Using Facebook® to Gain Academic Information: The Case of a Private Higher Education Institution in Malaysia
Bibliographic record
Abstract
Abstract: Logistics Department in one of the private higher education institution has encouraged its students to obtain information at the departmental level from its Facebook ® account named Logistics Student Association (LSA) Facebook®. This study applies two models namely the Information System Success Model (ISSM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Dimensions in the ISSM as well as in the UTAUT were used to investigate the perceptions from the logistics students pertaining to the LSA Facebook®. Data was gathered through questionnaires from 183 logistics students. Respondents provided their answers by perceiving the user satisfaction, system quality and information quality towards the behavioral intention to use the LSA Facebook®. Results indicated that system quality and user satisfaction are positively associated with the behavioral intention but not information quality. The standardized coefficients for user satisfaction was higher (β =.23) compared to system quality (β =.20) toward behavioral intention at p value <.001. The study implies that improving system quality and user satisfaction would yield high response from the users.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".