Relating Big Five Factor Model to the Acceptance and Use of On-line Shopping
Bibliographic record
Abstract
The main purpose of this paper is to determine the influence of the Big Five personality traits on consumers’ online shopping performance and perceptions of shopping experience. Building on previous research, it was found that personality characteristics shape an individual’s motivation, goals, and perception, thereby providing criteria to evaluate external stimuli and affect performance. The influence of personality traits on a consumer adopting online purchases as well as his acceptability and adaptability with the said medium is assessed. The paper consists of theoretical and research aspects. The first part encompasses theoretical insights into the secondary research regarding personality traits while the practical part presents the methodology and primary research results. In the study, research goals as well as previous findings and primary research results, corresponding hypothesis were set and confirmed. Inter variable correlation analysis has been performed to test the hypothesis followed by a regression analysis. The results potrayed respondents’ consistency in their personality traits (Extraversion, Openness to Experience and Conscientiousness) and their behaviour while shopping online. However, the trait neuroticism did not conform to the generalisation and hence did not display consistency between the trait and its related behaviour in online 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".