Creating enhanced work environments for global virtual teams: using CQ as the strongest link in the team
Bibliographic record
Abstract
Purpose This study investigates how the maximum cultural intelligence (Max CQ) within a team – defined as the highest cultural intelligence level of an individual member – affects intra-team communication, conflict dynamics and, ultimately, team satisfaction and performance in global virtual teams (GVTs). Design/methodology/approach Utilizing quantitative research methods, this investigation draws on a dataset comprising 3,385 participants, which forms a total of 686 GVTs. Findings The study reveals that MaxCQ significantly enhances team communication, which in turn mitigates conflict, increases satisfaction and improves performance. It is noteworthy that the influence of MaxCQ on GVT success is more significant than the average cultural intelligence (CQ) of team members, providing critical insights for effective GVT management strategies. Practical implications The findings suggest that managers may optimize team dynamics not by uniformly increasing each member’s CQ but by concentrating on maximizing the CQ of one individual who can act as an influencer within the team. Strategically placing individuals with high CQ in GVTs can enhance overall team function. Originality/value While existing literature primarily examines the individual effects of CQ on communication and conflict management, this study sheds light on the collective interplay between MaxCQ, communication and conflict. It highlights the importance of MaxCQ, along with the frequency of team communication and conflict, in influencing team satisfaction and performance in GVTs.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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 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".