Bibliographic record
Abstract
Purpose The aim of this paper is to theorize what relationship exists between knowledge loss and the manager type. Specifically, the purpose of this research is to determine if some types of middle managers report lower levels of information anxiety. A manager's knowledge classification was based on the seminal research of Davenport and Prusak, and Nonaka and Takeuchi. Design/methodology/approach A sample of Canadian Public Service middle managers completed an online survey instrument over a three‐month period in the autumn of 2003. Ninety‐nine usable survey results formed the basis of analysis for the project. To increase how one may generalize the findings, the sample was compared to a recent large random sample of the same population, which determined that the two samples were statistically the same. Segmenting the managers by knowledge transformation tasks (based on Davenport and Prusak) and knowledge exchange methods (based on Nonaka and Takeuchi) permitted the development of two hypotheses based on the dependent variable of information anxiety. Findings An empirical examination revealed that most of the sample reported relatively low levels of information anxiety. The type of tasks performed by the respondents was not a major factor; however, there was a significant negative relationship between frequency of task and information anxiety. The discovery of a weak positive relationship between tacit knowledge use and information anxiety provides the promise of exciting future research opportunities. Originality/value This pioneering research is the first project to consider the relationship between information anxiety and type of middle manager through the lens of knowledge transformation tasks and knowledge exchange methods.
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.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".