Market Intelligence Dissemination Practices
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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