The role of big data in improving the balanced scorecard in Jordanian commercial banks: A field study
Bibliographic record
Abstract
The study aimed to explore The Role of Big Data in Improving the Balanced Scorecard in Jordanian Commercial Banks. The descriptive approach was employed, and the quantitative method was adopted to achieve the study’s objectives and test its hypotheses. A questionnaire tool was developed, consisting of four sections for big data and four sections for the balanced scorecard, comprising a total of 48 items. The validity and reliability of the tool were verified. The questionnaire was allocated to a sample of 400 employees of the study community which is the Jordanian commercial banks. The study's findings revealed that big data has a statistically significant impact on enhancing the balanced scorecard in Jordanian commercial banks. Dimensions of big data, such as "variety" and "veracity," had a positive and direct effect on improving all aspects of the balanced scorecard, including financial performance, customer service, learning, and growth. On the other hand, the impact of "volume" and "velocity" was limited or statistically insignificant in some aspects. According to multiple regression analyses, big data contributes to explaining 82% of the improvements observed in the balanced scorecard, highlighting the importance of investing in big data to enhance operational and financial performance.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".