Online sales system and organization outcome
Bibliographic record
Abstract
The aim of this study is to identify the link between online sales systems, infrastructure, ease of use & information accuracy in improving Jordanian restaurants’ call centers' performance effectiveness (JRCCPE). In order to achieve the study's objectives, a questionnaire was conducted for measuring the link between online sales systems (OSS), infrastructure, ease of use & information accuracy in improving JRCCPE. The study sample was selected by distributing 220 questionnaires to all employees of Jordanian restaurants’ call centers (JRCC) from the set of employees working in an online sales system where 173 were retrieved. Aiming to answer the study questions and test hypotheses, the researcher extracted the means and standard deviations to apply the multiple regression equation. Accordingly, the study reached many results, showing a statistically significant effect for using the OSS, infrastructure, ease of use & information accuracy in improving JRCCPE. The study suggested that JRCC seeks adding various characteristics of editing, deleting, copying, and setting the time on the basis of text messaging through such networks as well as the need to conduct marketing studies in order to enable companies to achieve the customers' wishes in a method matching their expectations.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".