Technological Advancements and Consumer-Centric Transformation: The Evolution of Online Shopping Platforms
Bibliographic record
Abstract
In today's rapidly evolving Internet era, online shopping platforms are increasingly adopting new technologies and innovative marketing strategies to optimize spending and increase profits. Integrating emerging advertising media has led to faster and more effective product promotion strategies, helping to spread products rapidly. However, using big data sometimes raises concerns about potential information leakage and privacy invasion, through which sellers can access customers' sensitive personal information. This study provides insight into how online shopping platforms utilize emerging technologies to improve the customer experience and increase sales and advertising effectiveness. By analyzing how new technologies such as virtual reality (VR) and augmented reality (AR) are transforming product presentation and shopping experience, this study reveals how online shopping platforms enable more immersive and interactive environments that allow customers to understand and experience products fully. Virtual fitting rooms and 3D simulation displays of products provide customers with a unique shopping experience that helps increase engagement and reduce uncertainty in virtual shopping. In addition, this study looks at the use of big data in online shopping and explores how it affects customer experience and advertising strategies. However, using big data raises questions about privacy and data security, with customers concerned that their sensitive information may be misused.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".