Knowledge Acquisition and Integration in Virtual Teams - A Practice-based Perspective
Bibliographic record
Abstract
Although virtual teams have become increasingly important for managing knowledge work in present organizations, the extant literature suggests that they are faced with significant hurdles to knowledge acquisition and integration. On the other hand, research focusing on knowledge management in virtual teams is yet to be enriched. This dissertation aims to add to the understanding of the mechanisms by which virtual teams acquire and integrate knowledge to achieve favorable individual and team performance, and of the role of information and communication technology (ICT) in the process. Based on a practice-based perspective of learning that suggests learning is situated in individualsâ work practice in relation to its surrounding physical and social context, a three-phased study is designed. The first phase attempts to define and measure shared practice â the key construct in the practice-based learning theory, and examine the relationship between shared practice, learning outcomes and use of ICT. The second phase adopts a process-oriented approach and focuses on explaining how knowledge acquisition and integration occurs through ICT-enabled work practice. The third phase works as a substantial extension of the first two phases by replicating them in a second organization with different settings, with the aim to examine the generalizability of the findings from the first organization. This study is expected to contribute to the growing body of the virtual team literature, its aspect of knowledge management in particular, by looking into the construct of shared practice, its relationship with learning outcomes, and the process of practice-based learning in virtual teams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".