Canadian competitive intelligence practices – a study of practicing strategic and competitive intelligence professionals Canadian members
Bibliographic record
Abstract
Purpose With intelligence (a field related to foresight) practice growing, the purpose of this study was to examine the practices of Canadian competitive intelligence (CI) practitioners. Design/methodology/approach Survey of Canadian CI practitioners who are SCIP members (Strategic and Competitive Intelligence Professional), using a revision to a previously used instrument designed to examine competitive intelligence practices. Findings Canadian SCIP member competitive intelligence practices seem to be more formalized than those found in the global SCIP study in 2006 with 84.8 per cent having a manager with CI responsibilities, 61 per cent with a formal centralized CI unit and only 9 per cent responding that CI was done informally. Intelligence units were generally smaller with 38 per cent having one full-time CI resource and 41 per cent having between 2 and 4 full-time resources. Additional findings on information sources used, analytical techniques used, evaluation methods and communication methods are reported. Research limitations/implications Despite getting responses from close to 50 per cent of SCIP members, the small sample size (79) makes it difficult to generalize the results beyond the Canadian SCIP environment and limits the testing that can be done. Originality/value The last study on Canadian competitive intelligence practices was in 2008, thus part of the originality of the study was getting more recent information on corporate intelligence practice. In addition, this is the first Canadian study to focus specifically on known intelligence practitioners (SCIP members). Past studies focused on companies in general regardless of whether respondents knew what competitive intelligence was or practiced CI.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".