Voice commerce a live commerce jako nové trendy v B2C e-commerce
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
The B2C e-commerce market had to face recently to completely new challenges. The development\nof information and communication technologies, COVID-19, changes in consumer behavior, and other\nexternal factors have caused significant changes to the online environment. If sellers wanted to stay on the\nmarket, they had to fast adapt to the changing market environment. Some sellers have therefore decided to\nuse the pandemic period to their advantage and seize new market opportunities. This paper aims to introduce\nvoice commerce and live commerce as new trends in B2C e-commerce, identify the advantages, risks, and\nbarriers of using voice commerce and live commerce, and inform how these trends are currently perceived\nby consumers in the Czech Republic. To achieve this aim, an online questionnaire survey was implemented\nin the first quarter of 2022, in which more than 600 respondents participated. Based on the results of the\nquestionnaire survey, it was found that respondents in the Czech Republic do not use voice commerce and\nlive commerce. Nowadays, voice commerce and live commerce are used mainly in the USA and China, where\nseveral multinational companies have already decided to implement these trends in their practice. We can\nassume that these trends will also penetrate the Czech Republic.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.028 |
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