Bibliographic record
Abstract
Purpose The aim of this study is to develop and empirically test a theoretical model of competition between anchor and non‐anchor stores in a shopping mall. In doing so, the goals are to extend the literature on retail co‐location to account for effects of anchor stores' quality levels, and to explain an observed pattern of choices of anchor‐store quality levels made by mall developers. Design/methodology/approach This study uses a game‐theoretic approach to model the actions of mall developers, stores, and consumers in a competitive framework, then verifies the equilibrium predictions of this model using an empirical approach and a data set including all major malls in the US and Canada. Findings The key finding of both the analytical and empirical models is that there exists a positive and concave (i.e. reverse U‐shaped) relationship between anchor quality and mall size, i.e. that the highest‐quality malls are typically found in the middle range of mall sizes. Research limitations/implications This study introduces a relatively basic framework that could be expanded to incorporate a more flexible variety of contract types between mall developers and tenants, as well as additional sources of consumer utility associated with a single visit to a mall. Practical implications This study provides mall developers with a basis for understanding the impact of anchor quality on competition between stores in a mall. Originality/value This study addresses a gap in both the analytical and empirical literature on determinants of mall traffic and profit, specifically pertaining to how these variables are affected by anchor stores and their quality levels.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".