The review followed PRISMA 2020 guidelines to ensure transparency in search, screening, and synthesis.

In total, 112 records were identified, of which 89 were screened after duplicate removal; 76 full-text articles were assessed for eligibility, and 52 studies were finally included in the review (see Figure 1 ).

Search was conducted in Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. Boolean strings included:

The Boolean search string applied in article indexing databases was:

(“engineering management” OR “technology management” OR “innovation management”)

AND (“higher education” OR “university” OR “HEIs” OR “academic institution”)

AND (“research governance” OR “research management” OR “R&D centers” OR “innovation hubs”

OR “technology transfer” OR “intellectual property management” OR “knowledge management”

OR “portfolio management” OR “commercialization” OR “research productivity”)

AND (PUBYEAR > 2009 AND PUBYEAR < 2026)

Inclusion: peer-reviewed journal articles, books, and proceedings from 2010-2025, explicitly addressing HEIs and innovation governance.

Exclusion: non-peer-reviewed materials, editorials, and studies outside HEIs.

Google Scholar results were cross-checked with Scopus and Web of Science to remove duplicates using title-author-year matching. Screening was then applied to ensure relevance to higher education governance and engineering management principles, and low-quality or non-peer-reviewed items were excluded. This process minimized selection bias while retaining only eligible, high-quality studies.

Studies were coded by governance model, engineering management principle applied, outcomes, barriers, and best practices.

Articles were assessed using the CASP and JBI tools for clarity, rigor, and reliability.

In Table 1 , most studies demonstrated clear objectives, rigorous methods, and relevance, though a few lacked methodological detail (especially in Asia/Latin America case studies).

Using the adapted CASP/JBI checklist in Table 1 , studies scoring ≥ /10 were classified as high quality, those with 5 - 7 as moderate, and those with ≤4 as low. Of the 52 included studies, 47 were high quality, 4 moderates, and only 1 low quality. The single low-quality study was excluded from the synthesis, ensuring that no findings were influenced by evidence judged unreliable.

A total of 52 studies met the inclusion criteria. They covered a range of themes including portfolio and project management ( Mikkola, 2001 ; Philbin, 2017 ), knowledge management (KM) and innovation ( Parikh, 2001 ; Boroujerdi, Hasani, & Delshab, 2020 ; Ngoc-Tan & Gregar, 2018 ), governance frameworks ( Etzkowitz & Zhou, 2018 ; Schuetzenmeister, 2010 ; Arciénaga Morales et al., 2018 ), commercialization and technology transfer ( Dill, 1995 ; McAdam, Keogh, Galbraith, & Laurie, 2004 ; Swamidass, 2013 ), and emerging IT and blockchain tools ( Batubara, Ubacht, & Janssen, 2018 ; van Haaren-van Duijn, Bonnin Roca, Romme, & Weggeman, 2023 ; Rampersad et al., 2012 ).

KM dominates the literature (esp. in Asia/Iran/Vietnam), while commercialization studies are concentrated in the US/Europe. Emerging blockchain/IT governance tools are still underexplored.

All studies were double-coded independently by two reviewers, and discrepancies were resolved through discussion. An inter-rater agreement of 0.87 (Cohen’s kappa) confirmed the reliability of the coding used to generate the percentage distributions in Table 2 .

Portfolio and project management are increasingly adopted to optimize university research investments. In Table 3 , Mikkola (2001) developed the R&D Project Portfolio Matrix as a tool to balance projects across risk and innovation value. Philbin (2017) showed how applying project management methodologies (e.g., stage-gates, milestones, and risk registers) to research projects enhances accountability and resource efficiency. Cooper & Sommer (2016) proposed the Agile-Stage-Gate hybrid, which allows adaptability in dynamic research settings.

KM is a recurring success factor. In Table 4 , Parikh (2001) conceptualized the KM cycle for R&D knowledge, while Boroujerdi et al. (2020) empirically validated the link between KM practices and organizational innovation in 63 Iranian HEIs. Rampersad et al. (2012) found that IT-enabled KM platforms in technology transfer offices improved collaboration and reduced delays in commercialization. In Malaysia and Vietnam, studies confirmed KM’s predictive role in research productivity and innovation culture ( Kowang et al., 2015 ; Ngoc-Tan & Gregar, 2018 ).

Governance models highlight the systems nature of HEIs. Schuetzenmeister (2010) framed research management as “boundary work” connecting science and society. Etzkowitz & Zhou (2018) emphasized the Triple Helix of university-industry-government as a framework for entrepreneurship. In Europe and Latin America, Arciénaga Morales et al. (2018) noted competence-based innovation management frameworks, while Blass & Hayward (2014) foresaw a growing role for social innovation in universities.

Key Insight: Governance is shifting from centralized control toward hybrid multi-actor frameworks, but many universities in developing regions still rely on bureaucratic models that stifle agility.

Commercialization is a critical governance outcome. Dill (1995) highlighted how technology transfer units (TTUs) structure licensing and entrepreneurship. McAdam et al. (2004) proposed process mapping to improve licensing and venture building. Swamidass (2013) showed that university startups, when supported by structured governance (proof-of-concept funds, OTT staff training), outperform traditional licensing in commercialization success.

Emerging tools transform governance structures. Rampersad et al. (2012) demonstrated that IT integration in TT offices improved knowledge flows. Batubara et al. (2018) reviewed blockchain adoption in e-government, identifying scalability, security, and regulation as barriers. Van Haaren-van Duijn et al. (2023) warned of “seven sins” in blockchain ecosystem governance, such as power imbalances and lack of accountability. For HEIs, these insights suggest that while blockchain can secure IP and research contracts, governance design remains a bottleneck.

Drawing from the synthesis of studies, a conceptual framework was developed to illustrate how engineering management principles shape research and innovation governance in HEIs.

The conceptual framework ( Figure 2 ) models how engineering management (EM) principles enhance research and innovation governance in HEIs. Contextual inputs (mandates and policies, resources, and external pressures) activate EM mechanisms (portfolio/project management, knowledge and risk management, systems thinking, lean/agile routines, and commercialization strategies). These mechanisms are operationalized through governance structures (centralized, decentralized, or hybrid), shaping decision rights, coordination, and accountability. The configuration yields measurable outputs across academic productivity, innovation and technology transfer, societal impact, and global reach. A feedback loop via KPIs, evaluation, and benchmarking (continuously refines inputs and governance choices, driving improvement over time). Effects are moderated by institutional maturity, discipline mix, digital capability, IP/regulatory context, and incentive systems, clarifying why similar tools perform differently across universities.

Challenges: structural rigidity ( Jonsson et al., 2015 ), cultural resistance ( Blass & Hayward, 2014 ), funding constraints ( Kowang et al., 2015 ), and policy fragmentation ( Arciénaga Morales et al., 2018 ).

Best Practices: professional research managers bridging academia and industry ( Schuetzenmeister, 2010 ), integrated KM systems ( Boroujerdi et al., 2020 ; Ngoc-Tan & Gregar, 2018 ), Agile-Stage-Gate methods ( Cooper & Sommer, 2016 ), and Triple/Quadruple Helix collaborations ( Etzkowitz & Zhou, 2018 ).

Asia: Strong emphasis on KM and innovation capacity building due to funding/resource constraints ( Kowang et al., 2015 ; Ngoc-Tan & Gregar, 2018 ). Governance remains bureaucratic, making agile adoption slower.

Europe: Focus on collaborative governance and Triple Helix frameworks ( Etzkowitz & Zhou, 2018 ). Strong policy integration drives commercialization.

Latin America: Innovation management models often emphasize social innovation and sustainability ( Arciénaga Morales et al., 2018 ). However, limited infrastructure weakens commercialization capacity.