The Evolution of Retail Location Decision-Making in Canada
Bibliographic record
Abstract
Major retail firms use various data, techniques, and technologies to support their location decision-making activities. Retail location decision-making has evolved in response to technological innovations, advances in data and analytical techniques, and the need to understand contemporary consumer behaviors. This study provides insights into the current state of location-based decision-making among leading Canadian retail firms, highlighting the extent to which they adopt big data and data science tools to support strategic decisions. Based on an online survey of corporate decision-makers, this study examines how retail location decision-making has evolved in the Canadian retail industry. The findings reveal that retailers face increasing data volume, variety, and velocity, with many using mobile data to provide granular customer insights. Established techniques remain central to decision support; however, big data techniques based on artificial intelligence are emerging. The geographic dimension of data is seen as important, although leveraging geographic insights can be challenging. The findings highlight the critical role of professional geographers in bringing in-demand geographical skills to complex retail location decisions. Canada serves as a comparison point for studying retail location decision-making due to its advanced retail sector, increasing adoption of data-driven practices, and unique geographic characteristics, offering transferable insights for other countries.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".