Contemporary Practices of Intelligence Support for Competitiveness
Bibliographic record
Abstract
This paper focuses on the practices, assessment approaches, procedures, and applied aspects of competitive intelligence (CI). The study relies upon a survey of CI practitioners conducted in 2019 and a comparison of its results with a similar survey in 2006. It was found that companies spend the time devoted to this activity mainly on processes that go beyond collecting information, including planning, analysis, communications, and management. Most enterprises have official divisions and profile managers. The results are used to perform a variety of strategic and tactical tasks.The main sources of information are the Internet, company employees, customers, and industry experts. Compared to 2006, a new key resource has emerged — social networks. Of the analytical methods, SWOT analysis and the study of competitors are most often used. Several channels of communication are used simultaneously to disseminate the received information, mainly email and presentations are used. Key performance criteria are customer satisfaction and the number of decisions made based on the information gathered.A comparative analysis revealed that over the period separating the surveys of 2006 and 2019, the function of the CR has become more formalized. The share of companies with centralized divisions and CI managers has grown. Currently, this activity more often goes beyond the simple profiling and evaluation of competitors. Technology assessment, economic, and political analysis are more actively practiced.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".