Bibliographic record
Abstract
This research is aimed to study the impact of virtual customer experiences on the tendency to social commerce in digi-kala sale site, Telegram and Instagram networks while investigating previous studies and collecting valid information.In order to identify variables, the results of previous researches were firstly studied by library method and their results were used to design research's conceptual model and field and questionnaire method was also applied to collect information.Applied research method is used in this study based on objective and non-experimental method; and correlation research method was used based on method of conducting study.This study also used cross-sectional research method for time interval of data collection.Set of convergent and divergent validity methods were used to test validity of research tool and Cronbach's alpha and combined reliability were used to test its reliability.Structural equation analytical approach was used to test research's model.This research was conducted through "Partial Lest Squares" collection method and Smart PLS.This research's statistical population included buyer and purchase users of digi-kala sale site and Instagram and Telegram selected through simple random method.The obtained results of this research show that all the research's hypotheses have been acceptable.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.877 | 0.805 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".