Analyzing the effects of data mining techniques on management decision making and information exchange in the industrial sector: the role of cooperation as a moderating factor in Saudi Arabia
Bibliographic record
Abstract
This research explores the influence of data mining methods on the managerial decision-making in Saudi Arabia's industrial sector, emphasizing the moderating function of cooperation. A total of 500 questionnaires were distributed to information technology managers, with 265 responses selected for data analysis. Smart PLS 4 software was used for the data analysis, and statistical measures were used to analyze the correlations between variables. The findings show that data mining approaches have a substantial positive relationship with improving decision-making and information exchange within external and internal contexts. The study also demonstrates that cooperation plays an important moderating role in these interactions, emphasizing the significance of building a cooperative atmosphere to improve the influence of data mining methods on decision-making and information sharing. The study's conclusions have practical relevance for organizations in the industrial sector. Organizations may improve their decision-making processes and information sharing by adopting data mining tools and boosting collaboration, enhancing performance and competitiveness.
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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 | 0.002 | 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 | 0.000 | 0.003 |
| Open science | 0.002 | 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 itClassification
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".