Navigating digital transformation in accounting system: Challenges and opportunities
Bibliographic record
Abstract
The primary objective of this study was to assess the challenges associated with the digital transformation process in the accounting systems of companies listed on the Khartoum Stock Exchange, focusing on the perspectives of senior management, financial management, and technology management. These departments were considered integral due to their profound understanding of digital transformation processes, requirements, and existing as well as anticipated challenges. Employing a descriptive-analytical approach, the study utilized a questionnaire to gather data from 315 individuals within the study population. The results revealed a favorable inclination among Khartoum Stock Exchange-listed companies towards digital transformation, manifested through a notable automation of accounting procedures, the cultivation of a digital culture, and the adoption of strategic approaches for digital transformation. However, the study identified several challenges, including insufficient technological infrastructure at the national level, the absence of a clear national strategic plan for digital transformation, and limited financial resources allocated to digital transformation in accounting systems. The study also proposed a set of solutions to address these challenges, categorized into national and corporate levels.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".