Power of nonverbal behavior in online business negotiations: understanding trust, honesty, satisfaction, and beyond
Bibliographic record
Abstract
Digital teamwork has become prevalent and is ever since becoming part of the human work- and life-style, globally. But in comparison with face-to-face setting, virtual teams face multifold challenges. To date, scarce empirical research has examined whether team-breaking challenges are associated with limited access to peer nonverbal signals. This study examines whether access to body signals is associated with effective teamwork, and whether limited access provokes key team challenges. We also examine what social-psychological team concepts can be detected from peers’ consciously or unconsciously displayed visual cues that cannot be as effectively gained without visual access. 14 dyadic teams of MBA students were examined in an online business negotiation task to reach an authentic commercial deal. Half of the teams negotiated only through voice and text, while the other half had camera access as well. Using an exploratory mixed methods analysis, we identified 12 unique team factors based on nonverbal data. We also found that teams with camera access could build mutual trust more rapidly, detect peer honesty better, and realize agreements on suggestions more accurately. Surprisingly, we also found instances where camera access became stressful and participants reported it as an additional burden. Conclusions and implications are reported at the end.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".