The effect of the website attributes on online impulse buying: An empirical investigation of utilitarian and hedonic motivations
Bibliographic record
Abstract
Web based shopping has been the quickest developing channel of looking for over 10 years with deals developing at a yearly pace of 25%. One review connected properties of a site to drive purchasing conduct. Given the quick development of web-based shopping and the qualities of web-based shopping that energizes drive purchasing (for example open every minute of every day), drive purchasing on the web is probably going to be pervasive. Accordingly, this study plans to investigate the effects of site credits and persuasive variables on internet-based motivation purchasing. A self-directed poll was utilized to gather information for this review. The 400 respondents, in line with the required sample size, were people who have online buying experience in Thailand. The data analysis method is Structural Equation Modelling (SEM). The results of the study revealed that influence of web site attributes has a positive relationship with motivation factors. Moreover, motivation factors have a positive relationship with online impulse buying. Additionally, two components of motivation factors which are utilitarian and hedonic motivations, mediate the relationship between web site attributes and online impulse buying.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".