Investigating of the role of cybercrime and e-brand trust on purchase interest of e-commerce platforms
Bibliographic record
Abstract
In this digital era, the use of the Internet in business transactions through e-commerce platforms has created many conveniences, the development of Internet technology has given rise to crimes called cyber crime or crimes through the Internet network. The purpose of this study is to investigate the relationship between cybercrime and purchase intention on e-commerce platforms, and the relationship between e-brand trust and purchase intention on e-commerce platforms. This research method is a quantitative method to analyze the relationship between variables, research data was obtained by distributing online questionnaires through social media platforms, and questionnaires containing statement items were designed using a Likert scale of 1 to 5. The respondents of this study were 535 consumers who had shopped online on e-commerce platforms determined by the simple random sampling method. Data analysis used the PLS Partial least squares SEM (PLS-SEM) technique. In the study, the outer model which is called the measurement model has a meaning between indicators connected by other variables. The measurement model of convergent validity, discriminant, and reliability was used. The standard loading factor value in the concurrent validity test must be > 0.7 or greater than the established criteria. The same applies to the discriminant validity test, which uses a larger value for the loading factor. The construct reliability test uses Cronbach's alpha and the composite reliability value. The hypothesis testing uses partial least square (PLS) which is the inner model test result, namely the R-square output, path coefficient, or t-statistic. The convincing t-statistic result > 1.96 is that Ha is accepted and Ho is rejected. If the probability number (p-value) <0.005 is included, then Ha is accepted. If the p-value is <0.05 (or 5%), the t-statistic is > 1.96, and the beta coefficient is positive, then Ha can also be accepted. The results of the analysis show that cybercrime has a positive effect on consumer purchasing intentions on e-commerce platforms and e-brand trust hurts consumer purchasing intentions on e-commerce platforms.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".