Key Determinants of Artificial Intelligence Adoption and the Perception of Decision Makers Towards Enablement of Socio-Economic Sustainability Goals: Case UAE Smart Sustainable Cities

dc.contributor.advisorProfessor Edward Ochieng
dc.contributor.advisorProfessor Khalid Al Marri
dc.contributor.authorKHANSAHEB, KHULOOD SHEBIB HUSSAIN ABDULRAHMAN
dc.date.accessioned2024-01-16T11:16:35Z
dc.date.available2024-01-16T11:16:35Z
dc.date.issued2023-06
dc.description.abstractThe purpose of this research was to examine the case of the UAE smart cities with specific focus on the key determinants of AI adoption and the perception of decision makers regarding the usage of AI in enabling the socio-economic sustainability development goals of the UAE. The nexus between smart cities and sustainability was examined in detail with the underlying role of AI in smart cities functionalities being analyzed to understand how it fosters smart cities’ role in sustainability. The research adopted a quantitative methodological framework to rigorously examine the subject matter above. A survey was distributed to 500 decision makers in AI, smart cities, and sustainable development programmes across the UAE (mainly, Abu Dhabi, Dubai, and Sharjah). The research developed and tested six hypotheses using structural equation modelling (SEM) to test the strength of relationships between variables of AI adoption and usage, smart cities, and socio-economic sustainable development goals. The findings of the study revealed that, the mediating role of action fields of smart cities when affected by AI adoption led to the attainment of socio-economic sustainable development goals in the UAE at a strong regression coefficient of 0.789. The findings further enlightened that environmental factors of AI adoption including infrastructure readiness, stakeholder readiness, and regulatory environment strongly impacted the action fields of smart cities, overly leading to better attainment of socio-economic sustainable development goals. Other key findings of the study included but not limited to, organizational factors impacting AI adoption and usage were an impediment to the effective application of AI in UAE smart cities to realize the attainment of socio-economic sustainable development goals; technological factors of AI adoption and usage did not positively impact UAE smart cities to lead to the attainment socio-economic sustainable development goals. The research proposed a strategic conceptual model for the adoption, usage, and implementation of AI in UAE smart cities to facilitate socio-economic sustainability through achievement of socio-economic sustainable development goals. The research novelty lies in bridging the paucity in literature regarding the use of AI in smart cities by establishing the connection between AI, smart cities, and socio-economic sustainable development goals and developing a strategic conceptual model for the implementation of AI in smart cities for successful socio-economic sustainable development. Theoretically, the findings of the study regarding the relationships between AI, Smart cities and the Socio-economic development goals of the UAE, builds literature on the subject matter to a more integrated understanding of the functionalities of the disparate constructs. Practically, the conceptual model developed for the implementation of AI policies and programmes for UAE smart cities development will foster the attainment of the country’s socio-economic sustainable development goals through integration of these interdependent constructs.
dc.identifier.other20182272
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/2483
dc.language.isoen
dc.publisherThe British University in Dubai (BUiD)
dc.subjectUAE smart cities, artificial intelligence, sustainable development goals, diffusion of innovation, technological innovation. United Arab Emirates (UAE)
dc.titleKey Determinants of Artificial Intelligence Adoption and the Perception of Decision Makers Towards Enablement of Socio-Economic Sustainability Goals: Case UAE Smart Sustainable Cities
dc.typeThesis
Files