The Effects of Off-Price Store Opening on the Incumbent Channels of a Multichannel Retailer: Full-Line Stores Versus Online Store
Bibliographic record
Abstract
Many high-end retailers operate off-price or discount versions of their full-line stores to engage value-conscious customers. In this study, the authors empirically examine the effects of a high-end retailer's opening of off-price stores on customer behavior. Leveraging a unique customer-level dataset spanning pre- and postopening periods of off-price stores in the United States by a multichannel retailer, the authors disentangle the effects of physical off-price store opening on the incumbent channels (full-line physical stores and online store) and document the underlying mechanisms. The authors employ the group-time treatment effects doubly robust estimator that exploits the staggered opening of multiple off-price stores. The authors find that off-price store opening decreases customer spending (substitution effect or value customer effect) at the full-line store and increases spending at the online store (complementarity effect or option-to-return-products effect). The authors report that the retailer's new customers (i.e., those acquired via the retailer's off-price stores) tend to spend less, return products at a higher rate, purchase lower-priced items, and are in the lower-income group. Furthermore, the regular customers who shop at the full-line stores of the upscale retailer spend less at and return their online purchases at a higher rate to the off-price stores than the value-conscious customers. The authors perform a battery of robustness checks to rule out effects of confounding factors. Based on the results, the authors offer new insights and implications for high-end multichannel retailers that adopt an off-price store opening strategy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".