Antecedents of blockchain adoption success: The mediating effect of user satisfaction to enhance project management information systems
Bibliographic record
Abstract
This paper examined how information quality within project management information systems is being improved through the use of blockchain technology. A conceptual framework was developed based on past findings and relevant theories. User satisfaction determinants affecting blockchain adoption success in Jordan were examined in this paper. A cross-sectional design and Random sampling technique were employed. Data collection involved the use of questionnaires. Data obtained from 393 responses were analyzed using AMOS software. The findings showed a significant impact of system quality, information quality, ease of use on user satisfaction, whereas service quality did not show a similar impact. Also, ease of use impacted blockchain adoption success, but user satisfaction did not. In addition, user satisfaction did not mediate the relationship between ease of use and Blockchain adoption success. Results had implications on blockchain use in Jordan. Several recommendations were proposed for forthcoming scholarly works and blockchain actual adoption, in Jordan especially.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".