Bibliographic record
Abstract
Online interactions pave the way for new streams of collaborations and support among connected users of social networking sites, and this collaboration is leveraged for business purposes. The purpose of this paper is to contribute to a growing body of research on social commerce by studying individuals’ behavior from the consumer perspective of information technology innovations. By adopting “social support” and the technology acceptance model as a theoretical base, this study used a web-based questionnaire survey to collect data from 325 users of SNSs in Jordan. The data were then analyzed using Statistical Package for the Social Sciences regression. Jordan was selected because it is a country that has reported high SNS usage compared to other countries. The main factors that were identified as playing a significant role in individual adoption of social commerce were social commerce construct and perceived usefulness. This study did not find enough evidence that perceived ease of use and perceived information quality were a significant determinant of social commerce adoption. These findings have important implications and value for the academia, businesses managers, and social media specialists in terms of formulating better strategies for handling social commerce effects on businesses. For social media specialists, using the research model in this study can assist in increasing their understanding of why some individuals choose to adopt social commerce services. Additionally, business managers may need to improve their interaction with SNS users and develop a better understanding of how consumers use social commerce in the purchasing decision process. Based on our review of the existing scientific literature on social commerce, few empirical studies have been conducted to scientifically evaluate and explain the usage behavior of social commerce in Jordan. This paper contributes to the continuing research in social commerce adoption and diffusion in the individual context.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".