Implementation of Business Intelligence to Increase the Effectiveness of Decision Making Process of Managers in Companies Providing Payment Services
Bibliographic record
Abstract
The most important purpose of this research is implementation of intelligence to increase the effectiveness of decision-making process of managers in service providing companies (Case Study: Saman Kish Electronic Payment Company). The importance and necessity of such research becomes clear according to criteria such as Development of knowledge about techniques to facilitate decision-making in intelligence, strategic level of intelligence, tactical level of intelligence, operational level of intelligence and quality of intelligence implementation and absence of a system to provide advice for manager to for decision-making on matters related to intelligence implementation. In the end, the sample size for this research consists of 30 available experts willing to cooperate who were selected using a combination of Purposive non-probability (judgment) sampling and snowball sampling. Data were collected using first set of measuring tools (tools to measure the effect of variables in order to increase the effectiveness of decision-making process of managers) and the second set of measuring tools (tools to validate “support system for decision based on the principle of intelligence implementation in order to increase the effectiveness of decision-making process of managers”). This fact that analyzing business intelligence techniques to facilitate decision making can make decision-making process of managers in Saman Kish Electronic Payment Company comprehensively effective is among the most important results of this study. In the end, it was determined that the final difference between the outputs of support system for decision in this research which are BI+FDSS and average expert opinions has not been significant and has been calculated to be 0.06475 which means there is no significant relation between expert opinions and outputs of BI+FDSS System. ”Techniques to facilitate decision-making in intelligence”, ”strategic level of intelligence”, ”tactical level of intelligence”, ”operational level of intelligence” and quality of intelligence implementation” and “Companies providing payment services”
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".