Exploring Impact of E-Marketplace Reputation and Reference Group on Trust of E-Marketplace
Bibliographic record
Abstract
While electronic commerce radically accelerates business transactions and interactions, e-marketplace providers constantly face the challenge in attracting critical mass of participants. Previous studies suggest that trust is the prerequisite to online transaction and interaction. The purpose of this research is to explore relational factors in influencing e-vendors’ decision in terms of developing long term business relationship with e-marketplace. Based on trust-commitment theory, this research proposes e-marketplace reputation and reference groups as antecedents for developing e-vendors’ trust in e-marketplace providers and consequently lead to relationship commitment. Based on responses of 162 active e-vendors, the result reveals that e-marketplace reputation and reference groups contribute to trust of e-marketplace providers. Furthermore, e-vendors’ trust in e-marketplace providers has an impact on long-term business relationships. The research findings provide actionable guideline for e-marketplace provider.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| 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".