Competitive intelligence: a multiphasic precedent to marketing strategy
Bibliographic record
Abstract
Purpose The paper seeks to explore competitive intelligence as a complex business construct and as a precedent for marketing strategy formulation. Design/methodology/approach In total, 1,025 executives were surveyed about their companies' usage of competitive intelligence collection, analysis, and dissemination as well as their perception concerning certain organizational characteristics. Findings This research develops and tests intelligence as a precedent to marketing strategy formulation, revealing multiple phases and contributing aspects within the process. It also discovers that the practice of competitive intelligence, while strong in the area of information collection, is weak from a process and analytical perspective. Research limitations/implications While the sample was indeed a census of Canadian technology firms, care must be taken in generalizing the study beyond this industry, and certainly beyond the Canadian borders. Also, the questionnaire used only dichotomous variables (yes/no answers), which limited the testing that could be done. Practical implications Using these results, competitive intelligence departments and professionals can improve efficacy within their approach and execution strategies. Originality/value The contribution of this paper is two‐fold. It reveals many of the “state‐of‐the‐art” levels of practice within current competitive intelligence efforts, and it proposes a model of the intelligence process.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".