Customer attitudes towards online shopping: A systematic review of the influencing factors
Bibliographic record
Abstract
The primary goal of this study is to investigate the major elements that impact customer attitudes regarding internet purchasing in Jordan. This study employed a qualitative systematic literature review methodology, with 100 existing peer-reviewed articles completed in Jordan chosen for evaluation based on an inclusion/exclusion criterion. The findings of this study were collected utilizing a thematic method, which involved extracting previous researchers' findings from the literature, categorizing similar themes and findings, and drawing conclusions. According to the findings of this survey, the most important elements impacting customer attitudes about online buying in Jordan are trust, cultural hurdles such as uncertainty avoidance and a lack of understanding, security, perceived ease-of-use, and perceived utility. It was also discovered that Hofstede's cultural dimensions’ theory and the technology acceptance model (TAM) can help online businesses identify what factors drive online shopping adoption in Jordan. This study discovered, through a review of available literature, that the online shopping sector in Jordan is currently undeveloped, necessitating more effective growth techniques. Finally, the outcomes of this study provide online merchants with insight into what has to be prioritized in order to entice Jordanian customers to make online purchases. From the findings of this investigation, three areas of inquiry were uncovered for further study. Findings from this study also include suggestions for online retailers and policymakers looking to boost Jordanians' comfort level with making purchases over the Internet. The findings of this study will be invaluable to international online retailers that are considering entering the Jordanian market. They will reveal not only the most important aspects affecting consumers' perceptions of online shopping in Jordan, but also the level of maturity of this sector.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".