Analysis of factors influencing consumer’s purchase intention in social commerce: The mediator role of trust
Bibliographic record
Abstract
It is considered that determining the factors influencing the purchase intentions of customers in the field of social commerce by taking into account the cultural structures specific to each region can give highly valuable feedback to companies in the commercial field where competition has considerably increased. To this end, in the present study, it was aimed to propose and test out a structural model for the factors of social commerce constructs, social influence, habit and trust, which affect the purchase intentions of consumers. The study group of the research consisted of 447 Facebook users who live in North Cyprus. The scale form used as the data collection tool in the study consisted of 6 parts. In the first part of the scale, closed-ended questions were asked to determine the socio-demographic characteristics of the participants. The other parts of the scale consisted of 4 items about social commerce constructs, 4 items about habit, 3 items about social influence, 4 items about trust and 3 items about purchase intention. The reliability coefficients for the scales ranged between 0.79 and 0.92. These results indicated that the reliability coefficients were at the desired level. Socio-demographic variables were summarized using descriptive statistics, frequency and percentage distribution. First, normality test analyses were performed on the scales, and it was concluded that the data showed a normal distribution (kurtosis and skewness values varied between +2 / -2). Following the normality test, the measurement model was tested. Within the framework of a structural model proposed regarding the factors influencing the purchase intention of customers who shop through social commerce, it was concluded that habit, social influence and social commerce constructs had a significant effect on purchase intention. It was also concluded that habit and social commerce constructs significantly influenced purchase intention through trust.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".