Users’ attitude on perceived security of mobile cloud computing: empirical evidence from SME users in China
Bibliographic record
Abstract
Purpose The purpose of this paper is to rank the users’ attitude on major components of mobile cloud computing (MCC) security and investigate the degree of impact of these components on MCC security as a whole. Design/methodology/approach Hypotheses were evolved and tested by data collected through an online survey-questionnaire. The survey was administered to 363 users from Chinese organizations. Statistical analysis was carried out and structural equation model was built to validate the interactions. Findings The eight components of MCC security in the order of importance are as follows: mobile device related, user identity related, deployment model related, application-level security issues, data related, virtualization related, network related and service delivery related. The empirical analysis validates that these security issues are having significant impact on perceived security of MCC. Practical implications Constant vigilance on these eight issues and improving the level of user awareness on these issues enhance the overall security. Social implications These issues can be used for designing and developing secured MCC system. Originality/value While several previous research has studied various security factors in the MCC security domain, a consolidated understanding on the different components of MCC security is missing. This empirical research has identified and ranked the major components of MCC security. The degree of impact of each of these components on overall MCC security is identified. This provides a different perspective for managing MCC security by explaining what components are most important.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".