Mobile Curated News Readers’ Intention to Read Full-length Articles: Focusing on Heuristic and Systematic Factors
Bibliographic record
Abstract
Background: Mobile curated (shortened) news is now increasingly popular. Each curated news article is accompanied by a link that readers can click to read the full-length article on the news provider’s website. To date, little empirical research has examined the factors that influence mobile newsreaders’ intentions to read full-length articles from their curated short forms. To address this gap, this study employs the Heuristic-Systematic Model (HSM) of information processing and examines: 1) how the heuristic and systematic factors of online curated news influence newsreaders’ intention to read full-length articles; and 2) how newsreaders’ language proficiency levels moderate these effects. Method: A survey was conducted with 195 participants recruited from Amazon MTurk. The participants first read a sample curated news item developed for this study and then filled out the questionnaire to measure the variables of interest. To test the hypotheses, a partial least square method was used with SmartPLS 4.0. Results: Our results showed that people have stronger intentions to read full-length articles when they perceive the curated news to have highly relevant information, an attractive title, a credible source, or less understandable information. Furthermore, newsreaders' language proficiency level has a moderating impact on some of these effects. The effects of these factors can be attributed to how they influence newsreaders’ reading behaviors and their heuristic or systematic processing of the curated news. Conclusion: The curated news can be properly designed to motivate newsreaders’ intention to read full-length articles. The findings contribute to the body of knowledge on HSM and mobile news adoption. The findings also provide mobile-curated news service providers and online full-length news media platforms with valuable practical implications.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".