From Culture Clash to Synergy: How Managerial Influence Fosters Tacit Knowledge Transfer in IJVs
Bibliographic record
Abstract
While the majority of existing research on international joint ventures (IJVs) has emphasized national cultural differences, this novel study foregrounds the crucial but under-examined impact of organizational cultural distance on tacit knowledge transfer. Is synergy achievable between IJVs and foreign parent firms with different organizational cultures? In the context of this complex hierarchical relationship, our study foregrounds strategic influence activities (a manager’s upward and downward influence) as key mechanisms that bridge cultural gaps and facilitate tacit knowledge sharing. In this paper, we developed and tested a mediation model of interunit relational factors involved in tacit knowledge transfer. By integrating international business research with social identify theory and dynamic capability theory, we hypothesized that strategic influence activities mediate the effect between differences in organizational culture and tacit knowledge transfer. Using a PRROCESS Macro and survey data from 199 IJVs in South Korea, we tested these effects. Our study shows that strategic influence activities significantly mitigate the negative effects of organizational cultural differences on tacit knowledge transfer, highlighting downward activity as a critical mediator. This study dives deeper into strategic influence activities in the context of culturally diverse IJVs and offers practical insights for managing cultural diversity in hierarchical international business relationships.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".