An Analysis of Factors Affecting Intention to Purchase Products and Services in Social Commerce
Bibliographic record
Abstract
As a result of the popularity and growth of social networks, consumers often rely on recommendations and suggestions from online friends to make buying decisions. Through social commerce, people are driven from inefficient individual decisions toward collaborative decision-making with higher efficiency. In this paper, we study the factors that affect customer decisions on the purchase of recommended products and services in the context of social commerce. A total of 327 individuals on three popular social networks in Iran (i.e. Facebook, Cloob, and Telegram) were surveyed. Analysis of the results using the PLS-SEM approach revealed that:(1) Social commerce constructs has a positive effect on social support and relationship quality. (2) Perceived usefulness has a positive effect on relationship quality and intention to purchase. (3) social support has a positive effect on relationship quality and (4) relationship quality has a positive effect on intention to purchase.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".