Retailing in college towns: spatial location and multimodal commuting
Bibliographic record
Abstract
College town retailers face specific challenges given their unique context. Our study contributes to retailing research by examining college towns to explicate the impact of store-university distance and commuting multimodality. Our research questions are: (1) how does store-university distance moderate the relationship between university foot traffic and retailer foot traffic? (2) How does university foot traffic interact with multimodal commuting in affecting retailer foot traffic? (3) What moderating effects did changes in the containment and health responses during the COVID-19 pandemic exert upon these relationships? Using foot traffic data from 157 Walmart and Target stores in 38 college towns from 2018–2020, we find that university foot traffic positively impacts store visits. Moreover, this positive effect is weakened as the store to university distance increases but is strengthened with greater commuting multimodality. In addition, we find that pandemic related containment and health measures amplify both the negative effect exerted by the distance between the store and university and the positive effect exerted by commuting multimodality upon the relationship between university visits and store visits. This research provides information that can be utilised by government officials and retail managers to enhance consumer accessibility to retail stores in college towns.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".