Augmented reality in marketing: Conceptualization and systematic review
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
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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