Voice commerce a live commerce jako nové trendy v B2C e-commerce
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".