Artificial intelligence and consumer loyalty in e-commerce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study is to analyze the role of artificial intelligence (AI) in online shopping and to identify consumers’ attitudes towards the use of AI technologies in online shopping. The study was conducted in Russia in 2023 using a survey in which 425 people participated. The analysis of the collected data was carried out using descriptive and nonparametric statistics. The results showed that Russian shoppers are active users of e-commerce. More than half of the respondents are loyal to AI tools, but their use is not a necessity for them. A high level of education has a positive effect on the assessment of the ease and value of using AI-based tools in shopping, women generally respond more positively to the use of AI, and more experienced online shopping users also tend to be more loyal and satisfied with the use of AI tools. This is the first time such a large-scale study has been conducted in Russia, and its results fill a gap in existing knowledge about the relationship between the use of AI tools in online shopping and customer satisfaction and loyalty to the marketplace. The results of the study can be useful for e-commerce companies to understand the role of AI in shopping and its impact on consumer behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it