1–3 For example, Amaral et al.⁴ highlight that stringent regulatory requirements and complex approval processes substantially prolong development timelines and pose significant challenges for innovators in medical devices. Moreover, ethical considerations around patient safety further complicate the translation of laboratory findings into market-ready solutions.⁵

University–industry collaboration is widely recognized as a key driver of biomedical innovation.^6,7 Prior research highlights several critical success factors, including senior management commitment, effective communication, trust-building, and long-term strategic planning. For example, O’Dwyer et al.⁸ stress that communication and trust are fundamental yet often underestimated in sustaining collaborations, while Tereshchenko et al.⁹ emphasize the importance of leadership engagement and strategic foresight. However, persistent challenges such as misaligned incentives, regulatory constraints, and intellectual property (IP) disputes continue to impede effective knowledge and technology transfer.¹⁰ In biomedical engineering, these challenges are further intensified by sector-specific factors, including the complexity of clinical validation, prolonged product development cycles, and stringent regulatory approval processes aimed at ensuring patient safety.^5,11 Beyond these structural hurdles, cultural and organizational differences often undermine collaboration. Academic institutions primarily pursue fundamental knowledge with long-term research objectives, whereas industry prioritizes rapid commercialization and profitability.^11,12 This divergence in priorities can lead to delays, mutual frustration, and suboptimal outcomes.¹³ Timeline mismatches are particularly problematic, as industry often seeks accelerated delivery to remain competitive, while academic research is constrained by lengthy funding cycles and peer-review processes.^14,15 Bao et al.¹⁴ identify this misalignment as a key challenge to effective collaboration. Broader societal factors also shape partnership outcomes,¹⁶ and insufficient institutional suport such as the absence of dedicated technology transfer offices with sector-specific expertise is often perceived by industry partners as a major impediment to productive engagement.^11,17

While prior studies have made notable progress in identifying macro-level determinants of collaboration success such as national innovation systems and policy frameworks there is still limited understanding of the micro-level operational mechanisms that ultimately determine whether partnerships succeed or fail in practice.^7,18 Existing research rarely captures the fine-grained, day-to-day processes through which academic and industry actors negotiate priorities, allocate resources, and navigate regulatory and organizational constraints. This blind spot is particularly critical in biomedical engineering, where the stakes are high, timelines are long, and coordination demands are uniquely complex. Based on the reviewed literature, these underexplored mechanisms include internal decision-making workflows, contractual arrangements, governance structures, and cross-sector coordination practices that directly shape daily collaboration dynamics. This study advances the field by offering the first empirically grounded, stakeholder-informed analysis of these structural determinants in the biomedical engineering context. By integrating perspectives from both academia and industry, it not only maps the procedural and organizational factors that affect translational efficiency but also identifies actionable strategies to accelerate technology transfer and improve innovation outcomes.

2. Methods

2.1. Study design and participants

This study adopted an exploratory-descriptive design to examine the structural and organizational mechanisms influencing effective technology transfer between academia and industry in the biomedical engineering sector. Given the scarcity of empirical research addressing the operational dynamics of such collaborations, an exploratory approach was considered appropriate for uncovering patterns, perspectives, and practices that remain insufficiently understood. The descriptive component systematically mapped existing forms of collaboration and technology transfer processes based on the experiences of stakeholders directly engaged in innovation activities. A mixed-method strategy was implemented, integrating qualitative insights from structured interviews with quantitative indicators of technology transfer performance and case-based analysis. The scope of the study encompassed biomedical engineering technologies, including orthopedic implants, digital health applications, and patient-specific medical devices. A total of 180 participants were recruited from academic and industrial institutions involved in the development and commercialization of biomedical technologies. Inclusion criteria required at least three years of experience in research and development (R&D) or innovation-related roles, direct involvement in a minimum of two technology transfer processes, and a decision-making or expert position within their organization. To ensure balanced representation, the sample included 53.9% academic stakeholders (e.g., university researchers, technology transfer officers, clinical engineers affiliated with academic hospitals) and 46.1% industry representatives (e.g., R&D directors, innovation managers, business development executives from medtech companies).

2.2. Research tool and data collection

To ensure methodological rigor and to capture both qualitative and quantitative dimensions of technology transfer processes, the study employed a triangulated data collection strategy. First, structured interviews were conducted with all 180 participants to examine technology transfer mechanisms, identify operational bottlenecks, and document perceived best practices in academic–industry collaboration. Second, a quantitative analysis was carried out using objective indicators of technology transfer performance, including: (i) the number of patents filed and licensed by participating institutions (as recorded in the European Patent Office [EPO] database), (ii) the volume of joint R&D projects that reached the product commercialization stage, and (iii) the success rate of clinical validation efforts for transferred technologies. Third, a case-based mapping was conducted for two representative biomedical engineering projects selected to illustrate contrasting transfer outcomes - one culminating in successful commercialization and the other in project discontinuation. These cases were chosen based on the completeness of documentation across technical development, regulatory navigation, and market engagement. Each case was systematically analyzed to assess the influence of organizational structures, regulatory compliance, and stakeholder alignment on the ultimate trajectory of the technology.

2.3. Survey instrument

To complement the interviews and provide a broader quantitative validation of their findings, a standardized survey instrument was developed to capture key variables related to technology transfer practices. The survey comprised 25 closed-ended and 5 open-ended questions designed to assess the frequency, scope, and nature of academia–industry collaboration; identify primary drivers and obstacles in the technology transfer process; and collect respondent recommendations for systemic improvements. The closed-ended items targeted measurable dimensions such as specific forms of collaboration (e.g., licensing agreements, joint ventures, contract research), perceived benefits (e.g., access to cutting-edge research, accelerated innovation cycles), and commonly encountered barriers (e.g., administrative delays, funding misalignment, intellectual property disputes). Responses were recorded on a 5-point Likert scale, enabling comparative analysis across respondent groups. The open-ended questions explored more nuanced aspects, including organizational culture differences, intersectoral communication challenges, and structural recommendations for optimizing collaboration frameworks. Prior to full deployment, the questionnaire underwent a pilot study with 15 participants representing both academic and industrial stakeholders. Feedback from the pilot phase was used to refine wording, ensure thematic relevance, and assess completion time. Content validity was established through expert review by three independent specialists in technology transfer and biomedical innovation. Internal consistency of the closed-ended items was assessed using Cronbach's alpha, yielding an overall coefficient of 0.84, indicating acceptable to high internal reliability.

2.4. Procedure

Data collection was conducted between April and December 2024 using a multi-modal strategy that integrated structured interviews, standardized surveys, and documentary analysis to achieve methodological triangulation. Participants were recruited through institutional networks, professional associations, and targeted outreach to organizations engaged in biomedical technology development and commercialization. Depending on participant preference and logistical feasibility, data were gathered via online video interviews, in-person site visits, or self-administered questionnaires distributed through secure digital platforms. Prior to participation, all respondents received an information sheet detailing the study's purpose, scope, and confidentiality provisions, and provided informed consent. To ensure anonymity and protect data, all responses were de-identified and stored in encrypted databases accessible only to the research team. Documentary analysis was conducted in parallel with primary data collection. This involved a systematic review of institutional commercialization reports, project dossiers, and publicly available databases such as the European Patent Office (EPO). The documentary evidence was used to cross-verify self-reported technology transfer outcomes, with particular attention to patent activity, project progression, and commercialization milestones.

2.5. Case selection and description

To deepen the empirical analysis and contextualize findings from the interviews and survey, two in-depth case studies were conducted, each illustrating a distinct trajectory of academic–industrial collaboration in biomedical engineering. The aim of this component was to reveal structural, organizational, and procedural factors that influence the success or failure of technology transfer in practice. Cases were selected through purposive sampling to maximize contrast across key outcome dimensions. Both originated from technology transfer projects initiated between 2018 and 2022 at the Biomedical Engineering Institute in partnership with private-sector entities. Inclusion criteria required a formal collaboration agreement, documented milestones (technical, regulatory, and financial), and comprehensive project records covering development and commercialization phases. The first case focused on the creation of custom orthopedic insoles using advanced 3D scanning and additive manufacturing. Early industrial involvement at the design stage facilitated alignment between technical and commercial objectives, enabling the project to reach a high technical readiness level (TRL 8). The second case examined the development of a therapeutic sensory integration tunnel for children with autism spectrum disorder (ASD), designed to provide controlled sensory stimulation. This initiative stalled at TRL 4 due to unresolved technical validation issues and persistent misalignment between academic and industrial partners.

3. Results

3.1. Sample characteristics

A total of 180 respondents took part in the study, representing both academic institutions and industry organizations engaged in biomedical research, development, and commercialization. The response rate was 25.57%, calculated from targeted outreach to 704 eligible stakeholders who met the predefined inclusion criteria. As described in Section 2.1, the sampling strategy ensured that all participants had at least three years of experience in R&D or innovation-related activities, direct involvement in a minimum of two technology transfer processes, and a decision-making or expert role within their organizations. The final sample was nearly balanced between academia (53.9%) and industry (46.1%). Respondents included technology transfer specialists, innovation managers, R&D directors, clinical engineers, and business development professionals, providing a diverse range of perspectives across the technology transfer ecosystem.

3.2. Motivations for academic–industry collaboration

The most frequently cited motivation was access to new technologies and practical knowledge (28.3%), highlighting the shared interest in applying emerging scientific innovations to real-world contexts. Enhancement of innovation capabilities ranked second (22.8%), particularly relevant in environments where dynamic market conditions and technological convergence require agile collaboration. Financial support for R&D (18.9%) also emerged as a major driver, underscoring the role of intersectoral partnerships in securing resources for high-risk or capital-intensive projects. Opportunities for employee upskilling and capacity building such as internships, joint training programs, and knowledge exchange were noted by 16.1% of respondents, while addressing broader societal challenges (e.g., public health needs, population aging) was cited by 13.9%.

3.3. Barriers to effective technology transfer

The most frequently reported challenge was time constraints related to academic obligations, cited by 26.1% of respondents, predominantly from university settings. Participants noted that the demands of teaching, grant writing, and publication pressures often leave insufficient time and flexibility to engage in long-term industrial collaborations. Another major barrier, reported by 23.9% of participants, was the low level of private-sector investment in R&D, particularly in early-stage technologies. This limited the scope of joint research activities and hindered the scalability of innovation pathways. Moreover, conflicting priorities regarding knowledge dissemination and confidentiality were emphasized by 21.1% of respondents. While academic stakeholders are incentivized to publish research findings, industrial partners prioritize IP protection and competitive advantage, leading to misaligned expectations. Differences in project timelines between academia and industry also emerged as a recurrent friction point (17.8%). Academic research often operates on extended timescales oriented toward foundational discovery, whereas industry typically seeks short to medium-term results that can be rapidly implemented in the marketplace. Additionally, trust issues and challenges in negotiating IP ownership were highlighted by 11.1% of participants, emphasizing the necessity of transparent legal frameworks and clearly defined ownership structures to prevent disputes. Overall, these barriers reflect the structural misalignment between academic and industrial incentives. Addressing them requires intermediary mechanisms, standardized legal templates, and early stakeholder engagement to build trust and reduce transactional frictions in collaborative innovation.

3.4. Funding mechanisms supporting spin-out enterprises

The study also underscored the pivotal role of funding and institutional support mechanisms in enabling successful spin-out ventures emerging from academic research. A significant majority of respondents (87.2%) identified stable, long-term funding as essential not only for initiating joint projects but also for supporting downstream activities necessary for commercialization. Equally, 87.2% of participants emphasized the importance of administrative and organizational support, particularly in navigating bureaucratic processes, compliance requirements, and partnership formalities. Respondents noted that without adequate project coordination, legal facilitation, and regulatory assistance, even well-funded collaborations may stall. Recognition of collaborative engagement in academic performance evaluation systems was cited by 26.1% of respondents as a potential incentive for more frequent and sustained participation in industrial projects. Furthermore, 21.1% proposed institutionalizing dual-affiliation models or time-sharing frameworks, allowing researchers to operate across academic and industrial environments without compromising their primary responsibilities.

3.5. Case-based mapping results

3.5.1. Case 1: successful transfer - custom orthopedic insoles

This case study presents a successful transfer of biomedical technology involving the development of custom orthopedic insoles utilizing advanced 3D scanning and additive manufacturing (AM) methods. The project was a collaborative effort between the Biomedical Engineering Department at Bialystok University of Technology and a medical device manufacturer. The innovation was grounded in biomechanical research focused on pediatric patients with foot deformities. This research facilitated the design and implementation of novel computer-aided design (CAD) and computer-aided manufacturing (CAM) techniques for producing personalized orthopedic insoles. The academic partner provided robust scientific validation regarding optimal insole materials and confirmed the clinical efficacy of the manufacturing process, substantially mitigating financial risk for the industry collaborator. From a regulatory perspective, the product achieved certification under the European Union Medical Device Regulation (MDR) as a Class I medical device, facilitating timely market introduction. Key success factors included early and continuous engagement of the industrial partner during the design phase, establishment of joint intellectual property agreements, and implementation of shared governance structures. Close collaboration with orthopedic specialists and extensive clinical validation were critical in ensuring the product's effectiveness and acceptance by end users. Furthermore, active participation of physicians from Bialystok enhanced the integration of modern podiatric interventions into clinical practice, thereby improving patient care quality and accessibility.

3.5.2. Case 2: failed transfer - sensory integration tunnel for children with autism spectrum disorders

The second case focused on the development of a therapeutic sensory integration tunnel designed to support children with autism spectrum disorder (ASD) through controlled sensory stimulation. Unlike the first case, this project encountered considerable challenges and stalled at a technical readiness level (TRL 4) due to difficulties in technical validation and lack of collaborative alignment. The academic research team and the industrial partner struggled to reconcile design specifications with clinical requirements, resulting in significant delays and miscommunication. Regulatory approval was not obtained, mainly because of incomplete safety testing and the absence of standardized clinical validation protocols specific to sensory therapeutic devices. Usability testing involving therapists and children was limited in both scope and frequency, contributing to poor prototype acceptance among intended end-users. Consequently, the project did not advance to commercialization and was ultimately discontinued. Key barriers identified in this case included fragmented collaboration structures, unclear regulatory and reimbursement pathways, and insufficient engagement with clinical practitioners and end-users during critical stages of product validation. This case highlights the pivotal importance of integrated stakeholder involvement, early regulatory planning, and iterative user-centered design in the successful transfer of biomedical technologies.

3.6. Comparative analysis of collaboration priorities between academia and industry

The data-driven analysis of motivations and barriers reveals key areas of natural synergy, particularly in technological innovation, where academic–industry collaboration tends to thrive. Nonetheless, persistent friction points such as tensions between academic publication mandates and the need for IP protection, along with mismatches in project timelines, continue to impede optimal cooperation. These challenges highlight the urgent need for institutional reforms and policy-level adjustments.¹⁹ A detailed distribution of responses to Likert-scale items assessing the perceived importance of academic–industry collaboration outcomes is presented in Figure 1.

Figure 1.

Distribution of responses regarding the perceived importance of academic–industry collaboration outcomes (5-point Likert scale; N = 180).

The chart presents the distribution of responses, illustrating how participants rated the relevance of specific outcomes. Color coding reflects the level of importance assigned: unimportant (orange), slightly important (light orange), moderately important (gray), very important (light blue), and extremely important (dark blue). To deepen the understanding of stakeholder perceptions, a comparative analysis was conducted between respondents from academic institutions (n = 97) and industry representatives (n = 83). Responses were disaggregated by sector to highlight distinct priorities in collaborative outcomes (Figure 1). New research projects were rated as “extremely important” by 62% of academic respondents, compared to 41% in industry, indicating a stronger emphasis on scientific discovery within academia. Product development and design, on the other hand, received higher importance from industry respondents, with 55% rating “product design” as “very” or “extremely important”, versus 38% in academia. Patent-related outcomes were prioritized more by the industrial sector (47% “extremely important”) than by academia (29%), reflecting a commercial focus. Publications were overwhelmingly important for academic respondents (72% “very” or “extremely important”), while industry participants showed moderate interest (36%). Academic entrepreneurship and spin-offs were viewed favorably across both groups but were rated as “extremely important” by 49% of industry respondents compared to 35% in academia. Skill development opportunities (e.g., employee training and knowledge exchange) were consistently rated as “moderately to very important” in both sectors, with no statistically significant difference. Chi-square tests revealed statistically significant differences (p < 0.05) between academia and industry in their valuation of new research projects, product development, patent outcomes, publications, and entrepreneurship. However, skill development opportunities showed no significant difference, suggesting common ground in the perceived value of collaborative knowledge exchange and training.

4. Discussion

The findings of this study align with and extend the existing literature on motivations driving university–industry collaborations. The most frequently cited motivation was access to new technologies and practical knowledge (28.3%), confirming the strong mutual interest in leveraging emerging scientific innovations for real-world applications, a trend also noted by Atta-Owusu et al. in the Norwegian context.¹⁸ Similarly, the importance placed on enhancing innovation capabilities (22.8%) reflects the need for agility and adaptation in dynamic technological environments, echoing observations by,¹¹ who emphasize digital transformation as a key driver of university–industry cooperation in science, technology, engineering, and mathematics (STEM) fields. Financial support for R&D (18.9%) emerged as another pivotal motivation, highlighting the critical role of collaborations in pooling resources for high-risk or capital-intensive projects. A point also raised by Bao et al.,¹⁴ who identify funding constraints as a major barrier requiring strategic management. The value placed on employee upskilling and capacity building (16.1%) resonates with Austin et al.,²⁰ who highlight the role of joint training programs and knowledge exchange in enhancing the innovation ecosystem, particularly for SMEs in the digital health sector. Motivation related to addressing broader societal challenges (13.9%) adds an important dimension, paralleling the findings of,⁷ who discuss societal impact as a growing focus in academic–industry partnerships, especially in applied research domains. The comparative analysis (Table 1) revealed both convergences and divergences in motivations, barriers, and support mechanisms across academia and industry.

Table 1.
Comparison of motivations, barriers, support mechanisms, and outcomes in academic–industry collaboration.

Category Academia Industry

Motivations for collaboration −
Access to new technologies and practical knowledge (28.3%)
−
Enhancing innovation capabilities (22.8%)
−
Securing funding for research (18.9%)
−
Employee upskilling through training and internships (16.1%)
−
Addressing societal challenges (13.9%)
−
Access to innovative, market-ready solutions (28.3%)
−
Adaptation to technological change (22.8%)
−
Financial support for implementations (18.9%)
−
Workforce skill development (16.1%)
−
Corporate social responsibility (13.9%)

Key barriers to collaboration −
Lack of time due to academic teaching and research duties (26.1%)
−
Conflict between publication needs and business confidentiality (21.1%)
−
Lack of academic career recognition for collaboration (14.0%)
−
Misaligned project timelines (17.8%)
−
Trust issues and IP negotiation difficulties (11.1%)
−
Low level of R&D investment (23.9%)
−
Risk of premature knowledge disclosure (21.1%)
−
Bureaucratic and legal obstacles (14.0%)
−
Limited institutional flexibility (17.8%)
−
Misunderstanding of academic culture (11.1%)

Key support mechanisms −
Long-term funding for collaborative projects (87.2%)
−
Administrative support for project implementation (87.2%)
−
Recognition of collaboration in academic evaluation (26.1%)
−
Formal rules for dual affiliation or time-sharing (21.1%)
−
Stable access to innovation funding (87.2%)
−
Efficient formal/legal project support (87.2%)
−
Practical research application and commercialization (26.1%)
−
Hybrid working models with academic staff (21.1%)

Most important collaboration outcomes −
Joint research projects, patents, utility models, new product development
−
Commercialization of research
−
Practical application of scientific outcomes
−
Marketable new technologies, products, and processes
−
Increased competitiveness
−
Secured IP and fast-track implementation

Category	Academia	Industry
Motivations for collaboration	− Access to new technologies and practical knowledge (28.3%) − Enhancing innovation capabilities (22.8%) − Securing funding for research (18.9%) − Employee upskilling through training and internships (16.1%) − Addressing societal challenges (13.9%)	− Access to innovative, market-ready solutions (28.3%) − Adaptation to technological change (22.8%) − Financial support for implementations (18.9%) − Workforce skill development (16.1%) − Corporate social responsibility (13.9%)
Key barriers to collaboration	− Lack of time due to academic teaching and research duties (26.1%) − Conflict between publication needs and business confidentiality (21.1%) − Lack of academic career recognition for collaboration (14.0%) − Misaligned project timelines (17.8%) − Trust issues and IP negotiation difficulties (11.1%)	− Low level of R&D investment (23.9%) − Risk of premature knowledge disclosure (21.1%) − Bureaucratic and legal obstacles (14.0%) − Limited institutional flexibility (17.8%) − Misunderstanding of academic culture (11.1%)
Key support mechanisms	− Long-term funding for collaborative projects (87.2%) − Administrative support for project implementation (87.2%) − Recognition of collaboration in academic evaluation (26.1%) − Formal rules for dual affiliation or time-sharing (21.1%)	− Stable access to innovation funding (87.2%) − Efficient formal/legal project support (87.2%) − Practical research application and commercialization (26.1%) − Hybrid working models with academic staff (21.1%)
Most important collaboration outcomes	− Joint research projects, patents, utility models, new product development − Commercialization of research − Practical application of scientific outcomes	− Marketable new technologies, products, and processes − Increased competitiveness − Secured IP and fast-track implementation

Percentages indicate the proportion of respondents in each group selecting a given category; identical values reflect comparable response distributions.

The challenges identified in this study reflect those frequently reported in the broader literature on university-industry cooperation, highlighting persistent structural and cultural misalignments. Time constraints related to academic obligations, cited by 26.1% of respondents, align with findings by,¹¹ which emphasize how teaching loads, grant writing, and publication pressures limit academics’ capacity to effectively engage with industry partners. Institutional reforms and workload adjustments appear necessary to enable more sustained and flexible collaboration. The low level of private-sector investment in R&D (23.9%) echoes concerns raised by,¹⁴ particularly regarding funding challenges for early-stage, high-risk projects. This barrier restricts not only joint research activities but also the scalability and commercialization potential of innovations, as further discussed by Battaglia et al.,²¹ who highlight misaligned funding priorities between academia and industry. Conflicting priorities around knowledge dissemination and confidentiality (21.1%) reflect a classic tension identified by He et al.,⁵ where academia is incentivized to publish, while industry prioritizes intellectual property protection to maintain competitive advantage. This misalignment can cause delays and mistrust, underscoring the need for clear agreements on IP management and publication rights early in the collaboration process. Fernandes et al.⁷ also stress the importance of standardized legal frameworks to effectively navigate these conflicts. Differences in project timelines (17.8%) between academia's longer-term research focus and industry's demand for short-term results have been noted in several studies.^18,20 Trust issues and challenges in negotiating IP ownership (11.1%) reiterate findings by,^11,12 which identify trust as a fundamental enabler of successful collaboration. Building trust requires transparent legal agreements, early stakeholder engagement, and mutual understanding of each partner's goals and constraints. Administrative and organizational support is also vital, as noted by Evans et al.¹¹ and De Silva et al.¹² Navigating complex bureaucracy, legal frameworks, and compliance requirements can overwhelm both researchers and industry partners, so effective project coordination and legal facilitation are crucial, particularly in highly regulated fields like biomedical engineering. Recognition of collaboration within academic evaluation systems (26.1%) could incentivize greater participation, consistent with He et al.,⁵ who argue that current reward structures often undervalue industry engagement. Adjusting incentive mechanisms to formally acknowledge applied research and collaborative outcomes may motivate academics to dedicate more effort to industry partnerships. Moreover, institutionalizing dual-affiliation or time-sharing models (supported by 21.1% of respondents) addresses key structural challenges, allowing researchers to operate across academic and industrial environments without compromising primary responsibilities, as discussed by Atta-Owusu et al.¹⁸ and Evans & Miklosik.¹¹

Given these challenges and differing expectations, the theory of open innovation is particularly relevant. It suggests that companies should actively incorporate external knowledge and technologies into their innovation processes.^9,17 Unlike the traditional closed innovation model, which relies solely on internal resources, open innovation encourages a free flow of information between organizations.^22,23 University–industry collaborations exemplify outside-in innovation, where businesses leverage academic expertise to accelerate and enhance their innovation efforts.²⁴

The comparative analysis of the two case studies illustrates critical factors influencing the success and failure of university-industry technology transfer in biomedical engineering. The first case, involving custom orthopedic insoles, highlights how early and continuous engagement of the industrial partner, clear intellectual property agreements, and shared governance structures contribute significantly to successful collaboration. This aligns with findings from Evans et al.,¹¹ Battaglia et al.,²¹ and Desai et al.^²⁵ who emphasize the importance of well-defined collaboration frameworks and joint ownership arrangements in mitigating conflicts and facilitating effective knowledge transfer. Conversely, the failed transfer case of the sensory integration tunnel underscores how fragmented collaboration, insufficient clinical validation, and lack of early regulatory planning can derail technology commercialization efforts. Difficulties in reconciling design and clinical requirements and limited involvement of end-users mirror observations by Bao et al.¹⁴ and He et al.,⁵ who report that misaligned expectations and poor communication between academia and industry often result in projects failing to meet market or user demands. Moreover, the absence of standardized clinical protocols and incomplete safety testing highlight a recurring challenge in biomedical innovation- navigating complex regulatory landscapes and ensuring compliance.¹⁸

5. Conclusions

Therefore, the objectives set at the outset of this research have been met, as the study successfully delivered an empirically grounded, stakeholder-informed analysis of structural determinants in biomedical engineering, while also generating practical recommendations to enhance technology transfer and innovation outcomes. Practically, these findings suggest that future university–industry partnerships should prioritize establishing clear communication channels, joint intellectual property frameworks, and inclusive project governance early in the collaboration process. In biomedical engineering, where product development must integrate engineering precision with clinical safety requirements, these structures are essential to align objectives and manage complex workflows. Moreover, involving clinical practitioners and end-users during the design and validation phases can significantly enhance product relevance and acceptance. Proactive attention to regulatory considerations, particularly stringent in medical device development, is vital to prevent delays and compliance issues, ideally supported by institutional mechanisms that guide both researchers and industry partners through the translational process.

Limitations of this study include the predominance of multiple-choice questions, which may have restricted the depth and richness of qualitative insights. Future research should incorporate a broader range of methodologies and data sources to achieve a more comprehensive understanding of university-industry collaboration dynamics.

Footnotes

Acknowledgements

The authors would like to thank B. Hoscilo and A. Ruszewski for their assistance in distributing the survey.

Ethics statement

Ethical review and approval were waived for this study because the research involved an anonymous survey of professionals (scientists and entrepreneurs) regarding structural and organizational processes. The study did not involve clinical trials, human tissue, or sensitive personal medical data, posing no risk to participants. Participation was entirely voluntary, and informed consent was obtained from all subjects at the start of the survey. All data were processed and stored in accordance with the principles of the Declaration of Helsinki and GDPR regulations.

ORCID iD

Jolanta Pauk

Author contributions

Conceptualization: J.P. and A.S.; Methodology: J.P. and K.P.; Survey design - J.P and A.S.; Formal analysis: J.P and K.P.; Writing - original draft preparation: J.P and K.P..; Writing - review and editing: J.P and A.S. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was co-financed by the state budget under the Minister of Education and Science program Science for Society II (Project no. NdS-II/SP/0181/2023/01).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are provided in an aggregated and anonymized format to protect the privacy and confidentiality of the survey participants (scientists and industry professionals).

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