Types of Asymmetries in Exporter-Importer Relationships and Alignment Behaviour
Bibliographic record
Abstract
The purpose of this paper is to explore the phenomenon of asymmetry in international supply chain relationships and investigate how small exporter firms manage these asymmetric relationships. Prior studies have conceptualized asymmetry as a relationship attribute and concentrated on causal effects of asymmetry in supplier-buyer relationships by highlighting the extensive amount of reasons why asymmetry occurs in dyadic relationships. However, those extensive reasons intent to articulate why asymmetry occurs in relationships, have not provided enough in-depth to understand the complexity of asymmetry in international supply chain relationships, therefore, this study aims to explore the concept of asymmetry by focusing on structure and exercise rather than simply discovering reasons. This research explores the four distinguishable types of relational asymmetry between exporters and importers suggest different implications for international supply chain relationships. As opposed to the existing literature, which has considered and largely agreed that asymmetry is related to a negative connotation, this research suggests that different types of asymmetries also have a positive relational outcome for small export firms. This exploratory paper provides managers with additional insight into the types of asymmetry in international supply chain relationships and suggests that asymmetric relationships must be examined carefully in order to overcome difficulties that distract long-term 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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".