Optimizing Business Performance Through Effective Accounting Information Systems: The Role of System Competence and Information Quality
Bibliographic record
Abstract
In today’s competitive business environment, accounting information systems (AISs) are crucial for organizations seeking to enhance decision making and improve performance. This study investigates the interplay between AIS competence, information quality, and system effectiveness and their collective impact on business performance within Saudi Arabian companies. Using a quantitative approach, data were collected from 123 manufacturing and service firms through a structured questionnaire. Employing structural equation modeling (SEM), this study elucidates the direct and mediating effects of AIS attributes on organizational outcomes. The findings indicate that system competence has a direct positive effect on both information quality and AIS effectiveness. Information quality, in turn, positively influences AIS effectiveness and business performance. Additionally, AIS effectiveness was found to have a direct positive impact on organizational performance. This study provides valuable insights for managers seeking to optimize AIS investments and emphasizes the importance of integrating high-quality information systems to achieve strategic and operational goals. The results offer a detailed understanding of AIS dynamics, particularly within the context of emerging markets, and contribute to the broader discourse on technology-driven business performance enhancement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".