Evaluating the Impact and Value of Competitive Intelligence From The users Perspective - The Case of the National Research Council’s Technical Intelligence Unit
Bibliographic record
Abstract
Understanding and being able to measure and prove the impact and value of intelligence is of significant importance. The objective of this study was to develop an evaluation instrument that the users of intelligence could fill in that could be used to assess both the impact and value of the intelligence they received. Starting with an evaluation instrument based on lists of benefits identified in the competitive intelligence literature, measures of these benefits and client satisfaction/service quality metrics, the study researchers interviewed clients of one large government competitive technical intelligence organization asking them to articulate the benefits they obtained from the intelligence they received and methods for evaluating these benefits. All users of intelligence identified benefits they had received from the intelligence received. Additional benefits beyond those that are in the current literature were identified by those interviewed. In terms of measurement of these benefits, intelligence users (the clients) understood why hard financial type measures for example ROI or dollar impact on performance was important (especially in their organization) they felt that assessing these for the intelligence they received would be difficult but that softer, more subjective measurement such as extent to which the user agrees that the intelligence provided the intended benefit could be used. Additional perceptual based indicators of service quality and customer satisfaction measures were also suggested by intelligence clients. Based onthe results of the literature review and interviews, an intelligence evaluation instrument was developed that asks the clients to assess the extent to which they have realized one or more of 27 impacts identified in this study as well as assessing 10 elements of service quality.
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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 | 0.040 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".