Exploring Satisfaction with and Trust in Social Networking Sites through the Lens of Fan Pages: Uncertainty Reduction and General Systems Theory Perspective
Bibliographic record
Abstract
The purpose of this study is to examine social networking sites (SNS) users’ overall satisfaction with and trust in SNS, as related to their activities of visiting fan pages. We employ two theories—uncertainty reduction theory (URT) and general systems theory (GST)—to examine antecedents affecting overall satisfaction with and trust in SNS. Using a web-based survey, we analyzed 200 SNS users who follow at least one company’s fan page, and utilized seemingly unrelated regression (SUR) models to empirically test our hypotheses. Our findings indicate that uncertainty reduction strategies supported by URT are significantly associated with perceived usefulness of companies’ posts in their fan pages. In turn, perceived usefulness of those posts promotes more visits to the fan page. Finally, users’ perceived usefulness of fan page posts eventually accounts for the overall satisfaction with and trust in SNS. The implications and limitations are discussed at the end of this study.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".