Conceptualizing and measuring the virtuality of teams
Bibliographic record
Abstract
Abstract Virtual teams (VTs) are teams whose members do not share a common workspace all of the time, and must therefore collaborate using communication and collaboration tools such as email, videoconferencing, etc. Although the body of research on VTs is quickly expanding, to date, the field has yet to produce a comprehensive and coherent foundation upon which future research can be based, and empirical findings based on a substantive sample of real VTs remain limited at this time. This study fills a void in the VT literature with respect to defining and operationalizing the construct of degree of virtuality, and responds to calls for research that studies ongoing VTs, under real conditions. Data were collected from 30 VTs working in a Canadian technology‐based organization. Degree of virtuality was defined to include three dimensions: the proportion of work time that the VT members spend working apart (team time worked virtually), the proportion of the team's members who work virtually (member virtuality) and the degree of separation of the team's members (distance virtuality). The VTs in this study were found to have varying degrees of virtuality, and although the three dimensions were not highly intercorrelated, all were found to be significantly correlated to variables that have been previously linked to VT effectiveness. The correlations were all in the expected direction (negative), indicating that higher degrees of virtuality are associated with perceived decreases in the quality of team interactions and performance. The results of this research would suggest that the more that teams move away from the proximate form, the more the traditional measures of team effectiveness are negatively impacted.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".