Use of artificial intelligence system to predict consumers’ behaviors
Bibliographic record
Abstract
In online shopping enterprises, AI technology has been widely used to provide accurate and fast personalized consumer services. This research demonstrates the use of AI technology in the e-commerce business, specifically online enterprises, to determine different effects. The study was conducted in Jordan and involved about 230 participants. The study evaluated different impacts of AI, such as e-payment and stimulating consumers' sentiments. The study used the Stimulus–Organism–Response model (SOR) empirical model, which states that the examination of human processes differs from that of the machine assessment. The model classified the AI technology experienced by the customers' when they visit online to do purchasing. Online purchasing behaviors can be influenced by insight, accuracy, and interaction experience. Also, the perceived value was used as a mediating variable from the prospects of perceived hedonic and utility value. The research integrated empirical research models such as SEM and SPSS to analyze the data on the effects of three-dimension. The results indicated that the AI technology accuracy, interactive experience, and insight significantly affected customers' perceived hedonic and utilitarian values.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".