Multidimensional Data Analysis for Enhancing In-Depth Knowledge on the Characteristics of Science and Technology Parks

The role played by science and technology parks (STPs) in technology transfer, industrial innovation, and economic growth is examined in this paper. The accurate monitoring of their evolution and impact is hindered by the lack of uniformity in STP models or goals, and the scarcity of high-quality da...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 23; p. 12595
Main Authors Francés, Olga, Abreu-Salas, José, Fernández, Javi, Gutiérrez, Yoan, Palomar, Manuel
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2023
Subjects
Case studies
classification
core features
Data analysis
dataset
Datasets
Decision making
Economic growth
Information management
Machine learning
multidimensional data analysis
Objectives
Science
science and technology parks
Technology transfer
Spain
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Summary:The role played by science and technology parks (STPs) in technology transfer, industrial innovation, and economic growth is examined in this paper. The accurate monitoring of their evolution and impact is hindered by the lack of uniformity in STP models or goals, and the scarcity of high-quality datasets. This work uses existing terminologies, definitions, and core features of STPs to conduct a multidimensional data analysis that explores and evaluates the 21 core features which describe the key internal factors of an STP. The core features are gathered from a reliable and updatable dataset of Spanish STPs. The methodological framework can be replicated for other STP contexts and is based on descriptive techniques and machine-learning tools. The results of the study provide an overview of the general situation of STPs in Spain, validate the existence and characteristics of three types of STPs, and identify the typical features of STPs. Moreover, the prototype STP can be used as a benchmark so that other STPs can identify the features that need to be improved. Finally, this work makes it possible to carry out classifications of STPs, in addition to prediction and decision making for innovation ecosystems.
ISSN:2076-3417
2076-3417
DOI:10.3390/app132312595