The e‐flow audit: an evaluation of knowledge flow within and outside a high‐tech firm
Bibliographic record
Abstract
Use of computer mediated communication, specifically electronic mail (e‐mail), has grown exponentially in recent years reaching as high as 75 percent penetration per household in some countries. The penetration rate is even higher for corporate environments. E‐mail is the communication medium of choice for most businesses and can therefore be construed as a proxy for codified knowledge flow in organizations. This paper advances the knowledge management body of literature by empirically examining several phenomena. Specifically, a comparison is made between inter‐ and intra‐departmental knowledge flows in organizations. Furthermore, knowledge flows within functional departments as well as knowledge flows to and from the external environment are examined. Data were collected from 15,500 e‐mails logged over five random days in a high‐tech organization of 480 employees. These data were then mapped on to the organizational chart and compared with the perceptual responses of a survey administration. Quantitative results were then triangulated with qualitative data gathered during focus groups. The research results yielded two important findings: (1) individuals showed an interesting bias towards over‐estimating their perceived knowledge flow throughout the organization; and (2) the e‐flow audit is an insightful managerial tool which provides a snapshot for knowledge management evaluation.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".