Competitive intelligence and absorptive capacity for enhancing innovation performance of 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
In dynamic and complex environments, it can be difficult for small and medium- sized enterprises (SMEs) to achieve business performance, innovate and survive, even though these actions are crucial for economic growth and competitiveness. Competitive intelligence (CI) appears as a strategic practice to help them. Although there are many theoretical studies that propose the relationship between CI and innovation, few studies have conducted empirical studies in the context of SMEs. The objective of this paper is to investigate how competitive intelligence enhances innovation performance in the context of a SME. Based on a literature review and empirical data from several interviews with managers of one SME, our findings allowed us to propose a framework showing the contribution of CI to innovation performance relying on absorptive capacity. Our findings also highlight that a prospector owner-manager can improve the results of CI in the SME and contribute to better innovation performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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