Analysis of results VQR 2020-24
Introduction, aims and structure of the report
This Report analysing the results of the Research Quality Assessment (VQR) 2020–2024 has been prepared with the aim of providing a comprehensive, critical and planning-oriented analysis of the results achieved by the Politecnico di Bari in the latest national assessment exercise promoted by ANVUR.
This document is based on the Institution Report for the Politecnico di Bari, published as part of the VQR 2020–2024, and offers an integrated analysis in light of the requirements of internal research governance, quality assurance and continuous improvement. Its purpose is not to reproduce the ANVUR Report, which remains the official reference for data and indicators, but to produce a university document capable of explaining the significance of the results, highlighting performance profiles and identifying priorities for action in preparation for the next planning cycle.
The VQR as a research governance tool
The VQR is the main national exercise for assessing research quality. It helps measure the quality of universities’ scientific output, the effectiveness of recruitment and training policies, their capacity to enhance knowledge, and their competitiveness in obtaining funding through competitive projects.
The indicators produced by the VQR are of strategic importance to universities, both because of their potential impact on university system funding and because they are used in self-assessment, accreditation, strategic planning and the evaluation of internal units.
For this reason, analysis of the results cannot be limited to describing numerical values. The indicators must be interpreted in light of the university’s size, the disciplinary composition of its departments, the distribution of research outputs across scientific fields and its relative standing compared with similar institutions.
VQR regulatory and methodological framework
2.1 Regulatory and documentary framework
The VQR 2020–2024 forms part of the national framework for assessing the quality of university research and was entrusted to ANVUR by the Ministry of University and Research. The Institution Report for the Politecnico di Bari cites, among its main sources, Ministerial Decree No. 998 of 1 August 2023 and the Call for Participation in the VQR 2020–2024, adopted by decree of the President of ANVUR No. 8 of 31 October 2023.
The VQR aims to assess the quality of the scientific research results produced by institutions and their internal units, including departmental structures.
The VQR 2020–2024 exercise evaluates scientific output relating to the 2020–2024 period and considers research staff affiliated with the institutions and in service as of 1 November 2024.
The VQR aims to assess the quality of the scientific research results produced by institutions and their internal units, including departmental structures. In the 2020–2024 exercise, the scope of the evaluation includes not only scientific output, but also knowledge valorisation and the financial value of international competitive projects.
Organisation of the University VQR process
To manage the 2020–2024 Research Quality Assessment (VQR) exercise, the Polytechnic University of Bari adopted a structured organisational model, formalised by Rector’s Decree no. 1379 of 7 December 2023. The decree confirms and consolidates the organisational framework that had already been successfully tested in the previous 2015–2019 VQR, while introducing significant measures to strengthen coordination, governance, process support tools, and the planning and monitoring of activities.
The approach adopted is based on a multi-level governance model, characterised by a clear allocation of roles and responsibilities between the central and departmental levels, following a process-based logic to ensure the continuous involvement of academic and administrative units throughout every stage of the evaluation. Other key features of the model include the integration of scientific and administrative expertise, the use of advanced digital tools, the standardisation of procedures, and ongoing monitoring.
The model is based on three levels of functional responsibility, with process leads identified at both University and departmental level, supported across the board by technical and administrative coordination that assists all those involved. The following diagram summarises the University organisational chart adopted for the 2020–2024 VQR exercise.
Analysis of the University’s overall results
The analysis focuses on five dimensions: completeness of research output submissions, distribution of output by scientific field, quality of scientific output, knowledge valorisation, and ability to attract international competitive projects.
The first assessment element concerns the University’s ability to complete the submission of the expected outputs. The ANVUR Report indicates that the Polytechnic University of Bari had 1,016 expected outputs and submitted 1,016, with no missing outputs. This figure signals full coverage of the scope required by the VQR and confirms the effectiveness of the internal process for overseeing and coordinating submissions.
The distribution of expected outputs highlights the prominence of the University’s core areas and its scientific profile. In particular, Area 9, Industrial and Information Engineering, accounts for the largest share, with 576 expected outputs, equal to approximately 56.7% of the University total. This is followed by Areas 8a and 8b, with 154 and 145 outputs, respectively. Overall, Areas 8a, 8b and 9 account for 875 outputs, or approximately 86.1% of the total expected outputs.
Analysis of research quality indicators
Quality profiles and University indicators
Overall staff profile: R1_2
The ANVUR summary table reports the R indicator values calculated for the quality profiles across the institution as a whole. Indicators R1, R2 and R1_2 assess the quality of the institution’s scientific output relative to the national average, set at 1. Values above 1 indicate performance above the national average in terms of quality, while values below 1 point to relatively weaker performance.
For the Polytechnic University of Bari, the three indicators present an overall positive picture. In particular, indicator R1, which relates to the profile of permanent staff, is above the national threshold, indicating that the quality of scientific output exceeds the average for the state university system. Indicator R2, which refers to newly recruited staff or staff who have been promoted, is also above 1, confirming the good quality of the output associated with recruitment and career progression policies.
The value above the national threshold suggests that the contribution of the various staff profiles keeps the Politecnico in a competitive position among state universities. Compared with other state universities, the Politecnico di Bari falls in the upper-middle range of the distribution, with values close to or above the national average. This result indicates solid performance, although there is still room for further improvement relative to the universities at the top of the rankings. Overall, indicators R1, R2 and R1_2 present a positive picture of the quality of the Politecnico’s scientific output. The most significant finding is the University’s ability to maintain values above the national average for both permanent staff and staff affected by recruitment or career advancement processes. Looking ahead, consolidating these results will require strengthening research quality policies, continuously monitoring scientific output, and focusing attention on areas or sectors where deviations from national benchmarks are smaller.
Research training: R3
Indicator R3 concerns the research training profile and refers to the outputs submitted by staff who, according to the Call’s definition, obtained a PhD during the period under consideration. The Politecnico di Bari’s R3 value is 1.02, with IRAS3 x 100 equal to 0.93. This result confirms a positive assessment of the research training profile, indicating that the quality of the outputs associated with this profile is above the average for the reference group. This should be viewed as a favourable sign of the University’s ability to contribute to the training and scientific development of researchers.
Analysis by Scientific Area
Indicators by Scientific Area
Taken together, the indicators present a generally positive picture. The overall R1_2 profile is above average in Areas 1, 2, 8a and 9, broadly in line with the average in Area 8b, and below average in Area 3. Particularly noteworthy is the performance of Area 2, which records high values across all available profiles.
As shown by the data in the following figure, Area 1 records the most positive result in terms of recruitment: the average score for Profile B, at 0.91, is considerably higher than that for Profile A, at 0.72, with a B/A ratio of 1.26. Profile B also receives a slightly higher score in Area 9, with a ratio of 1.05. In Area 2, the two profiles are broadly in line, although permanent staff have a slight advantage; the B/A ratio is 0.98. The picture is less positive in Areas 8a and, particularly, 8b, where the products of Profile B receive lower average scores than those of Profile A. In Area 8b in particular, the ratio falls to 0.85, indicating that the average quality of the products of recruited or promoted staff is significantly lower than that of permanent staff. Overall, recruitment policies appear particularly effective in Areas 1 and 9, broadly neutral in Area 2, and less favourable in Areas 8a and 8b.
Products in classes A+B and assessment classes
The comparison shows that the Polytechnic has a higher share of products in classes A+B than the national figure in Areas 1, 2, 8a and 9. Area 2 shows the largest positive difference, with an A+B share of 94.23%, 16.71 percentage points above the national figure. It is followed by Area 1, with a positive difference of 14.37 points, and Area 9, at +7.57 points.
Analysis of Department results
Expected products and indicators by Department
The following figure summarises, for each Department, the number of expected products and the number of expected products attributed to researchers in mobility. The distribution of expected products varies across Departments. DMMM, DEI and DICATECh are the largest units, while DIF, owing to its specific inter-university and disciplinary configuration, has a lower volume.
Quality and weight of Departments by scientific area
The R quality indicators make it possible to assess the average performance of Departments against the national average for comparable institutions. Reading the three profiles together makes it possible to distinguish between the established quality of permanent staff, the quality of recruitment and promotion policies, and the overall quality of staff. The figures present an overall positive picture. DIF records the highest quality values in all three profiles. DEI and DMMM show solid overall results, with R1_2 values of 1.02 and 1.04, respectively; DICATECh is above the average in the overall profile, with some differences between areas; ARCOD records values close to the average, with an overall profile of 0.99.
Qualitative-quantitative indicator IRD1_2
IRD indicators represent the qualitative-quantitative component of departmental assessment. Unlike R indicators, they measure not only average quality, but combine quality with the institution’s weight in the national system. For this reason, larger Departments may achieve higher IRD values even when their R indicators are similar to those of smaller institutions.
Knowledge valorisation and competitive projects
Knowledge valorisation
Politecnico di Bari submitted 4 case studies for the assessment of knowledge valorisation. The average score is 0.78, with an R4 indicator of 1.14. A value above 1 indicates performance above the national average for the relevant category. The institution ranks 33rd out of 100 institutions overall, placing it in the third quartile and 8th out of 28 in its quartile.
International competitive projects
The R4 value of 1.14 indicates above-average performance in knowledge valorisation. The distribution of case studies is particularly significant: 75% of cases fall into classes A and B, while there are no cases in classes D and E. The result suggests that the University has strengthened its capacity to generate impact through its research, knowledge transfer, public engagement and valorisation activities.
Indicator R5 concerns the financial value of international competitive projects and is one of the most significant profiles for measuring the University’s competitiveness in securing research funding. Politecnico di Bari’s result is particularly positive.
Comparison with Politecnico di Milano and Politecnico di Torino
This chapter examines Politecnico di Bari’s position relative to Italy’s two leading polytechnic universities, Politecnico di Milano and Politecnico di Torino, using the official results of the VQR 2020-2024 exercise published by ANVUR in their respective Institutional Reports.
The comparison aims to assess the results of the Polytechnic University of Bari not only in absolute terms, but also in relation to institutions with a similar academic and scientific mission, despite significant differences in size, critical mass and project volumes.
The comparison examines four main dimensions: the scale of output submission, the qualitative R indicators, the quali-quantitative IRAS indicators, and the two strategic profiles relating to knowledge valorisation and international competitive projects.
Submission scale and quality indicators
The first variable to consider is the scale of output submission. The Polytechnic University of Bari has 1.016 expected outputs, compared with 4.580 at the Polytechnic University of Milan and 3.114 at the Polytechnic University of Turin. This figure highlights the different scale of the three institutions within the national landscape. Bari stands out positively for submitting all expected outputs: 1.016 outputs submitted out of 1.016 expected, with none missing. Milan and Turin also report submission rates close to 100%, at 99,85% and 99,84%, respectively, with very few missing outputs.
The R indicators make it possible to compare the relative quality of assessment profiles against the national average. Values above 1 indicate performance above the average for the relevant category. The Polytechnic University of Bari records above-average R values across all profiles analysed and, for profiles directly related to scientific output, achieves particularly positive results: R1 stands at 1,04, R2 at 1,02 and R1_2 at 1,03. These values are slightly higher than those of the two comparator polytechnic universities, indicating good average scientific output quality relative to the institution’s scale. Milan and Turin show generally positive R values close to the average, with specific strengths in profiles R4 and R5. Turin stands out clearly in knowledge valorisation, while Milan holds a very strong position both in valorisation and in competitive projects.
Overall profile by scientific area
The comparison by scientific area makes it possible to observe internal differences in the quality profiles of scientific output. The R1_2 indicator, which relates to all staff, provides a useful summary of overall performance in the areas shared by the three polytechnic universities.
The Polytechnic University of Bari shows a particularly positive profile in Areas 1 and 2, where it records R1_2 values of 1.08 and 1.14, respectively. In Area 2, Bari’s value is markedly higher than those of Milan and Turin. Areas 8a and 9 have above-average values and are broadly in line with the two comparator polytechnic universities, while Areas 3 and 8b offer the greatest scope for comparative improvement.
Knowledge valorisation and international competitive projects
R4 and R5 represent two strategic areas of the VQR 2020-2024, as they link research quality to the ability to generate external impact and attract competitive international funding.
For R4, the Polytechnic University of Turin achieves the highest result among the three polytechnic universities, with an R4 of 1.32 and a ranking of 2/100. Milan follows with an R4 of 1.22 and a ranking of 8/100. Bari also records a positive result, with an R4 of 1.14, but ranks lower, at 33/100, indicating scope to strengthen its knowledge valorisation.
For R5, which concerns international competitive projects, Bari records the highest quality score among the three polytechnic universities, with an R5 of 1.20 and a ranking of 7/74. Milan and Turin report much higher funding volumes, at 220.4 million and 110.1 million euros, respectively, but their R5 values are lower than Bari’s. This result points to the high average quality of the Polytechnic University of Bari’s competitive project portfolio, despite its much smaller scale.
Comparison between VQR 2015-2019 and VQR 2020-2024
The comparison between the two VQR assessment exercises shows a very significant increase in the scale of the Polytechnic University of Bari: expected research products rise from 758 to 1,016 (+34.0%), while accredited/equivalent researchers increase from 283 to approximately 406 (+43.6%, estimated based on the 2020-2024 rule of 2.5 expected products per researcher). The average quality of research remains broadly stable and above the national average: the R1_2 indicator is 1.03 in both exercises. The most evident improvement concerns knowledge valorisation (R4: from 0.73 to 1.14), while the profile for international competitive projects (R5 = 1.20) is new positive evidence from the 2020-2024 exercise.
Growth in the University’s scale
The first piece of evidence is the growth in the assessment base. In 2015–2019, the Polytechnic University of Bari had 283 accredited researchers and 758 expected/submitted products. In 2020–2024, expected and submitted products rose to 1,016, with no missing products. Applying the VQR 2020–2024 rule (2.5 expected products per researcher), the number of equivalent researchers is approximately 406.4. This figure should not be interpreted as an exact administrative headcount, but as a comparable measure of the size of the assessment base.
| Indicator | VQR 2015-2019 | VQR 2020-2024 | Change |
|---|---|---|---|
|
Accredited/equivalent researchers |
283 |
≈ 406,4 |
+43,6% |
|
Expected products |
758 |
1.016 |
+34,0% |
|
Submitted products |
758 |
1.016 |
+34,0% |
|
Missing products |
0 |
0 |
No issues |
The increase in size is highly significant because it changes the nature of the assessment: the task is no longer to measure the quality of a relatively small, selected base, but to determine whether the university has been able to maintain average quality despite the expansion in the number of researchers and submitted products.
Comparison of the university’s qualitative indicators
The R qualitative indicators provide a more robust basis for comparing periods, as they measure relative quality against the average for the relevant category. From this perspective, the overall picture is largely positive, but it should not be interpreted as a general improvement in research output: the overall R1_2 profile remains exactly 1.03 in both assessment periods.
| Profile | R 2015-2019 | R 2020-2024 | Change |
|---|---|---|---|
|
R1 permanent staff |
1.01 |
1.04 |
+0.03 |
|
R2 recruitment/promotions |
1.04 |
1.02 |
-0.02 |
|
R1_2 total staff |
1.03 |
1.03 |
+0.00 |
|
R3 research training |
1.03 |
1.02 |
-0.01 |
|
R4 knowledge valorisation |
0.73 |
1.14 |
+0.41 |
|
R5 international competitive projects |
n.d. |
1.20 |
New 2020–2024 profile |
Expected products by Area and Department
The analysis by Area shows that the increase in expected products is not evenly distributed. The largest increase is in Area 9, which rises from 387 to 576 expected products (+48.8%) and continues to be the university’s largest area in quantitative terms. Areas 8a, 8b and 2 also grow, while Area 1 remains broadly stable.
The comparison by Department confirms that the growth in the size of the Polytechnic University of Bari was driven mainly by DEI and DMMM, followed by DICATECh and DIF. The comparison between the number of researchers in 2015–2019 and the equivalent researchers in 2020–2024 is indicative, as it is derived from the VQR 2020–2024 expected-products rule, but it allows the impact of this growth on the assessment to be estimated.
Regarding the R1_2 index, ARCOD improves, rising from 0.96 to 0.99, while remaining slightly below the threshold of R = 1.00; DIF increases from 1.10 to 1.14, confirming its position as the Department with the highest value. DEI remains stable at 1.02, while DICATECh and DMMM record a slight decrease, although remaining around or above the reference threshold. The figure for IRD1_2 shows a general increase in the indicator across all Departments. The increase is particularly evident for DEI, which rises from 0.12 to 0.18, and for DMMM, which rises from 0.14 to 0.19. DICATECh, ARCOD and DIF also record positive changes, albeit more modest ones. This result indicates a strengthening of the Departments’ qualitative and quantitative contribution, linked both to the relative quality and to the scale of scientific output.
Comparison by Scientific Area
The analysis by Area shows that the increase in expected research products is not evenly distributed. The most marked increase is in Area 9, which rises from 387 to 576 expected research products (+48,8%) and continues to be the University’s quantitatively dominant area. Area 8a, Area 8b and Area 2 also grow, while Area 1 remains substantially stable. A direct comparison of the R1 indicator shows a marked improvement in Area 2 and Area 8b, a positive confirmation in Area 8a, and substantial stability in Area 1 and Area 9. Area 3 requires specific consideration because, for 2020-2024, no direct comparison is available for the R1 profile in the same form; however, the overall R1_2 profile is 0,92, below the national average. This suggests that an internal review at SSD/GSD and Department level would be beneficial. Overall, values are generally close to or above the R = 1,00 threshold, confirming that the quality of research output is broadly in line with or above the national average. In the 2020-2024 VQR, Areas 1, 2, 8a and 9 are above the reference threshold, with the highest value in Area 2, at 1,14. Compared with the 2015-2019 VQR, Area 2 improved, rising from 1,10 to 1,14, while Area 8b recorded a slight increase from 0,97 to 0,98, although it remains just below the national threshold. Areas 1, 8a and 9 showed limited variation, maintaining values above 1 in the 2020-2024 round. Area 3, by contrast, declined more markedly, falling from 1,08 to 0,92, indicating a relative deterioration against the national benchmark. The comparison reveals a particularly significant increase in the areas where the Politecnico has the largest presence. Areas 8a, 8b and 9 show high values in both VQR rounds and record further improvement in 2020-2024. In particular, Area 9 rises from 2,46 to 3,11, Area 8a from 2,34 to 2,98, and Area 8b from 2,59 to 2,92. These increases confirm the strengthening of the Politecnico’s qualitative and quantitative contribution in the scientific areas central to the University. Areas 1 and 3 show a slight decline in the indicator, while Area 2 improves, rising from 0,67 to 0,81. Overall, the figure highlights that growth in the IRAS1_2 indicator is driven primarily by engineering and architecture, where the Politecnico has the greatest relative weight.
Comparison by Department
The comparison by Department confirms that the growth in the size of the Polytechnic University of Bari was driven primarily by DEI and DMMM, followed by DICATECh and DIF. The comparison between researchers in 2015–2019 and equivalent researchers in 2020–2024 is indicative, as it derives from the expected output rule of the 2020–2024 VQR, but it makes it possible to estimate the impact of growth on the evaluation. Growth in size did not lead to a general deterioration in quality. Despite a marked increase in the number of expected outputs, DEI and DMMM maintained R1_2 values above the average. DIF retained a very strong position in terms of quality. DICATECh received an overall Department evaluation in line with the average, but with internal differences between areas: very positive in Area 8a, weaker in Area 3 and in some components of Area 9. ARCOD showed more limited growth and an overall evaluation close to the average, with specific weaknesses in Area 8b.
Regarding the R1_2 index, ARCOD improves, rising from 0.96 to 0.99, while remaining slightly below the threshold of R = 1.00; DIF increases from 1.10 to 1.14, confirming its position as the Department with the highest value. DEI remains stable at 1.02, while DICATECh and DMMM record a slight decrease, although remaining around or above the reference threshold. The figure for IRD1_2 shows a general increase in the indicator across all Departments. The increase is particularly evident for DEI, which rises from 0.12 to 0.18, and for DMMM, which rises from 0.14 to 0.19. DICATECh, ARCOD and DIF also record positive changes, albeit more modest ones. This result indicates a strengthening of the Departments’ qualitative and quantitative contribution, linked both to the relative quality and to the scale of scientific output.
Overall, the two figures suggest that the improvement in IRD1_2 was also driven to a significant extent by the growth in the size of the Departments. The R1_2 indicator, by contrast, shows that relative quality was broadly maintained, with some specific improvements and slight declines. This confirms that, in the 2020–2024 VQR, the overall strengthening of the Departments stems from a combination of scientific quality and a greater volume of output assessed.
Growth in the number of researchers and counterfactual analysis of the size effect
The central question is: if the Polytechnic University of Bari had maintained the same number of researchers as in 2015–2019, would there have been a genuine improvement in the VQR? The answer must distinguish between average quality and the qualitative-quantitative impact.
In terms of average quality, the most relevant indicator is R1_2, which stands at 1.03 in both 2015–2019 and 2020–2024. Therefore, when considering the overall research profile alone, there is no clear improvement in average quality; rather, it remained above the national average despite the substantial increase in the size of the population assessed.
In qualitative-quantitative terms, however, the increase in researchers and expected outputs has a significant effect. If the Polytechnic had retained its 283 researchers from 2015–2019 in 2020–2024, the rule of 2.5 expected outputs per researcher would have yielded approximately 708 expected outputs, rather than 1,016. With the same average quality, R1_2 = 1.03, the H1_2 weight and therefore the IRAS1_2 indicator would have been significantly lower.
| Scenario | Expected products | H1_2 / proxy | R1_2 | IRAS1_2 / proxy | Interpretation |
|---|---|---|---|---|---|
|
Actual 2015–2019 |
758 |
0,47 |
1,03 |
0,49 |
Historical baseline |
|
Actual 2020–2024 |
1.016 |
0,63 |
1,03 |
0,64 |
Growth in size + stable quality |
|
2020–2024 counterfactual with 283 researchers |
≈ 708 |
0.44 |
1,03 |
0.45 |
Stable quality but lower weight |
The simulation cannot replace a genuine counterfactual analysis based on the outputs of individual researchers, since the ANVUR Report does not provide the necessary microdata. However, it does allow a robust interpretation: if the number of researchers had remained at the 2015–2019 level, the Polytechnic would not have shown a substantial improvement in the overall qualitative profile of its research, since R1_2 would still have been 1.03; instead, its qualitative-quantitative weight would have been lower, because it would have submitted far fewer outputs.
Strengths, critical issues and improvement actions
In the case of the Polytechnic University of Bari, all R indicators are above the reference threshold of 1, while the IRAS indicators are affected by the University’s overall size relative to the national system.
Summary of findings and strengths
| Scope | Evidence | Value | Strategic interpretation |
|---|---|---|---|
|
Submission of research outputs |
Outputs submitted relative to expected outputs |
100% |
Effective internal process and no missing outputs |
|
Quality of Research |
University R indicators |
all > 1 |
Quality above the national average |
|
Overall profile |
R1_2 |
1,03 |
Good overall quality of scientific output |
|
Enhancement |
R4 |
1,14 |
Strengthening the capacity to generate impact |
|
Competitive projects |
R5 |
1,20 |
Area of excellence and competitive advantage |
|
Size-related weight |
IRAS x 100 |
0,50-0,93 |
To be interpreted in light of the University’s size |
| Strength | Evidence | Implication |
|---|---|---|
|
Effectiveness of the VQR process |
1,016 outputs submitted out of 1,016 expected; zero missing outputs. |
Confirms the University’s and Departments’ organizational capacity. |
|
Average quality above the average |
R1=1,04; R2=1,02; R1_2=1,03; R3=1,02. |
Consolidation of scientific quality across the main evaluation profiles. |
|
Competitive project funding |
R5=1,20; 30 projects; over 12,8 million euros in funding. |
An area to be enhanced as a strategic positioning lever. |
|
Knowledge valorisation |
R4=1,14; 75% of case studies in class A or B. |
Positive evidence of impact and transfer of research results. |
|
Excellence by Area and Department |
Very high results in some Areas and departmental divisions. |
Opportunity to disseminate good practices and successful models. |
Areas requiring attention and critical issues
Areas requiring attention should not be viewed as isolated critical issues, but as areas to be monitored as part of the improvement cycle. The 2020-2024 VQR presents a positive overall picture, while also highlighting certain differences across scientific Areas, Departments and quality profiles that call for targeted action.
| Scope | Evidence | Risk | Action required |
|---|---|---|---|
|
Size-related weight |
IRAS indicators lower than expected in relation to quality R. |
Underestimating qualitative results if considered solely in quantitative terms. |
Communicate quality and scale separately. |
|
Differences across Areas |
Area 3 and Area 8b have R1_2 values below or close to the threshold. |
Possible weakening of the overall profile if not monitored. |
Conduct analyses by GSD/SSD and provide targeted support. |
|
Departmental variation |
Performance varies across Departments and Areas of affiliation. |
Difficulty in translating results into shared university-wide objectives. |
Prepare departmental improvement plans. |
|
Knowledge valorisation |
Positive result that needs to be consolidated over time. |
Inconsistency in the quality of future case studies. |
Establish ongoing oversight of case studies. |
|
Competitive projects |
High R5, but dependent on sustained participation in competitive calls. |
Decline in positioning without a stable project pipeline. |
Strengthen scouting, support and international partnerships. |
Analisi SWOT
The SWOT matrix summarizes the main internal and external findings emerging from the analysis of the VQR results, distinguishing between strengths, weaknesses, opportunities, and threats. The matrix is intended as a guide and serves to link the assessment results to strategic planning decisions.
- Strengths
- R indicators all above the national average.
- All expected research outputs submitted.
- Very positive result for R5 competitive projects.
- Growth in the R4 knowledge valorization profile.
- Weaknesses
- The institution’s size limits the IRAS indicators.
- Differences across Areas and Departments.
- Some Areas have an R1_2 value below or close to the threshold.
- More detailed monitoring by GSD/SSD is needed.
- Opportunities
- Strengthening the international project pipeline.
- Enhancing case studies and social impact.
- Sharing good practices among Departments.
- Integrating VQR results into strategic planning.
- Threats
- Growing competition within the national system.
- Potential loss of the advantage if R5 is not consolidated.
- Risk of uneven quality in future outputs.
- IRAS indicators’ dependence on the University’s relative size.
Improvement priorities
Improvement priorities have been organized according to strategic impact and operational feasibility. The most significant actions are those that make it possible to consolidate the positive results already achieved while also addressing areas that are less consistent.
| Priority | Objective | Proposed actions | Time frame | Responsibility |
|---|---|---|---|---|
|
Priority 1 |
Ongoing oversight of research outputs |
Annual monitoring of product quality and periodic simulations. |
Brief |
Research Committee, Departments |
|
Priority 2 |
Strengthening areas below threshold |
Analysis by GSD/SSD, meetings with Departments, targeted support plans. |
Short-Medium |
Departmental Research Delegates, Directors |
|
Priority 3 |
Consolidation of R5 |
Call scouting, support for project design, international partnerships. |
Ongoing |
Research Unit, Delegates |
|
Priority 4 |
Consolidation of R4 |
Case study database, early selection of impact evidence, methodological support. |
Medium |
Third Mission Delegate |
|
Priority 5 |
Promotion of good practices |
Dissemination of successful models identified in Areas and Departments with the best results. |
Medium |
Governing Bodies, Departments |
2026-2029 Roadmap
Improvement actions can be developed through a multi-year roadmap, in line with the University’s next strategic planning cycle and preparation for the subsequent evaluation exercise.
| Stage | Operational objective | Expected output | Progress indicator |
|---|---|---|---|
|
2026 |
Establish a stable monitoring system |
Annual research and VQR dashboard |
Annual update of indicators |
|
2027 |
Target Areas and GSD/SSD with scope for improvement |
Targeted departmental plans |
Reduction in the gap relative to the R=1 threshold |
|
2028 |
Consolidate R4 and R5 |
Pipeline of case studies and competitive projects |
Number of projects and case studies eligible for selection |
|
2029 |
Prepare for the new VQR |
Pre-submission simulation and product review |
Product coverage and expected quality |
Indicators to monitor
To operationalise the improvement plan, it is proposed that a core set of monitoring indicators be associated with the actions, updated at least annually and used in departmental and University planning processes.
| Indicator | Purpose | Frequency | Management use |
|---|---|---|---|
|
Product quality |
Oversee the potential quality of eligible products. |
Annual |
VQR simulations and support measures. |
|
A+B products |
Measure the proportion of products in the highest classes. |
Annual |
Define improvement objectives for each Area. |
|
R1_2 by Area |
Highlight disciplinary differences and trends. |
Annual |
Targeted measures for Areas/GSD/SSD below the threshold. |
|
Competitive projects |
Monitor the ability to attract funding. |
Semiannual |
Call scouting and project-design support. |
|
R4 case studies |
Prepare evidence of impact and valorisation. |
Annual |
Case study database and early selection. |
Conclusions and strategic guidelines
This chapter concludes the Report analysing the results of the Research Quality Assessment (VQR) 2020-2024 at the Polytechnic University of Bari, integrating the quantitative evidence presented in the ANVUR Report with a strategic perspective focused on continuous improvement. Its purpose is not merely to summarise the results achieved, but to translate them into operational guidelines for planning research, knowledge valorisation and competitive project development in the next cycle.
Concluding summary of results
The VQR 2020-2024 presents an overall positive picture for the Polytechnic University of Bari. The University submitted all 1,016 expected research products, with no missing products, thereby achieving 100% coverage. This result provides initial evidence of the effectiveness of the internal process for selecting, validating and submitting research products.
In qualitative terms, all University-level R indicators exceed the reference threshold of 1. In particular, positive values are evident for permanent staff, recruitment policies and the overall staff profile. The profiles relating to knowledge valorisation and international competitive projects are even more significant, representing two areas of the University’s strategic positioning.
| Indicator | Value | Concluding assessment |
|---|---|---|
|
Expected products |
1.016 |
Full coverage of the VQR scope |
|
Submitted products |
1.016 |
100% of expected outputs |
|
Missing products |
0 |
No penalties for outputs not submitted |
|
R1_2 |
1,03 |
Overall quality above average |
|
R4 |
1,14 |
Knowledge valorization above average |
|
R5 |
1,20 |
Strength in competitive international projects |
Overall assessment
A joint reading of the indicators supports a favourable overall assessment. The Polytechnic University of Bari demonstrates an average quality of scientific output above the national average, while its qualitative and quantitative weight is also influenced by the University’s relative size. This distinction is central: the R indicators measure relative quality, whereas the IRAS indicators also factor in institutional size. It follows that the University’s strategy must continue to safeguard product quality while also strengthening critical mass, project development, continuity of monitoring and the capacity to concentrate the best results in areas aligned with the University’s scientific and strategic priorities.
Strategic guidelines for the 2026-2029 period
In light of the evidence gathered, the Report identifies five strategic priorities for the 2026-2029 period. These priorities aim to strengthen the University’s position in the next evaluation exercise and, more generally, to consolidate research quality as a structural component of planning at both Department and University level.
| Strategic line | Main action | Primary responsibility |
|---|---|---|
|
1. Ongoing monitoring |
Develop a University-wide VQR dashboard, updated periodically. |
Research, Departments, Quality Assurance Unit |
|
2. Output quality |
Support the early selection of outputs and analysis of merit classes. |
Research Committee and Departments |
|
3. Strengthening Areas/Departments |
Define targeted actions for Areas and Departments with room for improvement. |
Department Heads and Departmental Research Delegates |
|
4. Knowledge valorization |
Systematically map and document case studies, impacts and beneficiaries. |
Third Mission Delegates and support units |
|
5. Competitive project development |
Strengthen support for international project development and project management. |
Research and International Relations Division |
Monitoring mechanisms
To put the proposed strategic guidelines into practice, the Report recommends adopting a monitoring system based on indicators that are straightforward, updatable and consistent with the VQR assessment profiles. The system should combine quantitative and qualitative indicators with descriptive information useful for documenting the impact of research and knowledge valorisation activities.
| Area of oversight | Indicators/information to monitor | Suggested frequency |
|---|---|---|
|
Product quality |
Share of outputs in top classes, high-ranking publications, bibliometric indicators and peer review |
Semiannual |
|
Scientific disciplines |
Indicators by discipline and comparison with the average threshold |
Annual |
|
Departments |
Departmental VQR reports and improvement objectives |
Annual |
|
Enhancement |
Potential case studies, impacts, beneficiaries, documentary evidence |
Ongoing |
|
Competitive projects |
Projects submitted, admitted, funding, partnerships |
Semiannual |
Conclusion of the report
The results of the 2020-2024 VQR confirm the ability of the Polytechnic University of Bari to achieve research quality above the national average across the main evaluation profiles. The University has a solid scientific foundation, an effective organisational process and areas of particular strength, such as competitive project development and knowledge valorisation.
The challenge of the next cycle will be to turn these results into a stable system of planning, monitoring and improvement. Research quality must be safeguarded not only during the submission phase, but throughout the entire cycle of producing, selecting, valorising and documenting results. From this perspective, the VQR becomes a tool for institutional learning, helping to strengthen the University’s scientific identity and guide the Departments’ development policies.
