Are You an E-consumer? A Case Study on Finding Factors Impacting Consumers' Purchase Behaviour and Their Willingness to Pay on Average on E-Commerce Platforms in Malaysia
Bibliographic record
Abstract
Online shopping has become phenomenal in this modern day. Moreover, the world was hit by the enormous Covid-19 pandemic. In the third quarter of 2021, e-commerce platform growth soared up to 17.1% and it also elevated our country’s GDP. People have become more comfortable buying things through the e-commerce platform rather than doing physical buying. These platforms unintentionally affect e-consumer behaviour. This research aims to study consumer behaviour on how much money a person spends on average on e-commerce platforms mainly for online shopping. A total of 150 consumers are surveyed via Google form. We intend to find out if price, customer satisfaction, information quality, and convenience can affect consumers’ purchase behaviour. The result of these findings shows that consumer purchase behaviour is directly related to the price, customer satisfaction with their buying experience and information quality. Consumers are not affected by the convenience variable as deeply as they do by other variables tested in this study. As the study is tested on a survey, the data collected may not be truly accurate.
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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 | 0.002 |
| 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".