Starting a relationship with AI! Exploring consumer's attitude towards digital human stylists
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
We investigate digital human stylists (DHS) - a new AI-powered form of personalised digital avatar - that can complement traditional retail services with innovative recommendations mimicking social presence. We explore the factors that can shape a positive attitude towards DHS, underpinning the interaction between consumers and machines. We employ a survey-based approach of collecting data from 357 respondents from 39 countries. Our results show that trust in technology, perceived enjoyment and usefulness lead consumers to develop relationships with DHS to access a personalised service. We also find that ease of use and social influence are important in leading consumers to fully engage with a DHS. This new form of human-computer machine emerges as a new AI service complementing traditional retail services. Our research is the first to provide empirical evidence about the factors that can improve consumers' acceptance of DHS, by making it complementary to traditional retail services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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