Bibliographic record
Abstract
Due to competitive markets in various industries, organizations should have accurate information about their competitors, market and their customers to make right decisions at the right time with sufficient information.In this regard, in the present paper, we discuss the components of intelligence competitive products and how these products affect developing marketing strategy stages.In other words, we will look that in what stage of strategic decision-making markets, achievements of competitive intelligence can help organization's managers.For this reason, there will be firstly identified processes and outcomes for the organization's competitive intelligence and steps of strategic marketing decision-making process by reviewing intelligence literature and citing examples of practical applications of competitive intelligence on successful organizations.Then, by preparing a questionnaire, we try to determine impact intensity of each product of competitive intelligence in any steps of strategic marketing decision-making process using opinions of organizational experts and managers and analyzing the questionnaires' data.Finally, we model the correlation between competitive intelligence products with any steps of strategic marketing decision-making process quantitatively.Using this model, organizational managers can realize the importance and position of any competitive intelligence products in their strategic decisions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.964 | 0.953 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".