The Influence of the Digital Accounting System on the Quality of Sustainable Decision-Making
Bibliographic record
Abstract
This study assesses De Lone and McLean’s Information System (D&M IS) Success Model concerning DAS throughout small and medium enterprises (SMEs) in Saudi Arabia (SA). The present work mainly sought to evaluate the impact of information quality (IQ), system quality (SysQ), service quality (SrvQ) serving, system utilization, and user satisfaction (Usat) on the usage of the Digital Accounting System (DAS), which is posited to ultimately improve the quality of sustainable decision-making. The research utilized a quantitative methodology, employing a self-administered questionnaire to collect data from 328 decision-makers who are knowledgeable about actual DAS usage by SMEs in SA. Subsequent to gathering data, validation was conducted via Structural Equation Modeling (SEM) by utilizing smart-PLS software. The findings indicate that SysQ and IQ significantly influenced system utilization, although SrvQ did not. DAS was determined to significantly influence user happiness. Moreover, system utilization and user satisfaction positively influenced DAS, thereby affecting the sustainability of decision-making and reflecting the overall benefits of DAS. This work enhances the current IS literature by identifying the characteristics that affect the net advantages of DAS, with the suggested model evaluated in SMEs in SA utilizing DAS. This study serves as a reference to elucidate the significance of DAS and offers consequences, limitations, and prospects for further research.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".