The impact of changes in the marketing era through digital marketing on purchase decisions
Bibliographic record
Abstract
In online shopping, it is currently an alternative place for shopping where this method is starting to be widely used by consumers. Various online shopping applications have sprung up, but several factors that influence consumer shopping decisions of course vary. In this study, researchers examined the effect of the accepted model of technology (ease of use and usefulness) and risk variables on consumer decisions to shop at online stores. In this study, the researcher used four variables, sixteen dimensions where each dimension was represented by two indicators so that in this study there were thirty-two indicators which would later be changed in the form of questions to respondents. In this study, the population used is consumers who have shopped online and since the population is very large, then with the quota sampling method the researchers determine the number of samples as much as five times the number of indicators so that the number of samples is 160 respondents, which would later be processed with AMOS SEM analysis tools. From the results of this study, it was found that the variables of ease of use, usefulness and risk had a significant and significant effect on consumer decisions to buy online. And the ease-of-use variable also affects the usefulness variable. The usefulness variable is found to have the greatest influence on consumer decisions, then the risk variable and finally the ease of use.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".