What influences news learning and sharing on mobile platforms? An analysis of multi-level informational factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of the study is threefold: first, to identify what factors influence mobile users' willingness of news learning and sharing, second, to find out whether users' learning in the news platforms will affect their sharing behavior and third, to access the impact of sharing intention on actual sharing behavior on the mobile platform. Design/methodology/approach This study proposes an influence mechanism model for examining the relationship among the factors, news learning and news sharing. The proposed mechanism includes factors at three levels: personal, interpersonal and social level. To achieve this, researchers collected data from 474 mobile news users in China to test the hypotheses. The tools SPSS 26.0 and AMOS 23.0 were used to analysis the reliability, validity, model fits and structural equation modeling (SEM), respectively. Findings The findings indicate that news learning on the mobile platforms is affected by self-efficacy and self-enhancement. And news sharing intention is influenced by self-efficacy, interpersonal trust, interpersonal reciprocity, online community identity and social norms positively. News sharing intention has a significant effect on news sharing behavior, but news learning has an insignificant relationship with new sharing. Originality/value This study provides practical guidelines for mobile platform operators and news media managers by explicating the various factors of users' engagement on the news platforms. This paper also enriches the literature of news learning and news sharing on mobile by the integration of two theories: the social ecology theory and the interpersonal behavior theory.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it