Business Intelligence Application Model in Hedge Funds Supporting Knowledge-Based Companies
Bibliographic record
Abstract
Nowadays, organizations having a more profound understanding as well as evaluation of their area of activities and acquiring more competitive advantages will be successful in the competitive environment. Organizations have excelled over their rivals and acquired a special status in the arena of competition with the help of increased competitive intelligence and organizational intelligence as well. The present research deals with presenting a business intelligence (BI) application model in hedge funds supporting knowledge-based companies to promote their performance. The present study is developmental, from the perspective of purpose, and descriptive survey, from that of research method. The statistical population of the study constitutes the employees of the hedge funds in Tehran; however, due to the limited scope of the statistical society, counting all method was used to choose the sample size. Questionnaire was used as the research tool. The validity and reliability of the questionnaire was confirmed using, respectively, Thurston method and Cronbach's alpha. Furthermore, SPSS19 software was used to analyze data. Investigation of the data revealed that business intelligence has a significant impact upon the funds in supporting knowledge-based companies. Amongst the indicators of business intelligence, the highest effectiveness was dedicated to analytical data warehouse indicator followed by corporate dashboards and data mining indicators, respectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".