Examining Multiculturals’ and Multilinguals’ Paradoxical Bridging Behaviors in Overcoming Cultural and Language Barriers in Organizations
Bibliographic record
Abstract
Research has identified the usefulness of multicultural and multilingual employees in overcoming cultural and language barriers in international work contexts, but still needs to clarify why and how these employees engage in bridging behavior. Based on in-depth analyses of 154 interviews, we inductively develop a comprehensive model of bridging behaviors with novel and counterintuitive insights. We show that bridging behaviors are not only based on individual strengths, which multiculturals and multilinguals possess, but also—paradoxically—on their weaknesses. Multiculturals’ and multilinguals’ strengths and experience with weaknesses result in different determinants and enactments of bridging. Grounded in our inductive theory building, we propose four bridging behaviors: cultural teaching, language teaching, cultural facilitating, and language facilitating. Multiculturals and multilinguals cycle between these bridging behaviors depending on their capabilities and motivations in specific situations. We provide theoretical and practical implications of our findings.
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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 | 0.001 | 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 | 0.000 | 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".