Mixed reality in U.S. retail: A review: Analyzing the immersive shopping experiences, customer engagement, and potential economic implications
Bibliographic record
Abstract
This study aims to explore the transformative impact of Mixed Reality (MR) technologies in the U.S. retail sector. It focuses on analyzing how MR reshapes shopping experiences, enhances customer engagement, and influences the economic landscape of retail. The methodology encompasses a comprehensive literature review, utilizing academic journals, conference proceedings, and industry reports. The search strategy involved keyword searches and manual screening, with inclusion and exclusion criteria set to filter relevant literature. The selection criteria prioritized recent studies to capture the latest trends in MR technology. The key findings reveal that MR technologies have evolved significantly, offering immersive and interactive shopping experiences that revolutionize customer engagement and satisfaction. The economic implications of MR in retail are profound, indicating substantial market growth and financial opportunities for retailers. However, the adoption of MR also presents challenges, including the need for integration into existing retail models and the development of user-friendly interfaces. The study also highlights the importance of regulatory frameworks and standardization in the successful implementation of MR technologies in retail. In conclusion, MR technologies hold great potential for the retail sector, offering innovative ways to engage customers and enhance their shopping experiences. However, realizing these opportunities requires overcoming various challenges, including adapting financial strategies and addressing infrastructure needs. As MR continues to evolve, it is poised to play a pivotal role in shaping the future of the retail sector. The study underscores the need for ongoing research to fully understand and leverage the potential of MR in retail.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".