Factors Influencing Employees’ Intention to Use Cloud Computing
Bibliographic record
Abstract
This paper aims to investigate the effects of perceived ease of use, perceived usefulness, self-efficacy, trust, job opportunity, top management support, competitive pressure, and regulatory support on employees’ behavioral intention to use cloud computing. Data was collected by means of self-administrated questionnaire containing 25 items from 205 employees’ working in three, four, and five star hotels. Multiple regression analysis was conducted to test the research hypotheses. Results of the current study revealed that there are significant impacts of four independent variables (i.e. job opportunity, top management support, competitive pressure, and regulatory support) on behavioral intention (BI) to use cloud computing; whereas four independent variables (i.e. perceived ease of use, perceived usefulness, self-efficacy, and trust) have no significant impact on BI. The results of T-test also showed that there is a significant difference in the impact of BI to use cloud computing in favor of gender. On the other hand, the results of ANOVA’s test showed that there is no significant difference in the impact of BI that can be attributed to age, educational level, and personal income; whereas a significant difference found in favor of work position and hotel’s classification. In light of these findings, implications to both theory and practice are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".