The impact of business intelligence tools on sustaining financial report quality in Jordanian commercial banks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to showcase the influence of Business Intelligence (BI) tools, such as Online Analytical Processing (OLAP), Extract, Transform, Load (ETL) processes, Data Mining (DM), Relational Database Management Systems (RDBMS), and Document Management Systems (DMS), on maintaining the quality of financial reports in Jordanian commercial banks listed on the Amman Stock Exchange. Two approaches were employed to achieve the research objectives: a descriptive-analytical approach involving the development of a questionnaire to gather primary data on the independent variables associated with BI tools (OLAP, ETL, DM, RDBMS, DMS), and an applied approach to evaluate the dependent variable represented by the sustainability of financial report quality, utilizing the financial statements of commercial banks listed on the Amman Stock Exchange from (2016 to 2021). Data analysis and hypothesis testing were conducted using statistical software (SPSS) through multiple regression analysis. The results of the statistical data analysis and input from the research community indicated that the sustainability of financial report quality, as a valuable asset for banks, relies on the utilization of Business Intelligence tools. IT professionals in commercial banks perceive a statistically significant impact of BI tools on maintaining the quality of financial reports. Consequently, the management of commercial banks listed on the Amman Stock Exchange should prioritize the effective utilization of Business Intelligence tools, as their potential lies in aiding the accounting process to achieve its objectives, which ultimately contribute to the sustainability of financial reports. By employing these tools accurately and efficiently in accounting practices, all stages of the accounting process can be influenced, enabling the transformation of available data into information that benefits decision-makers both internally and externally within the banking environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it