Individual Level Knowledge Transfer in Virtual Settings
Bibliographic record
Abstract
Since the emergence of the knowledge-based view of the firm in the mid-1990, researchers have made considerable effort to untangle the complexity of how individuals create, capture and realize value from knowledge. To date, this burgeoning field has offered rich and yet diverse insights involving contextual, process and outcome factors that influence individual level knowledge transfer. Concomitantly globalization and advancing technologies have extended virtual work arrangements such as virtual teams and virtual communities on the internet and considerably extended the knowledge base upon which individuals can draw when creating, acquiring, sharing and integrating knowledge. Research on individual level knowledge transfer has also embraced these virtual environments spawning new insights. Hence the objective of this paper is to assess current state of research and identify potential avenues for future research at the intersection of these two dimensions. The authors focus specifically on knowledge transfer research at the individual level instead of the team or firm level and within virtual settings. Applying a process view of knowledge transfer, they synthesize existing findings and discuss issues surrounding the inputs, processes, and outputs. The synthesis reveals both strengths and gaps in the literature. Accordingly, the authors offer directions for future research that may address the gaps and contribute to a more comprehensive understanding of individual level knowledge transfer in virtual settings.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".