Consumer Adoption of Cloud Computing Services in Germany: Investigation of Moderating Effects by Applying an UTAUT Model
Bibliographic record
Abstract
Cloud computing services have been growing rapidly in recent years, with Dropbox, Apple iCloud and Google Drive being amongst the most established. The main purpose of this study was to investigate whether there were moderator effects for cloud usage. Therefore, Gender, Age, IT Experience, and Usage Context were included as moderating variables. Theoretical backbone was an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT). Herby, we investigated the UTAUT-determinants Performance Expectancy, Effort Expectancy, Facilitating Conditions, and Social Influence plus incorporated external factors Attitude towards Use, Perceived Security Risks and Perceived Trust into the research model. Data was conducted via a web survey amongst internet users in Germany during October 2014. A total of 2135 panelists started the questionnaire with 2040 finishing. Data basis for the analyses was the number of cloud computing users (n=1047). Operationalization was tested using confirmatory factor analyses and causal hypotheses were evaluated by means of structural equation modeling. In addition, the critical ratios approach was applied to investigate moderating effects. Firstly, the results show that extended UTAUT is a robust research model. In detail, Social Influence, Performance Expectancy Effort Expectancy, and Perceived Security Risks were shown to significantly impact Attitude towards Use cloud services. The combination of all constructs used accounted for 67.2% of the variances observed in users’ attitude and 82.4% in users’ intention to use cloud services. Secondly, we found the moderating effect for all factors investigated, particularly gender and IT experience were shown to significantly moderate attitude and behavioral intention to use cloud services.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".