Augmented reality magic mirror in the service sector: experiential consumption and the self
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 This paper examines what the use of an augmented reality (AR) makeup mirror means to consumers, focusing on experiential consumption and the extended self. Design/methodology/approach The authors employed a multimethod approach involving netnography and semi-structured interviews with participants in India and the UK ( n = 30). Findings Two main themes emerged from the data: (1) the importance of imagination and fantasy and (2) the (in)authenticity of the self and the surrounding “reality.” Research limitations/implications This research focuses on AR magic makeup mirror. The authors call for further research on different AR contexts. Practical implications The authors provide service managers with insights on addressing gaps between the perceived service (i.e. AR contexts and the makeup consumption journey) and the conceived service (i.e. fantasies and the extended self). Originality/value The authors examine the lived fantasy experiences of AR experiential consumption. In addition, the authors reveal a novel understanding of the extended self as temporarily re-envisioned through the AR mirror.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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