Leveraging clustering techniques to drive sustainable economic innovation in the India–Gulf interchange

Bibliographic Data

ID22109954
AuthorsIbrahim Rashid Al Shamsi (0000-0003-3741-6942, Twitter (United States)), Boumedyen Shannaq (0000-0001-5867-3986, Al-Buraimi University College, corresponding author)
Year2024
Volume10
Issue1
Publication date2024-12-31
Open AccessYes
TypeARTICLE
VenueCogent Social Sciences (JOURNAL)
Journal identifiersISSN: 2331-1886 • E-ISSN: 2331-1886
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/23311886.2024.2341483
OpenAlexW4396616521
LanguageEN
Citations received3
References cited25

The collaboration between India and the Gulf regions presents a promising opportunity for sustainable economic innovation amidst global challenges. However, there is a notable research gap in understanding how machine learning techniques, particularly clustering, can drive such innovation effectively in this context. Existing literature lacks tailored models for the India–Gulf interchange’s specific needs. This study aims to fill this gap by investigating the application of machine learning clustering models to identify factors and opportunities for sustainable economic innovation. Specifically, it seeks to leverage these techniques to foster strategic partnerships and address environmental, social, and governance factors. Through SWOT analysis and clustering, the study identifies integration elements and proposes a forward-looking Paradigm for Future Development. The findings advocate for strategic collaborations and offer a model methodology for sustainable development, with broader policy implications. The study reveals that Current Saudi Arabia’s progress is linked to strategies akin to India’s, suggesting India as a model for Gulf countries

Business · Cluster analysis · Economics · Economy · Natural resource economics · Computer Science · Economic and Technological Innovation · Global Trade and Competitiveness · Global trade and economics

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