Desarrollo de tecnologías basadas en Inteligencia Artificial para el análisis y la toma de decisiones en Parques Científicos y Tecnológicos

• Autores: Olga Francés Hernández
• Directores de la Tesis: Manuel Palomar Sanz (dir. tes.) , Yoan Gutiérrez Vázquez (dir. tes.)
• Lectura: En la Universitat d'Alacant / Universidad de Alicante ( España ) en 2024
• Idioma: español
• Número de páginas: 208
• Tribunal Calificador de la Tesis: Ruslan Mitkov (presid.) , Paloma Moreda Pozo (secret.) , Marcelo Estayno (voc.)
• Enlaces
  • Tesis en acceso abierto en: RUA
• Resumen

  • Science and Technology Parks (STPs) are complex organisations that play a crucial role in fostering technology transfer, innovation, economic growth and business competitiveness. However, the different types, objectives and denominations has led to confusion on how to classify them as well as monitor their impact on the region in which they operate, causing an inaccurate perception of their value and scope. This could hinder their development arising from the lack of effective support policies as well as poor decision-making that impedes the proper running of the STP largely because it is overly influenced by intuition, personal experience, political conditioning factors, or benchmarking of other STPs. In the case of benchmarking, given that the situation and objectives of STPs are specific, it would be more effective to base the development strategy and decision-making on objective data that is relevant to each case. Considering all this, it is useful to do a deep dive into these innovation ecosystems that explores the types of existing STPs and the factors that appear to affect their heterogeneity and performance. A main contribution of this doctoral thesis is to increase the theoretical knowledge of the field with a particular emphasis on Science Parks (SPs), where universities play a central and crucial role. This has been carried out by accomplishing the following tasks: i) a systematic literature review that provides a solid point of reference for future research; ii) a glossary of concepts extensively used in the field of STPs and innovation that entailed a clarification of the terminology; iii) the definition of different types of STPs with different approaches; and iv) the distinctive characteristics and a structural analysis that enables a qualitative detection of critical factors related to performance. Additionally, this doctoral thesis makes a number of practical contributions in the area of STPs that provide a basis for the use of data and artificial intelligence tools to enhance their growth and development. Specifically, the thesis develops a methodology for the construction of standardised datasets for STPs from heterogeneous sources and formats. The importance of having quality data to analyse trends in these innovation ecosystems is highlighted. Furthermore, a multidimensional analysis involving statistical techniques and machine learning tools is proposed and integrated into a dashboard that facilitates the visualisation of data and tools for further exploration. The study enables analysis and evaluation of as well as prediction and prescription for STPs. Both the methodology of the dataset construction for STPs and the descriptive and machine learning analysis have been applied to the use case of Spanish STPs with data from 49 STPs, spanning 16 available years and comprising 21 essential characteristics. The dataset is easily updatable with new features and/or instances and together with the proposed analysis can be replicated for other geographical regions. As an international use case, the STPs ecosystem in Argentina, where access to data has been more limited, has been analysed. A comparative study of STPs in Spain and Argentina has also been developed, revealing a less mature ecosystem, with fewer initiatives and a more asymmetric geographical distribution. In both the Spanish and Argentinean use cases, the existence of 3 different types of STPs is validated: • Science Parks (SPs) with a greater orientation towards R&D and where universities play a key role. • Technology Parks (TPs) with a greater impact in terms of turnover and number of companies and promoted by other actors, mainly governmental. • Hybrid Parks (HPs) with intermediate characteristics to the previous ones. After the machine learning analysis, it is possible to classify the different STPs more precisely within these groups, as well as to establish the typical or prototype characteristics of each group. The continuous collection of information and the processing of quality data is crucial, not only for monitoring and evaluating the performance of STPs, but also for the construction of quality datasets that allow the application of advanced tools for prediction and prescription applied to the field of Science and Technology Parks. Despite the great potential impact that STPs have in their geographical area, there are no quality datasets or advanced analysis methodologies. This thesis lays a solid foundation for progress in this regard. Future work will enhance both prediction and prescription in the field of STPs using machine learning tools. It is also possible to incorporate other features and address other geographical areas, which would allow for the development of a comprehensive STP observatory. Generally, it is important to foster a culture of data and performance metrics in innovation ecosystems. Collecting as much information as possible will facilitate the processing and use of data for analytical tools, including AI-based systems. All this contributes to evidence-based decision-making and more efficient allocation of resources to better manage innovation ecosystems. In conclusion, data-driven decision-making for STPs represents a significant advance in the management and development of these innovation spaces. The methodologies developed in this work provide a valuable tool to address complex evaluation and decision-making in these dynamic and changing environments. In an increasingly data-driven world, this research provides a solid foundation for maximising the potential of STPs to promote scientific and technological advancement.