Factors Affecting the Adoption of Cloud Computing in Saudi Arabian Universities
Bibliographic record
Abstract
Cloud computing is a novel trend in the sphere of information technology. This research sought to identify the factors that could influence the adoption of cloud computing in Saudi Arabian universities, and to comprehend the theories of technology adoption that apply to the uptake of cloud computing in organisations or for individuals, and how they pertained to the study reported here. Four categories of possible influencers were investigated: technological, organisational, environmental, and cultural. This mixed-methods study was based in extended TOE theory (technology, organisation, and environment) and the Hofstede model, which includes cultural factors. To accomplish the goals of the research, an exploratory study consisting of two phases, including qualitative (interviews) and quantitative (survey) was initiated to determine the importance of each of these influencers and the degree of influence. The results revealed that the factors of relative advantage, compatibility, top management support, readiness, competitive pressure, regulatory support, high masculinity, and high individualism have positive impacts on the adoption of cloud computing in this particular context. They also showed that security concerns, high uncertainty avoidance, and high power distance have negative impacts on cloud computing adoption. Unexpectedly, the results indicated that complexity, language and religion do not influence the adoption process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".