Convergence analysis of artificial intelligence research capacity

Are the less developed catching up with the developed ones

Bibliographic Data

ID21594450
AuthorsSaima Javed (0000-0001-7969-5594, College of Teacher Education Zhejiang Normal University Jinhua China), Yu Rong (0000-0001-7387-302X, College of Teacher Education Zhejiang Normal University Jinhua China, corresponding author), Babar Nawaz Abbasi (0000-0002-2298-339X, School of Education Zhengzhou University Zhengzhou China)
Year2024
Volume36
Issue4
Pages2172-2192
Publication date2024-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of International Development (JOURNAL)
Journal identifiersISSN: 0954-1748 • E-ISSN: 1099-1328
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jid.3901
OpenAlexW4394013227
LanguageEN
Citations received3
References cited48

This study examines whether less developed countries are catching up with developed ones using the log t convergence technique (LCT) and the dynamic spatial ordered probit (DSOP) model. The findings revealed that first, there is no overall convergence in AI research capacity. Second, club clustering analysis showed convergence in four of the five groups of countries on AI research output and in three of the four groups on AI patent grants. Third, the countries are experiencing a slow divergence process in AI research capacity. Fourth, the region, income group and cluster of the countries are influencing the convergence process

Econometrics · Economic growth · Economics · Statistics · Computer Science · Mathematics · Technology Assessment and Management · Artificial Intelligence

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