Two Sides to Every Story: Mitigating Intercultural Conflict through Automated Feedback and Shared Self-Reflections in Global Virtual Teams
Bibliographic record
Abstract
Global virtual teams experience intercultural conflict. Yet, research on how Computer-Mediated Communication (CMC) tools can mitigate such conflict is minimal. We conducted an experiment with 30 Japanese-Canadian dyads who completed a negotiation task over email. Dyads were assigned to one of three conditions: C1) no feedback; C2) automated language feedback of participant emails based on national culture dimensions; and C3) automated language feedback (as in C2), and participants’ shared self-reflections of that feedback. Results show Japanese and Canadian partners interpreted the negotiation task differently, resulting in perceptions of intercultural conflict and negative impressions of their partner. Compared to C1, automated language feedback (C2) and shared self-reflections (C3) made cultural differences more salient, motivating participants to empathize with their partner. Shared self-reflections (C3) served as a meta-channel to communication, providing insight into each partner’s intentions and cultural values. We discuss implications for CMC tools to mitigate perceptions of intercultural conflict.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".