The Influence of Consumer Behavior on Consumer Purchase of Fashion Products(Case Study of Generation Z in Bali)
Bibliographic record
Abstract
In Indonesia, in terms of population, Generation Z is the largest.The number reached 72.8 million (27 percent) of Indonesia's 267 million population in 2019, and the results of Lokadata.id'sprocessing of the 2019 National Socio-Economic Survey (Susenas) revealed that of the 47 million millennial internet users, as many as 17 percent or around 7.8 million of them like shopping online, whether it's buying goods or services.With approximately 4.7 hours per day to access the internet, the activity for conducting online-based transactions has also increased.In essence, various past and present factors influence consumers.Future decisions will be influenced by current behavior.If a consumer is satisfied with the product he bought, then he tends to buy it again.But if you are not satisfied with a certain brand product.Consumer behavior can be influenced by several factors such as social factors, personal factors, and psychological factors.Tokopedia is an Indonesian E-commerce company with a mission to achieve economic equality digitally.Since its founding in 2009, Tokopedia has been a pioneer in digital transformation in the country.Quoted from IPrice, Tokopedia remains the most visited E-commerce in the 3rd quarter of 2021.The purpose of this study is to determine consumer behavior towards purchasing decisions for fashion at Tokopedia.The research method used is linear regression test using SPSS.The result of this study is that consumer behavior has a significant effect on purchasing decisions for fashion products at Tokopedia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".