Measuring cloud information systems’ effect on financial information quality using the information system success model: Evidence from Saudi Arabia
Bibliographic record
Abstract
This paper explores the effects of cloud information systems on the quality of financial reporting within Saudi Arabian enterprises, utilizing the DeLone and McLean Information Systems Success Model as its theoretical foundation. The central inquiry of this research assesses the influence of cloud information systems on the quality of financial data. It hypothesizes a beneficial correlation between these elements. The study involved 203 auditors from Saudi accounting firms, and the findings underscored a significant positive influence of the model's six dimensions on financial information quality. The utilization of cloud information systems appears to bolster financial reporting quality in organizations. This research enriches existing literature by empirically validating the positive effects of cloud information systems on financial data quality in Saudi contexts. Additionally, it underscores the practical utility of the IS Success Model in evaluating the efficacy of cloud information systems in enhancing financial information quality. These insights are particularly valuable for managers and policymakers contemplating the adoption of cloud-based systems to augment their financial reporting processes.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".