A Multicriteria Model to Evaluate E- Commerce Websites Under thePerspective of the Customer
Bibliographic record
Abstract
E-Commerce is reaching a new stage of conspicuous and widely disseminated development also due to the recent challenges of COVID 19. Thus, the process of shopping includes a first decision problem quite critical to the success of any business: which site of e-commerce should be selected by the consumer? This means that the selection of such site should be studied in terms of the satisfaction of the consumer when using it for shopping compared with other competitive sites rather on its descriptive features. The list of studies comparing and evaluating the E-Commerce sites is quite long but almost no attempts have been made to model the satisfaction function of the consumer and so they are not particularly relevant to study the competitive choice of sites by the consumer. In this paper a new model of consumer satisfaction is proposed using an approach–TRIDENT-based on the Multi Attribute Utility Theory (MAUT) and the critical stage of estimating the weights of multiple criteria is solved using an original method OptionCards which avoids the shortcomings of more traditional surveys. The utility function describing the satisfaction function concerning websites of E-Commerce is estimated using the OptionCards method for a group of young professionals and university students confirming a similar importance assigned to the three major criteria. Such utility function was used to estimate the rating of the 14 major Portuguese websites of E-Commerce using the answers of a group of young professionals using E-Commerce and the overall score confirms their relative level of popularity. Keywords E-commerce; Customer satisfaction; Multicriteria model; Optioncards method.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".