Augmented reality in marketing: Conceptualization and systematic review
Bibliographic record
Abstract
Abstract This systematic review of the literature focuses on the use of AR and its impacts in the marketing area. It provides a multidisciplinary, up‐to‐date synthesis of the literature and an exhaustive classification of AR. Through the use of the SPAR‐4‐SLR protocol, 148 articles were selected for analysis. The study has three main objectives. First, it reports the key characteristics (distribution by year, publication outlets, etc.), theoretical models, and methodologies used in this research domain. Second, it suggests a classification of the types of AR according to their triggers and the object of the augmentation (self vs. external). Third, it proposes a framework that presents (1) the AR features and attributes and the AR use experience; (2) the cognitive, affective, and social mediators; and (3) the outcomes of these experiences. Key moderators (types of AR, types of products, individual characteristics, etc.) are also discussed. Using the TCCM framework (theories, context, characteristics, and methodologies), this study offers several future research avenues and highlights the importance of considering the effects of the different types of AR. Finally, it offers pointers for managers on how to develop efficient AR solutions and how these can be used to reduce a company's carbon footprint.
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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 | 0.041 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.037 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".