Bibliographic record
Abstract
This study explores the multifaceted role of culture in supply chain collaboration, focusing on how cultural differences shape communication, trust-building, negotiation, conflict resolution, risk management, and ethical standards. Utilizing a qualitative methodology, semi-structured interviews were conducted with 25 supply chain professionals from diverse cultural backgrounds and industries. The findings reveal that high-context cultures favor indirect communication and nuanced information exchange, which can lead to misunderstandings with partners from low-context cultures who prefer direct and explicit communication. Trust-building practices vary significantly, with high uncertainty avoidance cultures emphasizing formal agreements and procedural rigor, while low uncertainty avoidance cultures prioritize personal relationships and demonstrated reliability. Negotiation strategies also diverge, reflecting the underlying values of collectivism versus individualism, impacting the tactics and outcomes of supply chain negotiations. Conflict resolution approaches differ, with high power distance cultures favoring hierarchical and mediated solutions, and low power distance cultures preferring direct and egalitarian methods. The study highlights the need for adaptability to cultural changes and the development of cultural intelligence as essential competencies for managing cross-cultural supply chains. Additionally, cultural differences in risk management approaches and governance mechanisms affect the coordination and resilience of supply chains, while varying ethical standards influenced by cultural norms necessitate culturally sensitive strategies for promoting corporate social responsibility. The insights gained from this research underscore the importance of understanding and leveraging cultural differences to build stronger, more productive supply chain relationships, contributing to the broader literature on culture and supply chain management and offering practical implications for enhancing global supply chain collaboration.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".