Cybersecurity and Social Media Networks for Donations
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
Previous studies have focused on the impact of the theory of reasoned action (TRA)'s components on the behavioral intention to donate more. However, whether the mediation roles of social media-presence and cybersecurity affect this impact is unclear. This paper extends the TRA with trust, commitment, and loyalty to explore the integration of cybersecurity and social media-presence into the behavioral intention to donate more. Data were collected from 315 donors to nonprofit organizations and analyzed using partial least squared (PLS) methods. The results indicate that social media-presence positively influences the donor commitment towards the behavioral intention to donate more. However, social media-presence does not increase donor trust and loyalty toward the behavioral intention to donate more. Furthermore, cybersecurity increases donor trust and loyalty toward the behavioral intention to donate more. However, cybersecurity does not influence donor commitment toward the behavioral intention to donate more. Theoretical and practical contributions are offered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it