Developments in Practice XIV: Marketing KM to the Organization
Bibliographic record
Abstract
KM is experiencing the steep downward slope of the "hype cycle" and some organizations are rushing to abandon KM as quickly as they rushed to adopt it. Unfortunately, much of our understanding of what KM can do for organizations is still limited to academic treatises and small pilot studies. Managers therefore realize they must market KM more effectively in order to communicate its potential and build a coalition of support while KM matures and evolves. To explore this issue, the authors convened a focus group of practicing knowledge managers. After examining how KM groups currently market themselves, this paper constructs a framework for marketing KM in an organization that integrates the experiences of KM managers with basic marketing principles. It concludes that KM faces many marketing challenges, including lack of understanding of the need, lack of brand awareness, and a negative brand attitude. It recommends that knowledge managers must see themselves as internal entrepreneurs, first building a market for their product and then developing an effective marketing strategy. It also suggests there is a hierarchy of knowledge needs in organizations that must be addressed sequentially in order to develop trust and credibility among general business managers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".