Abstract

This paper provides a comprehensive analytical examination of the Global Technological Maturity Index (GTMI), with a focus on assessing the determinants of national digital maturity and structurally modeling GTMI performance. Using rigorous data preparation methods including imputation, feature transformation, and exploratory statistical analysis the study applies multiple regression models (Decision Tree, Random Forest, Gradient Boosting, and MLP) to replicate the GTMI structure and identify its primary drivers. The UAE demonstrates a strong maturity profile, ranking in the  Very High  category globally. Deviation analysis shows that the UAE aligns closely with global leaders across the most structurally divergent indicators. Among the models tested, the Gradient Boosting Regressor achieved the highest structural replication accuracy, confirming its suitability for modeling the GTMI architecture. Crucially, the analysis identifies CGSI as the most influential structural driver of GTMI outcomes globally, followed by PSDI and DCEI, offering evidence-based insights to support policy and strategic interventions aimed at sustaining the UAE s digital leadership.

Publication Date

12-5-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ayman Ibrahim

Comments

This thesis has been embargoed. The full-text will be available on or around 1/5/2027.

Campus

RIT Dubai

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