Digital Transformation in Retail: Innovations in Shopping Carts and the Impact on Customer Behavior and Store Operations
Bibliographic record
Abstract
This research explores the impact of digital transformation and emerging smart cart technologies on customer behavior and store operations within the retail sector. The study evaluates traditional and motorized shopping carts, AI-assisted carts, and in-store technological innovations designed to improve accessibility, safety, and the overall shopping experience. Using a mixed-methods approach with customer surveys, store visits, and interviews with retail managers in Canada, the project identifies key trends in consumer preferences, operational efficiency, and the business feasibility of integrating advanced shopping cart technologies into retail environments. Findings show that while traditional carts remain widely used, there is strong potential for smart and motorized carts to enhance customer satisfaction, support individuals with mobility challenges, and improve in-store efficiency. This research contributes to the growing field of retail innovation by providing insights for retailers, technology developers, and policymakers aiming to modernize retail infrastructure and design customer-centric service systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".