Effect of Smart Attributes of SPA on Intention to Use of Blockchain System - Based on Securities Lending of Small and Medium Construction
Bibliographic record
Abstract
Background/Objectives: This study investigates the effects of Smart Attributes of SPA on the perceived usefulness and Intension to Use of blockchain systems. To achieve the purpose of the study, we analyzed the effect of Smart Attributes of SPA on Perceived Usefulness and Intension to Use of blockchain systems using TAM.Methods/Statistical analysis: The data for this study were collected through a survey. We conducted a survey of users from June to August 2019. A Likert 5-point scale was used. The survey targeted Small and Medium Construction staff and using the Blockchain system. There were 719 valid cases. SPSS 24 and AMOS 22 were used for the analysis of collected data. The analysis methods were technical analysis, frequency analysis, correlation analysis, reliability analysis, factor analysis, CFA, Structural Equation Model.Findings: The findings can be implied in three ways: First, when designing a blockchain-based securities lending system, the system should be designed according to the user's purpose, that is, the 'Usability' attribute should be included in the function. Second, 'Customization' should be considered when designing the system. In other words, the needs of specific customers should be fully understood and reflected in the system design. Third, when designing a system, the 'Connectivity' attribute must be fully considered. It is to be able to connect social and device-to-device networks and all the connectable objects.Improvements/Applications: When designing a blockchain-based securities lending system, the four attributes of Smart Attributes of SPA should be fully reflected. In other words, if 'Usability', 'Customization', and 'Connectivity' are reflected as much as possible, it will contribute to the early settlement of the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".