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Al Sereidi, Salem (2025) Predictive AI and AI-Measured KPIs in UAE Public Sector Organisations: The Mediating Role of Implementation Efficiency and Employee Readiness. PhD thesis, University of Gloucestershire. doi:10.46289/XBVD4868
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16462 Al Sereidi, Salem (2025) Predictive AI and AI-Measured KPIs in UAE Public Sector Organisations.pdf - Accepted Version Available under License All Rights Reserved. Download (3MB) | Preview |
Abstract
This study examines how Predictive Artificial Intelligence (AI) Analysis and AImeasured Key Performance Indicators (KPIs) influence Human Resource (HR) effectiveness in the public sector of the United Arab Emirates (UAE), while also considering the mediating roles of Implementation Efficiency and Employee Readiness. A quantitative research design was employed, with data gathered through a structured questionnaire distributed to 379 employees across UAE federal government organisations, resulting in 271 valid responses. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS. Findings demonstrate that Predictive AI Analysis and AImeasured KPIs significantly enhance HR effectiveness, whereas Implementation Efficiency and Employee Readiness did not show notable mediating effects, indicating that AI tools can directly strengthen HR functions. These insights emphasize the importance of AI’s modular design and the structured nature of UAE public organisations in supporting effective adoption. The study offers important implications for both theory and practice by extending understanding of how AI-driven analytics can enhance HR effectiveness within a socio-technical framework while guiding policymakers and HR leaders on strategic AI implementation. However, the study’s focus on the UAE public sector and reliance on self-reported data may limit generalisability. The research recommends that policymakers and HR leaders prioritise predictive analysis and performance measurement systems to accelerate AI integration, while future studies should explore long-term impacts and additional organisational factors across various sectors.
| Item Type: | Thesis (PhD) | ||||||
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| Uncontrolled Keywords: | Predictive AI Analysis; AI-measured KPIs; Human Resource Effectiveness; Implementation Efficiency; Employee Readiness | ||||||
| Subjects: | A General Works > AI Indexes (General) | ||||||
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences | ||||||
| Depositing User: | Kate Rea | ||||||
| Date Deposited: | 04 Aug 2026 12:46 | ||||||
| Last Modified: | 04 Aug 2026 12:46 | ||||||
| URI: | https://eprints.glos.ac.uk/id/eprint/16462 |
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