You are entitled to access the full text of this documentInteracting with the e-tailer’s service investment in the presence of a store brand: Selling model choice
Bibliographic record
Abstract
To alleviate the inventory pressure and improve operational performance, the e-tailer that develops a store brand (SB) may make a service investment in her self-operated stores. However, the existing literature rarely considers such a service investment strategy and its impact on national brand (NB) suppliers, especially on their selling model selection. We employ a theoretical model to explore the interactions of the NB supplier’s selling model selection and the service investment strategy of the e-tailer developing an SB. Our findings show that under the reselling model, the e-tailer always benefits from her service investment. Interestingly, however, the e-tailer may suffer from her service investment under the agency model. Meanwhile, the likelihood of the e-tailer adopting service investment decreases as the consumer service sensitivity increases. Furthermore, we find that the service investment increases the scope wherein both firms prefer the reselling model. In addition, we show that the supplier may adopt the agency model rather than the reselling model to counteract the service investment strategy of the e-tailer. These findings provide actionable insights to help suppliers and e-tailers make strategic operational decisions.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.634 | 0.402 |
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".