E-WOM Adoption and Sharing Behavior in Social Network Sites: The Impact of Engagement in SNSs
Bibliographic record
Abstract
Social network sites (SNSs) is becoming a credible source of online information. Despite the increasing use of social networks in message persuasion literature, there is still a need for investigating the role it can play in users’ adoption of online information and its impact on users' sharing behavioral intention of this online information. This research utilizes the peripheral route in elaboration likelihood model to investigate the impact of source credibility on engagement in SNSs and on e-WOM adoption, the impact of engagement in SNSs and recommendation rating on e-WOM adoption, and the impact of e-WOM adoption on sharing behavioral intention.The findings suggest that factors, that are not directly related to the online message content, like source credibility, recommendation rating, and online users' engagement in SNSs groups, positively impact online information adoption by SNSs users, and their sharing behavioral intention of this online information. The study is finally concluded by suggesting the theoretical implications, and by providing strategies for firms to adjust their online activities in order to succeed in improving their customers’ engagement, and their customers' adopting of these firms' products and services’ information.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".