FACTORS AFFECTING CLOUD COMPUTING ADOPTION AMONG UNIVERSITIES AND COLLEGES IN THE UNITED STATES AND CANADA
Bibliographic record
Abstract
Many colleges and universities around the world are adopting cloud computing resources and services.The benefits of cloud computing for institutions and students include factors such as mobility, scalability, security, availability, interoperability, and end user satisfaction in the use of software applications and other computing resources.However, some institutions are not taking advantage of the services offered by the cloud computing paradigm.Using the technology-organization-environment (TOE) framework, the authors have proposed a research model to investigate the factors that determine the adoption of cloud computing by colleges and universities.A nonexperimental, cross-sectional, quantitative study was conducted in 2013 of 119 CIOs and IT managers in colleges and universities in the U.S. and Canada that have implemented, or were planning to implement, cloud computing environments.An online survey was used to gather data to test the relationship between the criterion variable (cloud computing adoption) and the predictor variables (relative advantage, complexity, compatibility, institutional size, technology readiness, perceived barriers, regulatory policy, and service provider support).The results of the logistic regression analysis indicated that complexity, institutional size, and technology readiness were statistically significant in determining cloud computing adoption.The predictor variables relative advantage, regulatory policy, and service provider support were not statistically significant.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".