Exploring diversity effects: nationality composition and nationality context in workgroups
Bibliographic record
Abstract
The types of conflict that emerge in nationally diverse teams may depend on the number and types of nationalities present in the team. We investigate the possibility that when teams have individuals from multiple different nationalities (i.e., Dutch, Swiss and Indonesian), rather than just two different nationalities (Dutch and Indonesian), performance and task conflict will be higher while process and relationship conflicts will be comparatively lower. A scenario-based study was conducted in two countries in which we examined how nationality composition (size of national diversity or number of nationalities) and context (nature of national diversity or types of nationalities) affected perceived conflict and expected performance. We hypothesized and found that task conflict and performance are higher in nationally diverse workgroups that included multiple dissimilar nationalities compared to workgroups with just two nationalities. Results also showed that relationship and process conflicts are lower in groups that are diverse in size and nature of national diversity. We observed that social distances among nationalities varied in such a way that a distant nationality became more distanced and a close nationality became even closer in a nationally diverse group. Social distance, in that way, moderated the effect of national diversity. We discuss implications for diversity and conflict management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 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) | 0.000 | 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".