When we think about what makes a university successful in transferring technology to industry, we usually look at two things: how much talent it has (its researchers) and how much infrastructure it possesses (laboratories, patents, systems). But there is a third element that is almost always poorly measured—or not measured at all—: the quality of its internal relationships and its relationships with the external environment.

 

That is the central conclusion of an exploratory review conducted by Sara J. Anaya-Chaparro and Hugo E. Martínez-Ardila , from the Industrial University of Santander, published in Engineering and Competitiveness (2026). The study analyzed 94 scientific publications indexed in Web of Science to understand how academic literature has addressed intellectual capital in university technology transfer processes. It found an asymmetry with very concrete practical implications for those who manage innovation in higher education institutions.

 

The framework: three types of capital, three levels of maturity

The intellectual capital of a university—understood as the set of intangible assets that allow it to transform knowledge into value—is traditionally composed of three dimensions:

 

  • Human capital: the knowledge, skills, and experience of researchers and staff. This includes academic training, competencies, and motivation.
  • Structural capital: the systems, procedures, intellectual property and infrastructure that support knowledge production—patents, databases, institutional policies.
  • Relational capital: the economic, political, and institutional relationships that the university builds with businesses, government, other institutions, and society. It also includes reputation, trust, and position within collaborative networks.

The review shows that human capital and structural capital have solid conceptual foundations and relatively consolidated forms of measurement: one can count the number of publications, patents, R&D budgets, percentage of staff with postgraduate degrees, etc. These are variables that, despite being intangible, can be identified, operationalized, and compared between institutions.

Relational capital is a different kettle of fish.

 

The central problem: counting agreements is not the same as understanding relationships.

The authors identify that, in most of the studies reviewed, relational capital is measured by:

 

  • Aggregate indicators (number of university-industry agreements, number of consortia, number of joint international projects)
  • Financial indicators (amount of joint financing, business co-financing)
  • Subjective perceptions (Likert-type scales on perception of relationships, trust, reputation)

 

The problem isn’t that these indicators are useless—in fact, they provide a useful snapshot of an institution’s level of relational activity. The problem is that they don’t explain how, why, or under what conditions these relationships translate into concrete technology transfer outcomes : an executed license, a commercialized patent, a successful spin-off, or the use of the generated knowledge.

 

In other words: we know how many relationships a university has, but we don’t know if those relationships are well structured, if they depend on a few key actors (risk of fragility), if they generate real flows of knowledge, or if they simply exist on paper without generating value.

A discovery that connects with the "third mission"

The study frames this within the so-called third mission of universities: the idea that higher education institutions should not only teach and conduct research, but also generate direct economic and social value for their communities. Within this framework, technology transfer is one of the most visible mechanisms of this mission—and depends, almost by definition, on the university’s ability to coordinate with multiple external stakeholders.

This makes relational capital a strategic, not an accessory, resource. And yet, it is precisely the least developed dimension, both conceptually and methodologically, in the literature. The authors summarize it clearly: the gap lies not in recognizing that relationships matter, but in the inability of current metrics to explain their actual contribution to outcomes.

The proposal: social network analysis (SNA)

Here is the practical contribution of the article. The authors propose complementing traditional indicators with social network analysis , a methodology that allows us to go beyond simply counting relationships to understand their architecture :

 

  • Centrality: Which actors (researchers, groups, units) occupy strategic positions within the collaboration network?
  • Betweenness: Who acts as a bridge between clusters that would otherwise not be connected?
  • Density: How interconnected is the network as a whole?
  • Community detection: Are there subgroups or clusters in the collaboration network in which the institution participates?
  • Structural gaps: where are there a lack of connections that could represent untapped opportunities?

 

This approach does not replace traditional indicators of relational capital; rather, it complements them, giving them structural depth. For example, it allows us to identify whether a university’s technology transfer capacity depends excessively on a handful of “bridging” researchers (risk of dependency), or whether robust and diversified collaborative networks exist.

 

Why is this important for those who manage innovation?

For technology transfer offices (TTOs), research vice-rectorates, R&D&I managers and innovation policymakers, this study offers several practical implications:

    1. Evaluating the performance of a TTO solely by the number of agreements signed is insufficient. It is necessary to understand the quality and sustainability of those relationships over time.
    2. Strategic relationship management requires structural visibility. Knowing which actors connect the university with industry—and how dependent that connection is on specific individuals—is critical information for institutional continuity.
    3. Existing international rankings and evaluation models (VAIC™, ICMM, VQR, among others) have documented limitations in capturing the third university mission in a comprehensive way, especially in its relational dimension.
    4. There is an opportunity for differentiation. Institutions that manage to develop more sophisticated systems for measuring relational capital—combining traditional indicators with network metrics—will be better positioned to demonstrate and manage their impact on technology transfer.

     

    The pending agenda

    The authors conclude by pointing out where future research should go:

     

    • Studies that directly connect the dimensions of intellectual capital —especially relational— with observable results: executed licenses, marketed patents, creation of spin-offs, socio-economic impact.
    • Longitudinal studies that examine how university-environment collaboration networks evolve over time.
    • Combining quantitative network metrics with qualitative methods capable of explaining underlying mechanisms such as trust, governance, and the complementarity of capabilities between actors.

In short: simply stating that “relationships matter” for technology transfer is no longer enough. The challenge for universities—and those who advise them on innovation—in the next decade is to develop more precise ways of understanding how these relationships translate into tangible results.

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