Figure 1.
Conceptual framework: key variables and their hypothesized relationships in technology transfer processes. Note: This figure maps the key variables and their hypothesized relationships as an orientation tool for the reader. Arrows represent conceptual paths derived from qualitative insights and observed patterns in the data, not statistically verified causal effects.
Table 1.
Academic tenure and R&D tenure – overall values (N = 386).
| Indicator | Mean (years) | Median (years) | N |
|---|---|---|---|
| Academic employment length - overall | 19.6 | 18.5 | 386 |
| R&D experience | 12.5 | 10 | 312 |
Table 2.
Distribution of respondents by academic degree (N = 386).
| Academic degree | Number of respondents | Percentage share |
|---|---|---|
| PhD | 207 | 53.6% |
| Habilitated PhD | 106 | 27.5% |
| Professor | 38 | 9.8% |
| Master’s degree | 35 | 9.1% |
Figure 2.
Current and past R&D activity (N = 319–386*). *The number of observations varies slightly across different variables due to the specific questions asked: 312 respondents reported having at least one year of R&D experience, whereas 319 answered the question about their current R&D activity (207 active and 112 inactive). Past participation in R&D activity of any kind was reported by 258 respondents (66.8% of the full sample N=386). Source: Own elaboration based on CAWI survey results (N = 319–386).
Figure 3.
Mean competency ratings across R&D activity domains – mean score per competency on a 0–100 scale (N = 386). Note: The ’Other’ category aggregates diverse, self-reported competencies dynamically specified by respondents outside the predefined list. Due to its high heterogeneity, the mean scores for this category should be interpreted with caution. Source: Own elaboration based on CAWI survey results (N = 386).
Table 3.
Self-assessed competency levels across R&D experience groups – mean score per competency on a 0–100 scale (N = 386). Note: The ’Other’ category aggregates diverse, self-reported competencies dynamically specified by respondents outside the predefined list. Due to its high heterogeneity, the mean scores for this category should be interpreted with caution.
| Competency | Currently conducts (n=207) | Conducted in the past (n=51) | Never conducted (n=128) |
|---|---|---|---|
| Soft skills | 76.37 | 72.92 | 73.12 |
| Networking | 52.46 | 47.35 | 39.34 |
| Market knowledge | 50.15 | 44.32 | 38 |
| Obtaining funding | 44 | 35.44 | 22.38 |
| Team building | 60.37 | 52.83 | 34.86 |
| Planning and management of R&D projects | 55.9 | 49.4 | 27.28 |
| Negotiation skills | 48.57 | 42.17 | 34.78 |
| Intellectual property protection | 46.56 | 41.79 | 30.4 |
| Commercialization of R&D results | 33.43 | 30.04 | 18.82 |
| Implementations | 37.46 | 33.04 | 18.72 |
| Other | 30.87 | 15.92 | 16.9 |
Figure 4.
Most expected institutional support actions to facilitate business collaboration (multiple choice, N = 386). Source: Own elaboration based on CAWI survey results.
Figure 5.
Researchers’ definitions of R&D project success (multiple choice, N = 386). Source: Own elaboration based on CAWI survey results.
Table 4.
Typology of barriers to technology transfer identified in the qualitative study.
| Barrier level | Examples of identified barriers |
|---|---|
| Individual | Competency and communication gaps; lack of operational experience; differing time horizons and distinct “languages” between science and business. |
| Institutional | Paralyzing administrative procedures (e.g., public procurement); rigid organizational structures; excessive teaching obligations limiting time for R&D. |
| Systemic | Misaligned evaluation systems (“fighting for points”); lack of formal career benefits for engaging in time-intensive, confidential industry projects. |