Examining the Influence of COVID 19 Pandemic in Changing Customers' Orientation towards E-Shopping
Bibliographic record
Abstract
Current study aimed at examining the change in customer behavior during COVID 19 pandemic towards e-shopping. Variables taken into perspective included (Frequency, Necessity, Method of Payment, Price, and Availability of Product/Service). A simple random sample of (500) citizens in Jordan were exposed to an online questionnaire regarding their consuming behavior before and after the pandemic. Results of study indicated the COVID19 pandemic managed to change customer behavior towards depending more on online shopping and e-payment methods during COVID19 pandemic and the circumstances of lockdown and quarantine, in addition to that, results of pre and post behavior indicated that the influence appeared to be more influenced by gender and academic qualification as females' behavior appeared to be more influential and those who held a diploma. Study recommended that companies need to develop effective marketing strategies and enhance their presence in the e-commerce sector. However, one question remained unanswered; will society’s behavior change after the pandemic's demise, and will this behavior turn into an economic mind that measures things in numbers?
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".