Social and technical enablers of AI integration: Implications for innovative workplace behavior in the UAE
Bibliographic record
Abstract
This study investigates the social and technical factors influencing the adoption of Artificial Intelligence (AI) technologies within organizations and examines how these factors impact innovative workplace behaviour. Drawing on a combination of organizational culture, leader humility, work relationships, and AI-related technical skills, the study presents a comprehensive framework for understanding the integration of AI. Data were collected through an online survey from employees in the government, semi-government, banking, healthcare, and private sectors in the United Arab Emirates (UAE). 441 professional respondents from multiple sectors. The study’s findings reveal that social factors, such as organizational culture and leader humility, and technical factors, including managerial and employee AI skills, significantly contribute to the successful adoption and integration of AI. This study contributes to the literature by integrating both social and technical dimensions into a unified model. In addition, the study highlighted that AI adoption succeeds when technological readiness is matched with strong workplace relationships, supportive culture, and leader humility creating the conditions for sustained innovation. Finally, the findings provide practical implications for managers aiming to promote a supportive environment for AI adoption and innovation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".