Entrepreneurship Strategy through Social Commerce Platform: An Empirical Approach Using Contagion Theory and Information Adoption Model
Bibliographic record
Abstract
Entrepreneurship is the readiness and ability of an organization, primarily a new business, to develop, organize, and conduct its business to make a profit despite uncertainties. Social commerce (s-commerce) assists consumers to buy products online. However, few studies have investigated the influence of entrepreneurship and online platform capability on consumers’ online purchase decisions. Academicians, researchers, and practitioners are also increasingly interested in understanding how the s-commerce environment influences entrepreneurship and online purchase decisions. Against this background, this study set out to examine this phenomenon. Using information adoption models and contagion theory as well as the input from the literature review, a theoretical model was developed. Such a model was tested with a factor-based PLS-SEM approach by analyzing the responses of 342 respondents. The results find that electronic WOM (e-WOM) credibility, predicted by online e-WOM content and platform credibility, and impacted by online reputation, could significantly influence consumers’ online purchase decisions. The study also finds that both positive and negative valance of eWOM as well as entrepreneurship significantly influence eWOM credibility, which in turn positively influences consumers’ purchase decisions when using online platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".