Knowledge transfer in virtual settings: the role of individual virtual competency
Bibliographic record
Abstract
Abstract Economic forces, competitive pressures and technological advances have created an environment within which firms have developed new ways of organizing (e.g. virtual work settings) and managing their resources (e.g. knowledge management) in order to maintain and improve firm performance. Extant research has highlighted the challenges associated with managing knowledge in virtual settings. However, researchers are still struggling to provide effective guidance to practitioners in this field. We believe that a better understanding of individual virtual competency is a potential avenue for managing the complexity of knowledge transfer in virtual settings. In particular, we suggest that optimal knowledge transfers can be achieved by individuals armed with the right personal capabilities and skills for virtual work, particularly when those knowledge transfers are emergent, bottom‐up and cannot be specified a priori. The virtual competency exhibited by individuals can be the key to overcoming the constraints of knowledge transfers with such characteristics because underlying competency can facilitate effective action in unfamiliar and novel situations. In this conceptual research, we develop a theoretical model of individual virtual competence and describe its role in the communication process, which underpins effective knowledge transfer in virtual settings. Additionally, we consider the antecedent role that prior experience in virtual activity plays in aiding workers to develop virtual competence, which in turn engenders effective knowledge transfer. We conclude with implications for future research and for practicing 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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".