Investigating the adoption of digital Library by postgraduate students in Jordan: An Enhanced of UTAUT Model
Bibliographic record
Abstract
The current study aims to determine the factors influencing the intention to employ digital libraries among postgraduate students in Jordanian universities. The data were gathered through questionnaires in the format of Google Forms. The questionnaires were distributed to postgraduate students enrolled in Jordanian universities with digital libraries, who were recruited via purposive sampling. A total of 261 responses were received, with 67 deemed unsuitable for the study analysis and subsequently excluded. Resultantly, 194 valid questionnaires were finalized for data analysis. The SMART-partial least squares (PLS) software was utilized to conduct structural equation modelling (SEM) to test the study hypotheses. This study discovered that the variables, namely performance expectancy, effort, and facilitating conditions expectancy, were significantly and positively associated with the outcome variable, which is the intention to employ digital libraries. Meanwhile, social influence was revealed to be insignificant. Performance expectancy also significantly mediated the correlations between technological readiness and the intention to utilize and between online self-efficacy and the intention to utilize. The present study focused only on potential users among postgraduate students at Jordanian universities with digital libraries. The findings contributed valuable insights into the academic sector, especially library management to enhance the rate of digital library or e-library adoption at Jordanian universities. Digital library managers and policymakers could leverage the findings to design pertinent strategies that increase user engagement within digital library environments. The UTAUT model was demonstrated to be capable of predicting users’ intentions of employing digital libraries and corroborated the mediation role of performance expectancy on the associations between technological readiness and the intention to utilize and between online self-efficacy and the intention to use.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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".