Information technologies, knowledge integration, and performance in virtual teams
Bibliographic record
Abstract
Virtual teams---defined as groups of geographically-dispersed individuals working together to accomplish a common goal and who rely heavily on information technologies (ITs) to communicate and coordinate their work---have recently captured the attention of numerous scholars and practitioners. The ability of virtual teams to cross various boundaries (e.g., geographical, temporal, cultural, etc.) has made them an attractive means for leveraging the resources of distributed organizations. However, the factors and mechanisms that influence their performance remain mostly unclear. To address this issue, this thesis proposes three essays where each contributes to better understand the phenomenon of virtual team performance in a specific way. In the first essay, an integrative model of virtual team performance is developed and used to review the extant empirical literature. This exercise has lead to the identification of a set of key direct and indirect drivers of virtual team performance. The second essay offers a knowledge-based view of virtual team performance and proposes a conceptual framework of knowledge integration effectiveness in virtual teams. The framework identifies three integration mechanisms enabled by information technologies and describes how the usage of those mechanisms can facilitate knowledge integration effectiveness in virtual teams. The framework also outlines the role of common knowledge as a key factor leading to effective knowledge integration in virtual teams, and discusses the linkage between knowledge integration effectiveness and virtual team performance. Finally, the third essay provides an empirical demonstration of the conceptual framework of knowledge integration effectiveness developed in the second essay. The framework is tested with 700 individuals working in 102 existing knowledge-based VTs and who use IT to coordinate the use of their knowledge inputs across boundaries. Results indicate that the impact of IT on knowledge integration effectiveness is fully mediated by the common developed within VTs about their collective task, the distribution of expertise, the IT-enabled communication structure of the team, and members' specialized knowledge domains. Consistent with the premises of the first two essays, knowledge integration effectiveness was positively associated with VT performance. Overall, this thesis brings a new perspective for understanding the phenomenon of virtual team performance, describes gaps in current research, and recommends avenues for future research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".