Information and analytical support system of enterprise competitiveness management
Bibliographic record
Abstract
Under the conditions of intense competitive struggle, integrated assessment methods are gaining increasingly greater importance in the processes of information and analytical support of enterprise development management. Enterprise competitiveness is one of the most universal, and, consequently, methodological and applied features among such generalizing characteristics. However, almost all of the approaches used are characterized by both significant advantages and certain disadvantages as of the date. This study develops a system of information and analytical support for managing enterprise competitiveness in the market of fast moving consumer goods, where competition is near the highest. The author's approach for assessing the competitiveness status of enterprises is developed. For this purpose, a system of indicators is determined, which reveal comprehensively and systematically the main parameters of competitiveness according to its structural components, or subsystems: (1) personnel, (2) property, (3) commodity, (4) organizational. The peculiarity of the approach includes the combination of the method of enterprise position rating in the market, and expert survey, which ends with presentation of the results using the graphical method. Summarizing approbation of the developed methodological approach to the assessment of enterprise competitiveness status, the results allow to allocate the leader enterprises, enterprises of average level of competitiveness, enterprises of attack zones and lost opportunities, and outsider enterprises are obtained. The results include the possibility of implementing a new better and more comprehensive approach for analyzing the status of competitiveness of competing enterprises, which serves as a significant information and analytical basis for policy planning to strengthen the competitive position of businesses for the consumer goods market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".