Intermediate Effect of Adaptation Agility on Channel Performance under Different Incentive Strategies
Bibliographic record
Abstract
This study explores the relationship between the incentive strategies, retailers' adaptive agility and channel performance to construct a marketing relationship theory through empirical research, and applies the research results to the retail industry, thereby helping suppliers to choose suitable incentive strategies to achieve the goal of maximizing channel performance. Furthermore, this study intends to explore the appropriate incentive strategies suppliers should choose to improve retailers' agility, and how transactions should be enabled between suppliers and retailers to adapt quickly to the rules of both parties and respond quickly to the needs of each other. To verify our research hypothesis, this research collects data from the Taiwanese mobile phone market, where the retailer's purchasing staff and business executives will be our target group for questionnaires. 500 questionnaires were used to distributed, and 317 questionnaires were effectively recovered. Then, SEM was used to verify the relationship. This study discovers that suppliers adopting preferential incentive strategies will improve the overall channel performance. Suppliers adopting preferential incentives will increase the agility of retailers. The retailer adaptation agility is a mediating variable in the relationship between preferential incentive strategies and channel performance. There is no mediating effect in the relationship between effectiveness incentive strategies and channel performance.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".