Individual Personality Factors as Drivers for Electronic and Mobile-Shopping Acceptance in United Arab Emirates
Bibliographic record
Abstract
Purpose: The purpose of this paper is to examine how individual personality factors including functionality factors interactivity, psychological factors, usability and technology factors, and product/service characteristics can influence Dubai Emiratis to adopt and use online shopping. Methodology: Thirty six questionnaire items were administered to 180 adult Emiratis living in Dubai to examine the influence of personality factors on online shopping acceptance. The sample of 180 Emiratis was chosen through random sampling technique.Results: The findings significantly improved the understanding of users in Dubai in terms of their E&M-Shopping acceptance. The factors could assist in achieving successful E&M-Shopping acceptance. Along a similar line of importance, the findings highlighted the low awareness of users concerning government regulations and product return policy.Practical implications: The result of this study showed that usability and technology factors affected the consumers’ acceptance of Electronic and Mobile shopping. Therefore, future technology in E&M-Shopping should be enhanced through government initiatives and such enhancements will be evidenced in the country’s -GDP. This is also expected to achieve the target of the UAE vision 2021 to be among the top 20 countries that are in readiness to capture opportunities provided by information and communication technology to increase competitiveness. Originality or Value: The findings of this study are expected to add knowledge to the behaviour of Dubai Emirati consumers with regard to electronic commerce. Emirati consumers who dwell in Dubai have been compared to international consumers.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".