Power of nonverbal behavior in online business negotiations: understanding trust, honesty, satisfaction, and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it