Multilingual and multicultural managers’ effects on team performance: insights from professional football teams
Bibliographic record
Abstract
Purpose In this study, micro-foundations of strategy as the theoretical framework to study the effect of managers’ individual characteristics on multinational team performance are adopted. In particular, the purpose of this paper is to study managers’ multilingual communication abilities and multicultural background, and their role in, respectively, effectively reconfiguring team human assets and sensing cognitively distant opportunities and threats. Design/methodology/approach This study uses national football teams competing in national and international competitions and their coaches’ characteristics as the data set to test the theory. Using random coefficient modeling and ordinary least square regression, this paper analyzes two samples of 222 and 79 teams and found that both these characteristics contribute to team performance; however, their effects differ depending on the team environment. Findings Multicultural managers contribute positively to team performance only when the team is operating in a highly diverse environment, their effect is not statistically significant in homogeneous environments. In less diverse environments, it is the multilingual manager who can improve team performance through more efficient communication and greater effects of leadership on the team. Originality/value Managers’ characteristics such as their multicultural background and multilingual capabilities affect team performance. In particular, these effects come into play in highly diverse and international settings. Micro-foundation literature is advised to focus on the internationalization and multicultural backgrounds of managers as a precursor for organizational international performance.
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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 | 0.001 |
| 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".