The Role of Augmented Reality Experiences in Consumers' Purchase Intention Toward New Products
Bibliographic record
Abstract
ABSTRACT Augmented reality marketing (ARM) is rapidly emerging as a critical marketing channel to enhance consumer experiences. In the past, researchers have focused on ARM's varied antecedents, mechanisms, and outcomes. However, this study aims to deepen the knowledge by exploring how ARM experiences can drive unique outcomes. Based on the TEAV model of consumption experience, we followed a mixed methods approach, utilizing the data from 22 interview participants and 711 survey respondents, analyzed through PLS‐SEM. The findings indicate that ARM facilitates cognitive, affective, and co‐creation experiences, influencing the purchase intention toward new variants of familiar products. The study's insights establish the role of technology in enabling new experiences and influencing consumption behaviors. The findings expand the academic understanding of the unique outcomes of ARM experiences. Brands, retailers, and marketers can use this research to boost revenue and image by encouraging ARM‐enabled experiences and purchases.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".