Analysis of the State of Cooperation Between University and Industry from the Aspect of Financial Support
Notice bibliographique
Résumé
Purpose: Given that university-industry cooperation is associated with the transfer of knowledge and technology, one of the key indicators of a country's development is the effectiveness of collaboration between universities and industry. Due to the significance of this topic, the present article seeks to analyze the state of cooperation between Iranian universities and industry based on the co-publication of articles indexed in the Web of Science database from 2010 to 2022, particularly in terms of financial support. The findings of this research will inform national policymakers and planners about the current state of university-industry cooperation in Iran, enabling them to devise appropriate strategies to enhance this collaboration.Methodology: The research method employed in this study is both descriptive and quantitative, utilizing scientific techniques such as word co-occurrence analysis. The statistical population for this research comprises 2,891 articles. This study encompasses all articles that received financial support, focusing on the collaboration between universities and industry in Iran from 2010 to 2022; therefore, no sampling method was applied. Instead, a census sampling method was utilized. To examine the collaboration between universities and industry through scientific publications (articles), each article must include at least one author affiliated with an industrial organization and one author affiliated with an academic institution, both of whom must have a financial sponsor. Specific labels were used to identify the organizations involved. Data collection was conducted using the Web of Science database, and the data were analyzed and visualized using BibExcel and VOSviewer software.Findings: According to the findings of the current research, the highest number of financial supports for cooperation between industries and universities in Iran was recorded in 2021, with 430 articles published. In contrast, the lowest number was recorded in 2010, with only 89 articles. On the international front, 78 countries have co-published with Iran, with the United States leading in the number of collaborative articles. Other countries that have engaged in significant cooperation with Iran include England, Canada, Australia, France, New Zealand, China, Germany, Italy, and Russia are other countries that have had a large number of cooperation cases in Iran. The data related to financial support institutions showed that the National Iranian Oil Company along its subsidiaries (9.79%); Iran National Petrochemical Company along its subsidiaries (4.74%); Support fund for researchers and technologists (4.08%); Islamic Azad University (3.22%); National Gas Company and its subsidiaries (3.18%); University of Tehran with (2.94%); and Tehran University of Medical Sciences (2.80%) have provided the most financial support. Out of a total of 251 subject areas of Web of Science, 151 areas have received financial support in cooperation between the university and the industry. Based on thematic analysis, chemical engineering fields (10.27%); environmental sciences and materials science (4.28%); energy and fuels (4.21%); and water resources (3.65%), are the most used topics in the articles.Conclusion: The annual growth rate of articles receiving financial support indicates that the 13-year collaboration between universities and industry has experienced significant fluctuations, with some years witnessing a decline. Furthermore, the leading industries providing financial sponsorship include oil, petrochemicals, and gas. Most of these companies are supported by government organizations and are among the most profitable in the country. Consequently, a substantial portion of the investments in this collaboration is derived from the government budget. In terms of the subjects covered in the articles, the results reveal that, while the fields are diverse and extensive, there is a noticeable absence of certain topics, particularly in the humanities and social sciences.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,015 | 0,022 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».