Abstract
Universities are today required to make an ever-increasing effort to bridge the gap between research, innovation, and marketable solutions. The goal of the paper is to contribute to enhancing the process of exploiting research results of universities through the proposition of an AI-based search process, the experimentation of the process by means of a prototype web portal named “UNIBA TT Skills Portal” that implements it and the evaluation of the perceived user experience of the web portal by using the System Usability Scale questionnaire. The paper illustrates a case study conducted on the University of Bari Aldo Moro, a large university in southern Italy, using technology transfer data in the health domain. The collection of data in a unique “place” aims at fostering technology transfer activities and the creation/acceleration of new innovative enterprises (start-ups and spinoffs) in the Healthcare sector.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Commission, E., for Research, D.-G.: Innovation: towards a policy dialogue and exchange of best practices on knowledge valorisation: report about the results of the survey. Publications Office (2021). https://doi.org/10.2777/457841
Commission, E., for Research, D.G.: Innovation: valorisation policies: making research results work for society : industry-academia collaboration. Publications Office (2021). https://doi.org/10.2777/573275
Demarinis Loiotile, A., et al.: Best practices in knowledge transfer: insights from top universities. Sustainability 14, 15427 (2022). https://doi.org/10.3390/SU142215427
Commission, E., for Research, D.-G.: Innovation: science, research and innovation performance of the EU 2022: building a sustainable future in uncertain times. Publications Office of the European Union (2022). https://doi.org/10.2777/78826
Lynn, G.: Problems and practicalities of technology transfer: a survey of the literature. https://www.inderscienceonline.com/doi/abs/10.1504/IJTM.1988.025991. Accessed 21 Nov 2022. https://doi.org/10.1504/IJTM.1988.025991PDF
Khan, J., Haleem, A., Husain, Z.: Barriers to technology transfer: a total interpretative structural model approach. Int. J. Manuf. Technol. Manage. 31, 511–536 (2017). https://doi.org/10.1504/IJMTM.2017.089075
Nguyen, N.T.D., Aoyama, A.: Achieving efficient technology transfer through a specific corporate culture facilitated by management practices. J. High Technol. Manage. Res. 25, 108–122 (2014). https://doi.org/10.1016/J.HITECH.2014.07.001
Schuh, G., Aghassi, S., Valdez, A.C.: Supporting technology transfer via web-based platforms. In: 2013 Proceedings of PICMET 2013: Technology Management in the IT-Driven Services, pp. 858–866 (2013)
Novikova, I., Stepanova, A., Zhylinska, O., Bediukh, O.: Knowledge and technology transfer networking platforms in modern research universities. Innov. Mark. 16, 57–65 (2020). https://doi.org/10.21511/IM.16(1).2020.06
Shao, J., Yu, Z., Huang, T.: Innovation service platform of small and medium-sized microenterprises based on social perception and neural network algorithm. Comput. Intell. Neurosci. 2022, 1–9 (2022). https://doi.org/10.1155/2022/8700833
Dentamaro, V., Giglio, P., Impedovo, D., Moretti, L., Pirlo, G.: AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath. Pattern Recognit. 127, 108656 (2022). https://doi.org/10.1016/J.PATCOG.2022.108656
Impedovo, D., Longo, A., Palmisano, T., Sarcinella, L., Veneto, D.: An investigation on voice mimicry attacks to a speaker recognition system. In: Demetrescu, C., Mei, A. (eds.) Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022). CEUR, Rome (2022)
Carrera, F., Dentamaro, V., Galantucci, S., Iannacone, A., Impedovo, D., Pirlo, G.: Combining unsupervised approaches for near real-time network traffic anomaly detection. Appl. Sci. 12, 1759 (2022). https://doi.org/10.3390/APP12031759
Sætra, H.S.: AI for the Sustainable Development Goals. CRC Press, New York (2022)
Aggarwal, C.C.: Content-based recommender systems. In: Recommender Systems, pp. 139–166 (2016). https://doi.org/10.1007/978-3-319-29659-3_4
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 1, pp. 4171–4186 (2018). https://doi.org/10.48550/arxiv.1810.04805
SUS: A “Quick and Dirty” Usability Scale. Usability Evaluation in Industry, pp. 207–212 (1996). https://doi.org/10.1201/9781498710411-35
Brooke, J.: SUS: a retrospective. J. Usability Stud. 8, 29–40 (2013). https://doi.org/10.5555/2817912.2817913
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Demarinis Loiotile, A., Veneto, D., Agrimi, A., Semeraro, G., Amoroso, N. (2024). An AI-Based Approach for the Improvement of University Technology Transfer Processes in Healthcare. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 802. Springer, Cham. https://doi.org/10.1007/978-3-031-45651-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-45651-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45650-3
Online ISBN: 978-3-031-45651-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)