Bibliographic record
Abstract
As the implication of digital economy and the result of the technological innovation associated with change, new or transformed roles have been added to the online supply chain. The release of Industry 4.0 has brought about enormous changes, not only as regards to the increase of participants in the online marketplace, but also in terms of changing job responsibilities and job schedules. Design/methodology/approach—This article defines the most important trends on the online markets based on faculty literatures, on e-commerce and e-marketing conferences and on in-depth interviews (qualitative research method) with Hungarian experts. Furthermore, the article outlines how difficult is it to include these processes for webshop operators, according to their experiences. Findings—The article presents some results of e-commerce trends for which direction the online market might evolve and what sphere/division must focus on to exploit the opportunities of web. I tried to assess the difficulty of starting a new webshop during my research (on scale of 1–6). The innovative nature of the research is that it intends to illustrate Hungarian trends based on the processing of the professional literature of the subject. Originality/value—Much of the discussion is based on Web 4.0 and Industry 4.0. This article pushes a few new and high priority trends and to suggest the evolution of the Internet with a reason which is based on future consumer society.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".