Cooperative showroom deployment considering consumer one-stop comparison shopping behaviour
Bibliographic record
Abstract
Purpose The aim of this study is to investigate whether an online retailer should cooperate with a competing brick-and-mortar retailer for a cooperative showroom which displays more products than an independent showroom (operated by the online retailer) does. Design/methodology/approach We develop two showroom strategies: an independent showroom owned by the online retailer and a cooperative showroom in which the online retailer showcases products in a brick-and-mortar retailer’s store. The different effects of the independent showroom and the cooperative showroom on consumer preference learning behaviour are characterized. Several analytical game models have been developed to study whether the online retailer cooperates with a local brick-and-mortar retailer and the brick-and-mortar retailer’s response to cooperation. Findings First, the online retailer prefers the cooperative showroom strategy even if it cannot reduce display costs. Second, encouraging consumers’ one-stop comparison shopping behaviour can alleviate price competition. Third, for a low cooperation commission or product differentiation, the brick-and-mortar retailer may not be willing to cooperate when physical product display plays a more significant role in increasing consumers’ purchase intention of online products. To improve the brick-and-mortar retailer’s cooperation incentives, the online retailer can reveal information about opening an independent showroom that can be used as a deterrent and push the brick-and-mortar retailer to cooperate. Practical implications The results provide some managerial insights for online retailers that are making decisions on opening physical showrooms in the competitive market. Originality/value This study provides new insight into why and when an online retailer and its offline competitor can cooperate for product display.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".