Knowledge coordination via digital artefacts in highly dispersed teams
Bibliographic record
Abstract
Abstract Virtual teams face the unique challenge of coordinating their knowledge work across time, space, and people. Information technologies, and digital artefacts in particular, are essential to supporting coordination in highly dispersed teams, yet the extant literature is limited in explaining how such teams produce and reproduce digital artefacts for coordination. This paper describes a qualitative case study that examined the day‐to‐day practices of two highly dispersed virtual teams, with the initial conceptual lens informed by Carlile's (2004) knowledge management framework. Our observations suggest that knowledge coordination in these highly dispersed virtual teams involves the continuous production and reproduction of digital artefacts (which we refer to as technology practices) through three paired modes: ‘presenting‐accessing’ (related to knowledge transfer); ‘representing‐adding’ (related to knowledge translation); and ‘moulding‐challenging’ (related to knowledge transformation). We also observed an unexpected fourth pair of technology practices, ‘withholding‐ignoring,’ that had the effect of delaying certain knowledge coordination processes. Our findings contribute to both the knowledge coordination literature and the practical use of digital artefacts in virtual teams. Future research directions are discussed.
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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 | 0.007 | 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 | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".