Big Boxes Versus Traditional Shopping Centres. Looking at Households' Shopping Trip Patterns
Bibliographic record
Abstract
The expansion of large shopping centres and, more recently, of ìbig boxî outlets and ìpower centresî in North-American and West European urban areas is a major feature of the retail trade sector development. While several internal and external factors affecting retail facilities design and location may be brought forward as possible explanations for this concentration, customersí behaviour in terms of shopping destination choices emerges as one of the main determinants of retail competition. In this paper, the competition between, on the one hand, regional and superregional shopping centres and, on the other hand, ìcategory killersî and ìbig boxesî is analyzed using discrete choice modelling (logistic regression). Thanks to an extensive Origin-Destination phone survey carried out in the Quebec Metropolitan Area in 2001 for transportation planning purposes and providing detailed information on both householdsí socioeconomic and demographic profiles and daily trip patterns, it is possible to identify and model customersí shopping choices with respect to the type of retail facilities they favour. Findings suggest that several household and trip attributes do impact upon customersí choice for either big boxes or traditional shopping centres. These are: customerís gender and age, trip purpose, car ownership, day of the week, departure time and place, transportation mode, type of household and trip length.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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; both teacher heads agree on what is shown here.
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".