Virtual team collaboration: building shared meaning, resolving breakdowns and creating translucence
Bibliographic record
Abstract
Abstract Managing international teams with geographically distributed participants is a complex task. The risk of communication breakdowns increases due to cultural and organizational differences grounded in the geographical distribution of the participants. Such breakdowns indicate general misunderstandings and a lack of shared meaning between participants. In this paper, we address the complexity of building shared meaning. We examine the communication breakdowns that occurred in two globally distributed virtual teams by providing an analytical distinction of the organizational context as the foundation for building shared meaning at three levels. Also we investigate communication breakdowns that can be attributed to differences in lifeworld structures, organizational structures, and work process structures within a virtual team. We find that all communication breakdowns are manifested and experienced by the participants at the work process level; however, resolving breakdowns may require critical reflection at other levels. Where previous research argues that face‐to‐face interaction is an important variable for virtual team performance, our empirical observations reveal that communication breakdowns related to a lack of shared meaning at the lifeworld level often becomes more salient when the participants are co‐located than when geographically distributed. Last, we argue that creating translucence in communication structures is essential for building shared meanings at all three levels.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 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".