Big Data Analytics: The New Boundaries of Retail Location Decision Making
Bibliographic record
Abstract
Over recent years, the rapid growth of big data and associated analytical tools has provided unparalleled opportunities for retailers to enhance their location decision support activities. To date, there is a lack of research that looks at how retail firms are leveraging such innovation in data and technology. Based on an online survey conducted with Canadian retail location decision makers, this article examines the current state and evolution in (1) the type and scale of location decisions that retail firms undertake; (2) the availability and use of technology and geospatial data within the decision-making process; and (3) the range of location research methods that are employed. The findings highlight that there has been a widespread increase in the availability and use of technology and geospatial data within the decision-making process. The range of analytical approaches has also expanded to include methods that work with new data sources, such as social media and mobile device location data. The adoption and development of big data approaches is also challenged, however, by factors such as information hoarding, a lack of understanding and buy-in from senior management, and a lack of skilled analysts who can manage and synthesize the big data.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.023 | 0.041 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".