The online flow and its influence on awe experience: an AI-enabled e-tail service exploration
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
Purpose The study applied the stimulus–organism–response (S–O–R) framework to investigate the influence of flow elements (e.g. perceived control, concentration and cognitive enjoyment) on artificial intelligence (AI)-enabled e-tail services in evoking awe experience in online fashion apparel context. Design/methodology/approach Data of 739 active users of online fashion retail shoppers were collected using Amazon Mechanical Turk (MTurk). Partial least square-structural equation modeling was used for analysis. Findings This study suggested the relevance of AI-enabled services in evoking flow and stimulating the customers' awe experience in online fashion shopping. Practical implications The use of AI could help online fashion retailers to improve the experiential elements by using stimuli that evoke feelings of vastness, novelty and mysticism. Originality/value The study offers insights about the relevance and applicability of AI in enhancing the flow elements and awe experience on online fashion apparel shopping in an emerging economy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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