The Interplay of the Introverts’ Stress and Team Effectiveness in Virtual vs. In-Person Meetings
Bibliographic record
Abstract
The project contributes to the research community, academics, and practitioners in the field of teams and personality, particularly remote team meetings. It explores how the introversion of the team members can affect perceived stress, meeting effectiveness, and establishing individual and team benefits for introverts. We predicted that technology-mediated communication will affect the relationship between more introverted team members' personalities and critical team processes and outcomes in such a way that the relationship is more positive and strong compared to the face-to-face environment. Our preliminary findings support the interplay of the constructs. Empirical data from 510 IT managers in North America confirmed that introverts perceive online meetings as less stressful and that they are associated with higher team effectiveness, especially when mediated by reduced stress. In contrast, and in line with previous research, extroverts demonstrated a stronger preference for in-person interactions. Future studies will seek to identify specific conditions in which virtual environments enhance the role of personality traits and support the well-being of introverted members, as well as individual and team outcomes. In a post-COVID world, where hybrid and remote work persists, these insights are essential for developing strategies that value all personality types and maximize team potential.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".