Explaining Individuals’ Usage of Social Commerce: A Data Mining Approach
Bibliographic record
Abstract
With the use of Web 2.0 technology, e-commerce is undergoing a radical change that enriches consumer involvement and enables a better understanding of economic value. This emerging phenomenon is known as social commerce. Social commerce (s-commerce) presents a new alternative for consumers to search for and find information about products they are seeking to buy. In spite of its universality, the adoption of this burgeoning technology is affected by several factors. This research project is an initial attempt to explore individuals’ intention of s-commerce usage through the data mining approach. The data was collected via a web-based questionnaire survey of 360 social network site (SNS) users in Jordan. Data mining techniques were then used to analyze the collected data in order to figure out what group of features is best for predicting s-commerce adoption among SNS users. The results showed that data characteristics related to gender, monthly income, civil status, number of connections, and prior online shopping experience are key factors in the classification process. The findings may assist researchers in investigating social commerce issues and aid practitioners in developing new s-commerce strategies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".