Toward <scp>D2A</scp>: Enhancing Luxury Fashion With Seamless and Immersive Phygital Customer Experiences
Bibliographic record
Abstract
ABSTRACT This study explores direct‐to‐avatar (D2A) strategies—where brands engage directly with consumer avatars in virtual environments—in the luxury fashion retail sector, focusing on enhancing customer engagement and creating a seamless phygital (physical + digital) experience through virtual immersion. Situated at the crossroads of physical and digital realms, this research assesses how immersive experiences contribute to perceived seamlessness and customer engagement within D2A and direct‐to‐consumer (D2C) frameworks. Employing a mixed‐method approach, including qualitative interviews with luxury fashion brand managers and three experimental design studies, this paper addresses the relatively underexplored effects of immersive experiences in marketing. Our findings reveal that immersion in D2A significantly boosts customer perceptions of channel seamlessness and engagement, with empowerment playing a key amplifying role in the seamlessness–engagement relationship. This paper enriches digital marketing strategies by highlighting the pivotal role of D2A in crafting engaging and unified customer experiences, offering luxury fashion marketing managers practical insights to thrive in the phygital landscape.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".