Competitive intelligence: a multiphasic precedent to marketing strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it