The performance implications of perceptual differences of dependence in marketing channels
Bibliographic record
Abstract
Purpose – The purpose of this paper is to address two essential questions: do perceptual differences regarding dependence matter in determining channel performance, and if so, how? Design/methodology/approach – The paper conducted an empirical study of 347 cellular telephone supplier-retailer dyads in China. A questionnaire survey was employed. Findings – The results reveal that a retailer's perceptual difference of dependence exerts a significant effect on its evaluation of supplier performance only. Retailer trust partially mediates the effect of the perceptual differences on supplier performance and retailer performance. Therefore, the particular side of a dyadic relationship that researchers choose to study matters in an unbalanced dependence relationship. Practical implications – Managers, depending on their side, should pay close attention to perceptual differences and their consequences and deliberately employ different strategies to ensure effective channel management. Originality/value – Do differences in parties’ perceptions of dependence influence channel performance? If they do, how do these perceived differences exert direct and indirect impacts? By answering these questions, the authors contribute not only to an understanding of the unique nature of dyadic channel relationships but also to methodological notions about whether to study one side in a dyad.
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 | 0.003 | 0.017 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".