THE EFFECTS OF CULTURE IN COMPUTER-MEDIATED NEGOTIATIONS
Bibliographic record
Abstract
The paper explores the impact of culture on anonymous inter- and intracultural negotiations conducted via the Internet using a Web-based negotiation support system (NSS). In e-negotiations, technology acts as a moderator in the relationship between culture and negotiation behavior. This implies that patterns of cultural impact on negotiations can be different from face-to-face negotiations. Communication technology reduces the transmission of social cues and increases the importance of explicit communication. Thus, cultural dimensions such as power distance, which rely on social cues, are reduced in their impact, while the impact of communication-related dimensions of cultures such as high vs. low context is amplified by the system. The empirical analysis of these effects is based on a set of bilateral negotiations involving 1366 participants carried out with the Web-based NSS Inspire. It indicates a significant influence of culture, particularly regarding negotiators’ expectations. We also found significant cultural differences with regard to communication patterns emerging during the negotiation process and outcomes of negotiations. Our results also indicate that as the negotiation process progresses, individual differences between negotiators, including their approach to problem solving, become more important than their cultural characteristics.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".