Bibliographic record
Abstract
Purpose – The purpose of this paper is to test the conditional effect of team composition on team performance; specifically, how collective team orientation, group consensus, faultline configurations and trust among team members explain the objective performance of project teams in cross-cultural contexts. Design/methodology/approach – Employing path analytical framework and bootstrap methods, the authors analyze data from a sample of 73 cross cultural project teams. Relying on ordinary least-squares regression, the authors estimate the direct and indirect effects of the moderated mediation model. Findings – The findings demonstrate that the indirect effect of collective team orientation on performance through team trust is moderated by team member consensus, diversity heterogeneity and faultlines’ strength. By contrast, high dispersion among members, heterogeneous team configurations and strong team faultlines lead to low levels of trust and team performance. Research limitations/implications – The specific context of the study (cross-cultural students’ work projects) may influence external validity and limit the generalization of the findings as well as the different compositions of countries-of-origin. Practical implications – From a practical standpoint, these results may help practitioners understand how the emergence of trust contributes to performance. It will also help them comprehend the importance of managing teams while bearing in mind the cross-cultural contexts in which they operate. Social implications – In order to foster team consensus and overcome the effects of group members’ cross-cultural dissimilarities as well as team faultlines, organizations should invest in improving members’ dedication, cooperation and trust before looking to achieve significant results, specially in heterogeneous teams and cross-cultural contexts. Originality/value – The study advances organizational group research by showing the combined effect of team configurations and collective team orientation to overall team performance and by exploring significant constructs such as team consensus, team trust and diversity faultline strength to examine their possible moderated mediation role in the process.
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 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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".