Assessing the Quality of E-Services Software Using Artificial Intelligent Techniques
Bibliographic record
Abstract
Computer applications, termed e-services, are being developed to offer efficient access to services, by electronic means. Quality of e-service is one of the major aspects that play a key role in the success or failure of online organizations. It improves the competitive advantages and mends the relations with clients and increases their gratifications. E-services are becoming progressively pervasive, and this development has been supplemented by augmented professional interest in measuring and handling online service quality. This interest is also echoed in a large number of academic studies. In spite of this, there is a slight agreement about the dimensions and antecedents of perceived e-service quality. Assessing e-service quality come to be industry or context dependent in which, it may raise problems to create a global measure. To tackle this issue, several website quality models have been established, with a congruently huge literature. This paper provides a review of this literature as well as recommends a new vision to develop measurement scales for e-service quality based on artificial intelligent technique.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".