The road to learning “who am I” is digitized: A study on consumer self‐discovery through augmented reality tools
Bibliographic record
Abstract
Abstract Today, digital tools offer multiple avenues for consumers to learn about themselves. Self‐discovery or knowing “who am I” is fundamental to our everyday experience. However, there is a paucity of research that investigates the “how and why” of self‐discovery, made possible by technological advancements. Adopting the theoretical tenets of extended self, possible selves, storied selves, and the twin metaphors of self and identity, we follow a multimethod qualitative approach to explore consumer self‐discovery in the context of AR‐based makeup and grooming apps and filters. We establish a framework for AR‐facilitated self‐discovery by analyzing the data obtained via Netnography and 22 in‐depth interviews. The findings suggest that digital tools enable the discovery of previously unknown facets of the consumers' self‐concept. Theoretically, this study demystifies the process of technology‐enabled self‐discovery, which is related to better life decisions and consumer well‐being. Brands may apply these insights to inculcate the discovery components into the AR design, which can facilitate the adoption of new products. Finally, this study highlights the possible challenges to be avoided to ensure consumer well‐being while using AR‐enabled digital tools.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".