Competitive intelligence as a creative tool for the innovation process: an exploratory study in SMEs
Bibliographic record
Abstract
Although small and medium enterprises (SMEs) innovate by developing new products and services, these types of innovation have a high failure rate. This could be due to the poor implementation of tools and techniques that help evaluate and select the best ideas and turn them into new products and services. The implementation of competitive intelligence (CI) techniques in the ideation phase of innovation process is a factor that can promote innovation and its success. However, knowledge about these techniques in SMEs is limited. This research attempts to fill this gap through an exploratory study of three SMEs that are successful in products and services innovation. Our findings highlight that the use of a set of CI techniques, like strengths, weaknesses, opportunities, and threats analysis and brainstorm, improves the ideation phase of the innovation process and promotes success of products and services innovation. This study suggests an innovation process model, which includes CI as a set of techniques for generating and selecting ideas, and as an adjustment tool for the development and commercialisation phases.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".