Market Intelligence Dissemination Practices
Bibliographic record
Abstract
Market intelligence is a cornerstone of the marketing concept and essential to market-focused strategic planning and implementation. Although the importance of market intelligence is widely accepted, how managers can ensure the organization-wide generation, dissemination, and responsiveness to market intelligence remains a persistent challenge. In this article, the authors investigate market intelligence dissemination practices and their resulting managerial responses. Using qualitative methods, the authors identify five market intelligence dissemination practices that either update and reinforce organization members’ existing schemas (mental models) of the market or create new, shared schemas of the market. Specifically, they find that the creation, existence, or absence of organizationally shared market schemas is crucial in explaining the effectiveness of different market intelligence dissemination practices. Thus, in addition to being experts on market intelligence, intelligence directors must be authorities on organizational learning and ways to create shared meaning structures that enable disseminated intelligence to be understood and used within their organizations. The authors conclude with suggestions for practitioners on how to manage intelligence dissemination across their organizations more effectively and efficiently.
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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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".