Integration of business intelligence with corporate strategic management
Bibliographic record
Abstract
The traditional model of competitive intelligence and its operationalization in most organizations appears to be inadequate to address the intelligence challenges arising from the speed of change in the environment, increasing data complexity, and growth of international activities. To address this challenge, this article borrows concepts from open innovation, applying them to all CI activities. We are suggesting going beyond the traditional model of an in-house CI unit with activities largely conducted by the units personnel and moving towards a cross pollination approach whereby others in the firm contribute to all CI activities including, for example, the selection of key intelligence topics and being involved in analysis and eventually towards a full open intelligence model in which key stakeholders and external experts also assist the organization in all aspects of competitive intelligence activity. In proposing a more open approach for intelligence, the authors recognize the concern that CI professionals will have regarding sharing intelligence and intelligence activities outside the CI unit and outside the organization. However, as pointed out in this article, organizations around the world have been moving quickly towards an open innovation model generally concluding that the benefits associated with opening up all elements of the innovation process, including R&D, outweigh the risks of intellectual property loss.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".