Competitive intelligence as a creative tool for the innovation process: an exploratory study in SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it