Competitive intelligence information and innovation in small Canadian firms
Bibliographic record
Abstract
Purpose The paper seeks to examine the relationship between the number of competitive intelligence (CI) information topics used by small Canadian firms and their innovation performance, measured by the number of newly launched products, processes and services. Design/methodology/approach A CI information framework was applied including 42 information topics classified into four groups, i.e. industry, competitors, customers and firm. The 45 firms in the sample were classified into three types, i.e. new technology‐based, specialized supplier, and service firms. Statistical analysis was used to analyze the relationship between CI information and innovation. Findings Analysis of the results suggested that there was a clear relationship between the CI information firms used and their innovation performance, specialized suppliers firms were the most efficient users of CI information, information about industry and competitors was the least used but highly relevant for firms' innovation performance, and information about customers was found to be highly used and relevant for the innovation of all firms. Practical implications The methodological validation of the CI information framework could help executive managers in the development of analytical tools enhancing the role of CI for new product/process/service launch. Originality/value The results demonstrate the need for using appropriate firm classifications and in depth statistical analysis when studying the relationship between CI information and innovation.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".