An evaluation framework for outcome and impact measures
Bibliographic record
Abstract
ABSTRACT Competitive intelligence (CI) measurement practices within organizations remain fragmentary and elusive, although prescriptive CI performance and impact measures have been proposed in the literature. This study responds to calls for research into CI measurement in order to examine why organizations fail to measure CI, and to develop an evaluation framework for prescriptive measures that would support the evolution of best practices in measurement. A qualitative study (the ‘users study') consisting of interviews and shared negotiated texts with 12 users of CI was conducted. Study participants were senior managers and executives who use CI in the course of their work responsibilities at their respective individual organizations. Participants indicated that measurement cost, confused conceptualizations of CI measurement, and skepticism regarding the informativeness of measurement were obstacles to the implementation of CI measurement within their organizations. Although few participants conduct measurement activities, participants were all able to describe the ideal characteristics of CI outcome and impact measures. That list is here combined with the findings of an earlier study (the ‘experts study') conducted by the authors (Gainor & Bouthillier, ), in order to develop the evaluation framework provided here. This study provides a rare account of CI user perspectives on the rationale behind the lack of CI measurement within organizations, and a unique tool, the evaluation framework, which may be used to support both research and training within the field of CI.
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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 | 0.374 | 0.321 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".