From Counters to Self-Checkouts: A Systematic Review of Factors Affecting Operational Efficiency in Retail Automation
Bibliographic record
Abstract
Abstract— The Self-Checkout System (SCS) is a key element of retail automation, designed to improve operational efficiency and enhance customer convenience. This systematic literature review synthesizes insights from 16 peer-reviewed Q1/Q2 studies published between 2020 and 2025, leading to the identification of four critical factors influencing efficiency: technological design, user behavior, organizational preparedness, and workforce impact. The findings suggest that a combination of advanced perception technologies (e.g., AI vision, depth cameras), user-centered interface design, process reengineering, and comprehensive staff training, including cross-training for hybrid support roles, plays a pivotal role in shaping outcomes. Recent research also highlights emerging adoption drivers such as reduced stigma around sensitive purchases, increased privacy awareness, and shifting dynamics of customer empowerment. Efficiency is not an inherent attribute of the system alone, but rather the result of interactions among technology, users, and institutional contexts. Retailers and policymakers are encouraged to pursue integrated design approaches that holistically align technological innovation, store operations, and human labor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".