Exploring the Determinants of Artificial Intelligence Adoption Intention in the SMEs of United Arab Emirates

Bibliographic Data

ID22006509
AuthorsGeorge Thomas (0000-0002-7192-7554, Prince Sultan University, corresponding author), Norah Albishri (0000-0001-9840-7105, Princess Nourah bint Abdulrahman University), Norah Ali Albishri (Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia), Jamid Ul Islam (0000-0002-2545-5162, Canadian University of Dubai), Muhammad Tanveer (0000-0003-4560-6439, Imam Mohammad ibn Saud Islamic University)
Year2025
Volume15
Issue4
Publication date2025-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSAGE Open (JOURNAL)
Journal identifiersISSN: 2158-2440 • E-ISSN: 2158-2440
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/21582440251395561
OpenAlexW4416105560
LanguageEN
Citations received2
References cited69

The global business landscape is currently experiencing a transformative shift propelled by technological advancements, particularly Artificial Intelligence, which is reshaping operations and enhancing organizational capabilities. This paper investigates the adoption intention of AI within the Small and Medium Enterprises in the United Arab Emirates, a country recognized for its embrace of technological innovation. Addressing a key research gap, the study emphasizes the limited exploration of AI adoption determinants within SMEs in emerging economies. Built on the application of the technology–organization–environment framework and integrating the Technology Acceptance Model, the research identifies factors influencing AI adoption intention in UAE hospitality SMEs. A quantitative survey of 315 respondents was conducted, and data were analyzed using structural equation modeling (SEM) in SmartPLS. The findings reveal significant relationships between perceived competitive advantage, perceived usefulness, perceived top management support, perceived employee capability, perceived competitive pressure, perceived government regulations and AI adoption intention, affirming the validity of the proposed conceptual model. The results further show that organizational (top management support) and environmental factors (competitive pressure, government regulation) exert stronger influence than technological perceptions, highlighting the role of leadership and institutional pressures in shaping adoption. The study contributes theoretically by contextualizing TOE–TAM integration and practically by offering actionable insights for SME leaders, policymakers, and stakeholders to drive AI adoption

Competitive advantage · Conceptual model · Hospitality · Hospitality industry · Structural equation modeling · Technology Acceptance Model · Transformative learning · AI in Service Interactions · Organizational and Employee Performance · Technology Adoption and User Behaviour

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