Factors that Influence Customers’ Buying Intention on Shopping Online
Bibliographic record
Abstract
On-line commerce through Internet is gaining attention from students today. The aim of this research is to studythe factors influencing student’s buying intention through internet shopping in an institution of higher learning inMalaysia. Several factors such as usefulness, ease of use, compatibility, privacy, security, normative-beliefs andattitude that influence student’s buying intention were analyzed. Respondents who were selected are studying ina public institution of higher learning in Penang, Malaysia. Based on theory of reasoned action (TRA), thetechnology acceptance model (TAM) concluded that there are two salient beliefs which are ease of use andusefulness. This theory has been applied on the study to adopt technology user different and has been emerged asa model in investigation to increase predictive power. Such theory was used in this study to explain students’buying intention on-line. Besides the ease of use and usefulness, others factors such as: compatibility, privacy,security, normative beliefs and self-efficacy are utilized at this TAM. The results support seven hypotheses fromnine. Compatibility, usefulness, ease of use and security has been found to be important predictors towardattitude in on-line shopping.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".