Quantitative exploration of digital facility management adoption among United Arab Emirates facility managers
Bibliographic record
Abstract
In the dynamic realm of facility management in the UAE, this study investigates the uptake of digital facility management systems among managers operating locally. The study aims to uncover the influential factors affecting technology acceptance within this sector. Its primary objective is to identify and understand the determinants that impact the adoption of these systems, utilizing a structured framework to analyze influential factors and their implications. The initial phase involves a systematic review to reveal trends in integrating digital technologies, focusing specifically on digital twin technology. Subsequently, quantitative surveys are conducted with 407 facility managers, guided by the UTAUT framework, employing statistical analyses to pinpoint key factors. Notably, Effort Expectancy and Performance Expectancy emerge as significant influencers, particularly influenced by the managerial level. This study provides detailed insights into the nuanced factors that drive acceptance, emphasizing the crucial role of constructs like Performance Expectancy and Effort Expectancy moderated by managerial level, shaping Behavioral Intention and Use Behavior. These findings offer practical implications for devising strategies to encourage the adoption of Digital Facility Management Systems in the UAE, laying the groundwork for future research and industry advancements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".