Exploring the Application of Both Internet of Things and Artificial Intelligence under the Omni Channel from the Perspective of Drama Theory
Bibliographic record
Abstract
The ability to establish a single business platform with a unified front-end and back-end system to deliver the experience becomes the key factor in the Omni-channel. This study will adopt the transactions between the dealer and the consumer to match to a drama performance and quote actors as dealers, the audience as consumers, and the scene as physical stores and virtual access to the shopping environment. We applied purposive sampling towards consumers, who have had experiences in purchasing at chained apparel retail stores through either physical or virtual access, having collected 407 valid questionnaires in total. This study used the method of context simulation, and divided the consumers into two different sample groups based on different power influence. This study suggests to in the future the retailers use the consumer situation created by the Internet of Things and artificial intelligence to have consumers immersed in an environment that stimulates their purchase intention, as well as to arouse consumers' inner need, in order to increase the intention, frequency, and promotion price of consumers' purchasing behaviors.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".