Exploring the critical success factors of s-commerce in social media platforms: The case of Jordan
Bibliographic record
Abstract
The unprecedented growth of social media imposed fierce competition on business companies. That is investors found new methods to expand their business activities, and, in turn, boost their revenues. While there has been a plethora of research done to examine the critical success factors of social commerce (s-commerce) in developed countries, there is a dearth of studies conducted in developing countries. Meanwhile, it has been evident that the significance of these factors may vary across cultures. Therefore, this study, following the social cognitive theory, aims to explore the critical success factors of s-commerce from the perspective of consumers in a developing country. To achieve that, this study utilized a questionnaire that sought information related to factors driving consumers' intention to purchase in s-commerce. Seven hundred and fifty-seven subjects completed the survey. Structural equation modeling techniques were utilized to analyze the data. The findings of this study show that trust in sellers, sociability, electronic Word-Of-Mouth (eWOM), perceived economic benefit, and informational fit-to-task positively influence the intention to purchase in s-commerce. In addition to that, it was found that sociability and eWOM positively influence consumers' trust in sellers. The findings of this study are expected to contribute to the theoretical and practical areas of s-commerce. They are expected to make a significant contribution to the literature on s-commerce adoption from the perspective of a developing country. From a practical point of view, the results of this study should help stakeholders in s-commerce in developing business strategies to better their competitive advantage, retain existing consumers and attract new ones, and, in turn, increase sales and profits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".