Decoding online shopping adoption in Saudi Arabia: The role of trust and user experience
Bibliographic record
Abstract
The current paper discusses the factors, which affect consumer acceptance of online shopping platforms in Saudi Arabia. It examined this by the Unified Theory of Acceptability and Use of Technology (UTAUT2) model and the other factors of trust and user experience. The present paper has assessed how trust and experience have an effect on the behavioral intention of online shopping websites within the context of Saudi Arabia. It is based on the UTAUT2 model. The study was quantitative in nature and a survey technique was used to gather information about Saudi customers. The research was conducted based on a model known as structural equation modeling (SEM) that was employed to test the correlation between the elements proposed and the acceptance of online shopping platform. Findings of the research indicate that expectancy of performance, social influence and trust are highly potent factors that determine the desire to utilize online shopping platforms. All this makes the level of customer acceptability very high with regard to overall satisfaction and intentions to buy online. There was no significant effect of effort expectancy and enabling environments as well as hedonic incentives on behavioral intention, however. These results reveal that trust and pleasant user experience are major factors that define the uptake of Internet commerce in Saudi Arabia among consumers. We discover that the long-held opinion that other factors such as performance expectancy and social influence continue to play a role in acceptance of online purchasing technology is true. However, the aspects of trust remain decisive, depending on the traits of cognition and user experience. The study had practitioner implications among researchers of consumer behavior on digital marketplace. This implication is clear to people who belong to the e-commerce industry or to people who are developing a feature to win the favor of users. In such situations, the measures of trust-building need to be implemented, and the user experience needs to be optimized.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".