Utilizing business intelligence and digital transformation and leadership to enhance employee job satisfaction and business added value in greater Amman municipality
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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 goal of this study was to find out how business intelligence systems, AI, and digital leadership affect how satisfied employees are with their jobs and how much value they add to companies in the Greater Amman Municipality. After the study samples were taken and looked at, a total of 246 samples were approved to be used in the PLS software-based analysis. The results of this study showed that putting in place business intelligence tools, artificial intelligence, and digital leadership all made employees happier with their jobs and gave businesses more value. The research showed that there are four key parts to digital leadership: commander, communicator, collaborator, and co-creator. The main parts of business intelligence are Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. Findings show that digital transformation is made up of three key parts: changing processes, developing business models, and changing domains. The results also show that an employee's level of job satisfaction, which includes things like business success, work commitment, and job thinking, is linked to how much value they add to the company. Intriguingly, the current results go against those of earlier studies, which said that the variables of interest have no effect on how happy employees are with their jobs or how much value companies add for their customers. When the results of this study are looked at as a whole, they say that businesses should start doing things that make employees happier at work and increase the value of the business. The current study is innovative because it focuses on the most important parts of business intelligence, artificial intelligence, and digital leadership in order to improve employee satisfaction at work and the quality of business learning with added value in Greater Amman Municipality.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| 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