The impact of digital transformation on accounting information systems: Evidence from the aviation industry of the United Arab Emirates
Bibliographic record
Abstract
In 2023, the United Arab Emirates (UAE) Digital Government Strategy 2025 required government entities and companies to participate in transforming the country into a smart nation. The first phase named “digital transformation” focuses on digitizing all operations. As such, accounting information systems (AISs)—which collect, organize, and report financial data—must evolve in alignment with this vision. This study explores how professionals in the UAE’s government-owned aviation industry view AIS adaptation to meet national digital transformation goals. Data were gathered through semi-structured interviews with 17 AIS experts, each with at least two years of experience in both AIS and digital transitions. The responses were then open-coded into themes centered around the objectives, benefits, challenges, and organizational impacts of AIS transformation. The findings reveal that a variety of new technologies are being used. For example, blockchain is being applied to supply chains to enhance partner traceability. AI is being used to analyze large data sets, automate repetitive tasks, and integrate non-financial data, such as for fair value assessments, to support IFRS compliance. AI is also helping to improve GDPR compliance by identifying data vulnerabilities and triggering automated safeguards. Cloud computing is also being adopted to reduce idle capacity and offer scalable flexibility. Nevertheless, some challenges were noted, such as limited employee competence and resistance to adopting new systems.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".