Bibliographic record
Abstract
Purpose The article traces the origins of the competitive intelligence fields and identifies both the practitioner, academic and inter‐disciplinary views on CI practice. An examination of the literature relating to the field is presented, including the identification of the linear relationship which CI has with marketing and strategic planning activities. Design/methodology/approach Bibliometric assessment of the discipline. Findings reveal the representation of cross disciplinary literature which emphasises the multi‐faceted role which competitive intelligence plays in a modern organization. Findings The analysis supports the view of competitive intelligence being an activity consisting dominantly of environmental scanning and strategic management literature. New fields of study and activity are rapidly becoming part of the competitive intelligence framework. Research limitations/implications The analysis only uses ABI Inform as the primary sources for literature alongside Society of Competitive Intelligence Professionals (SCIP) and Competitive Intelligence Foundation (CIF) publications, particularly the Journal of Competitive Intelligence and Management. A more comprehensive bibliometric analysis might reveal additional insights. Simple counts were used for analytical purposes rather than co‐citation analysis. Practical implications Attention is drawn to the need for the integration of additional, complementary fields of study and competitive intelligence practice. It is clear that today's competitive intelligence practitioner cannot afford to rely on what they learned 20 years ago in order to ensure the continued competitive advantage of their firm. A keen understanding of all business functions, especially marketing and planning is advocated. Originality/value While there have been bibliographies of competitive intelligence literature there have been few attempts to relate this to the three distinct areas of practice. This article is of use to scholars in assisting them to disentangle the various aspect of competitive intelligence and also to managers who wish to gain an appreciation of the potential which competitive intelligence can bring to marking and business success.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.071 |
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".