The impact of firm size on competitive intelligence activities
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Given the importance of competitive intelligence (CI) to the economic performance of firms, understanding whether CI practice is impacted by firm size or by their awareness of CI maybe important when creating programs designed to improve firms’ CI performance. This paper aims to address this by examining the extent to which the CI practices of small and medium-sized enterprises (SMEs) and large firms differed using a sample of firms with knowledge/awareness of CI. Design/methodology/approach A survey was developed that included 10 CI organization questions and 67 CI process questions. The survey was sent to a sample with awareness/knowledge of CI – strategic and CI professionals (SCIP) members and individuals who had attended SCIP events T-tests were then used to compare the SME’s and large firms’ responses to the 10 CI organization and 67 CI process questions. Findings For firms with CI awareness/knowledge, the study results suggest that size has very little relationship with CI practice. Of the 10 CI organization variables, only two were significantly different between the SME’s and the large firms. Large firms had more full-time CI staff and were more likely to have a formal intelligence unit compared to the SME’s. Of the 67 CI process variables, only four were significantly different between the SME’s and the large firms. Large firms made more use of company intranet for distributing CI findings use business analytics software and use commercial databases for information than SME’s while the SME’s used social media, in particular Facebook more than large firms, in their competitive intelligence activities. Originality/value This study uses a sample frame of firms with CI awareness/knowledge in examining differences between SME’s and large firms CI practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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