Understanding the Paradox in the Results of National Cultural Distance Impact on International Joint Venture Performance: A Narrative Review of Prior Literature
Bibliographic record
Abstract
The literature on international joint ventures (IJVs) continues to grow owing to their crucial importance in global market competition. National cultural distance (NCD) between partner firms is conventionally considered one of the main contributors to unsatisfactory IJV performance, leading to its dissolution. Nevertheless, there is no consensus on the extent of the impact of NCD on IJV performance, nor on whether this impact is predominantly positive or negative. Therefore, we conducted a narrative review to shed light on prior research (including the earliest works) to explore and clarify the causes of these apparent inconsistencies in the research’s results. Theoretically, the current research offers guidance for future study on the topic. Researchers should introduce moderator and mediator variables in pursuit of a more nuanced understanding such as mutual trust between partners. Practically, this paper will help IJV managers mitigate the negative effects of NCD and improve overall venture performance by selecting culturally intelligent managers, providing cross-cultural training for IJV staff, and implementing cooperative conflict resolution. Like any study, this paper has some limitations that should be examined by future researchers.
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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".