Barriers Affecting Higher Education Institutions’ Adoption of Blockchain Technology: A Qualitative Study
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
Despite the many benefits of blockchain technology in higher education, this technology is not widely adopted by Higher Education Institutions (HEIs). Therefore, instead of providing additional motives for adopting blockchain technology, this research tries to understand what factors discourage HEIs from merging blockchain with their procedures. The methodology used for this research is based upon qualitative research using 14 interviews with administrative and academic staff from the European Union (EU) and Canada. Our findings based on our empirical data revealed 15 key challenges to blockchain adoption by HEIs that are classified based on the technology, organization, and environment (TOE) framework. Theoretically, this study contributes to the body of knowledge relating to blockchain technology adoption. Practically, this research is expected to aid HEIs to assess the applicability of blockchain technology and pave the way for the widespread adoption of this technology in the educational field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it