Theoretical Aspects and Methodological Approaches to Sales Services Quality Assessment
Bibliographic record
Abstract
The article defines trade service quality and proposes an object-oriented approach for its essence interpretation, according to which such components as product offering and goods quality, service forms and goods selling methods, merchandising, services and staff are singled out; a model of managing retail outlets trading service, which covers levels of strategic, tactical and operational management and is aimed at ensuring customers’ perception expectations, achieving sustainable competitive positions and increasing customers’ loyalty is worked out; a methodology of trade services quality estimation that allows to carry out a comparative assessment of cooperative retailing both in terms of general indicators and their individual components, regulate the factors affecting trade services quality and have a positive administrative action is developed and tested; the results of evaluation of the customers’ service quality in the consumer cooperative retailers, dynamics of overall and comprehensive indicators of measurement of trade service quality for selected components are given; the main directions and measures for improving trade services quality basing on quantitative values of individual indicators for each of the five selected components (product offering and goods quality, service forms and sale methods, merchandising, services, staff) are stated.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".