Working Together Apart? Building a Knowledge‐Sharing Culture for Global Virtual Teams
Bibliographic record
Abstract
A new impetus for greater knowledge‐sharing among team members needs to be emphasized due to the emergence of a significant new form of working known as ‘global virtual teams’. As information and communication technologies permeate every aspect of organizational life and impact the way teams communicate, work and structure relationships, global virtual teams require innovative communication and learning capabilities for different team members to effectively work together across cultural, organizational and geographical boundaries. Whereas information technology‐facilitated communication processes rely on technologically advanced systems to succeed, the ability to create a knowledge‐sharing culture within a global virtual team rests on the existence (and maintenance) of intra‐team respect, mutual trust, reciprocity and positive individual and group relationships. Thus, some of the inherent questions we address in our paper are: (1) what are the cross‐cultural challenges faced by global virtual teams?; (2) how do organizations develop a knowledge sharing culture to promote effective organizational learning among culturally‐diverse team members? and; (3) what are some of the practices that can help maximize the performance of global virtual teams? We conclude by examining ways that global virtual teams can be more effectively managed in order to reach their potential in this new interconnected world and put forward suggestions for further research.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".