New taxonomy of relationship value and the moderating effect of relationship age: An empirical study in manufacturer-retailers partnership
Bibliographic record
Abstract
This study aims to examine the effect of relationship value and test the moderating effect of relationship age on performance in a manufacturer-retailer relationship.Relationship value is identified from four dimensions -personal, financial, knowledge, and strategic value, each of which is indicated in different outcomes.This research presents a new taxonomy that can be used for manufacturers from retailers' perceptions of what counts as relationship value.Furthermore, this study explores the effect of relationship age on the relationship value and performance.Data were collected from 259 retailers from paint and chemical stores in Indonesia, through a structured questionnaire, and collected quantitative data were analyzed through structural equation modeling (SEM).The analysis of the sample suggests a positive relationship between relationship value and performance.This research shows the impact of relationship age into a short-term and long-term relationship.Relationship age did not strengthen the positive relationship between relationship value and performance.Findings suggest that manufacturers should invest more time and effort in relationship value drivers with their key retailers to enhance their relationships with those retailers.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".