Data analytics for national innovation research: a systematic literature review

Siham Hamidi and Abdelaziz Berrado

International Journal of Technological Learning, Innovation and Development, 2024, vol. 15, issue 3, 270-306

Abstract: The aim of this systematic literature review (SLR) is two-fold: first, to examine the use of data analytics techniques in studying national innovation by providing a comprehensive analysis of this research field. Second, to inform researchers and policymakers about the state of the art and to highlight topics that merit further attention. This SLR is based on Scopus and Web of Science publications appearing between 1984 and 2023. Only 149 articles met the selection criteria, showing that this is an emerging field that requires more investigation. In addition, the SLR identified several sources of data in this field that present, if combined with machine learning techniques, a promising opening for researchers. This SLR also shows a geographical disparity in this research field. The literature mainly investigates the EU and OECD countries. In contrast, low-income countries, small economies, and African countries have received very little attention.

Keywords: data analytics techniques; national innovation determinants; national innovation capacity; national innovation performance; innovation index; machine learning in innovation. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijtlid:v:15:y:2024:i:3:p:270-306

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