On the relationship between competitive intelligence and innovation
Bibliographic record
Abstract
Innovation research suggests customer, competitor and market knowledge are important requirements for innovation. Researchers in competitive intelligence (CI) have proposed that there should be a relationship between CI and innovation. Yet despite both fields recognising the need for CI and related areas for innovation in their theories, there have not been many empirical studies that look at CI and innovation and those few studies that do exist have limited focus and have only looked at a small subset of CI variables (for example collection sources). The aim of this study is to examine if there is a relationship between CI and innovation. This was done by surveying Strategic and Competitive Intelligence Professional (SCIP) members and those attending SCIP events, and asking them about their intelligence practices and how innovative their company was. Ninety-five questions were asked about CI structure and organization, intelligence focus, information sources used, analytical techniques used, communication methods, and the management of the intelligence efforts. Of the 95 competitive intelligence measures used in this study, 56 (59%) were significantly correlated with the study’s measure of innovation. The measures within the CI organizational elements and CI management categories had the highest percentage of measures significantly correlated with innovation (90% and 89%). Four of the CI measures had statistically significant correlations above .300. These included the extent to which business decisions in the organization were better facilitated/supported as a result of intelligence efforts (.355), the number of performance measures used in assessing CI’s performance (.322) and decision depth (.313), which is a measure of the number of decisions that utilized CI. As a study of this nature measuring the relationship between CI and innovation has not been conducted previously, the findings can be beneficial to organisations using innovation to succeed in the competitive environment.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".