Body Language of Sellers and Its Impact on Customer Loyalty
Bibliographic record
Abstract
The goal of this study was to ascertain, from the perspective of a sample of shoppers at significant commercial shopping centers in the city of Babylon, Iraq, the effects of sellers' body language, as expressed by its five dimensions (body posture, smile, physical appearance, eye contact, and personal space), on customer loyalty. The study data was collected using a questionnaire and an analytical method known as descriptive analysis was employed to achieve the study's goals. The study sample received 60 electronic surveys via social networking sites, 50 of which were suitable for statistical analysis, then utilizing the statistical program (SPSS), a number of statistical tests were used to assess the data. According to the study's findings, there is a favorable correlation between the five components of body language and customer loyalty. The results also showed that there are no statistical. differences in customer loyalty due to demographic factors (gender and educational level) and the presence of fundamental differences depending on the age factor. The researcher finally Providing a number of recommendations to researchers on the one hand regarding future studies and to commercial center owners on the other hand to gain customer loyalty and retain them.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".