Antecedents of user attitude towards e-commerce and future purchase intention
Bibliographic record
Abstract
This study attempts to analyze the antecedents of e-commerce user behavior and their effect on future purchase intentions. Theoretical exploration shows that the antecedents used in the behavior of e-commerce users are perceived self-efficacy, perceived ease of use and perceived usefulness. The study uses the behavior of e-commerce users as a mediating variable. The study was also conducted using a quantitative method, by distributing questionnaires to 250 e-commerce users in Indonesia. The analysis technique used is Structural Equation Modeling (SEM) with SmartPLS software. The results show that perceived self-efficacy had a positive effect on perceived ease of use and perceived usefulness. These three antecedents in turn have a positive and significant effect on future e-commerce shopping interest by using mediation of user behavior. The results are theoretically useful for deepening Technology Acceptance Model exploration by estimating future buying behavior and interest. Practically, this study encourages e-commerce platform website developers to increase the ease and usefulness to increase the positive behavior of users in purchasing products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".