Bibliographic record
Abstract
Electronic shelf label (ESL) is an emerging price display technology around the world. While these new technologies require non-trivial investments by the retailer, they also promise significant operational efficiencies in the form of savings in material, labor and managerial costs. The presumed benefits of ESL, for example, tend to be focused around lower price adjustment costs (PAC), also known as menu costs. However, ESL not only can save PAC but may also enable the retailer to price “better,” generating greater value for the transacting parties. Thus, ESL’s strategic impact for retailers occurs between claiming these presumed efficiencies and realizing the value generating potential. Using transactions data from a longitudinal field experiment, we assess such impact of ESL by studying how it shapes retail pricing practices and outcomes. Our general finding is that ESL plays an enabling role to the retailer’s strategy – thereby enhancing the retailer’s sales and revenues. The price adjustment efficiencies of ESL allows retailers to do better waste management, price discovery, as well as leveraging value in information for consumers. However, ESL’s impact on prices is nuanced, based on the retail strategy (EDLP, HI-LO) being used. Papers quantifying emerging technologies’ impact on retail outcomes are sparse, even fewer investigating their role in pricing. To the best of our knowledge, ours is the first study to explore and quantify how ESL interacts with retail strategy to affect retail pricing practices and retail outcomes.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.185 | 0.023 |
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".